diff --git a/configs/_base_/datasets/cityscapes_detection.py b/configs/_base_/datasets/cityscapes_detection.py index 156aca0..e341b59 100644 --- a/configs/_base_/datasets/cityscapes_detection.py +++ b/configs/_base_/datasets/cityscapes_detection.py @@ -1,3 +1,4 @@ +# dataset settings dataset_type = 'CityscapesDataset' data_root = 'data/cityscapes/' img_norm_cfg = dict( diff --git a/configs/_base_/datasets/cityscapes_instance.py b/configs/_base_/datasets/cityscapes_instance.py index 3c5472a..4e3c34e 100644 --- a/configs/_base_/datasets/cityscapes_instance.py +++ b/configs/_base_/datasets/cityscapes_instance.py @@ -1,3 +1,4 @@ +# dataset settings dataset_type = 'CityscapesDataset' data_root = 'data/cityscapes/' img_norm_cfg = dict( diff --git a/configs/_base_/datasets/coco_detection.py b/configs/_base_/datasets/coco_detection.py index 09a75c4..149f590 100644 --- a/configs/_base_/datasets/coco_detection.py +++ b/configs/_base_/datasets/coco_detection.py @@ -1,3 +1,4 @@ +# dataset settings dataset_type = 'CocoDataset' data_root = 'data/coco/' img_norm_cfg = dict( diff --git a/configs/_base_/datasets/coco_instance.py b/configs/_base_/datasets/coco_instance.py index f6ea4f4..9901a85 100644 --- a/configs/_base_/datasets/coco_instance.py +++ b/configs/_base_/datasets/coco_instance.py @@ -1,3 +1,4 @@ +# dataset settings dataset_type = 'CocoDataset' data_root = 'data/coco/' img_norm_cfg = dict( diff --git a/configs/_base_/datasets/coco_instance_semantic.py b/configs/_base_/datasets/coco_instance_semantic.py index f7c072e..6c8bf07 100644 --- a/configs/_base_/datasets/coco_instance_semantic.py +++ b/configs/_base_/datasets/coco_instance_semantic.py @@ -1,3 +1,4 @@ +# dataset settings dataset_type = 'CocoDataset' data_root = 'data/coco/' img_norm_cfg = dict( diff --git a/configs/_base_/datasets/coco_panoptic.py b/configs/_base_/datasets/coco_panoptic.py new file mode 100644 index 0000000..dbade7c --- /dev/null +++ b/configs/_base_/datasets/coco_panoptic.py @@ -0,0 +1,59 @@ +# dataset settings +dataset_type = 'CocoPanopticDataset' +data_root = 'data/coco/' +img_norm_cfg = dict( + mean=[123.675, 116.28, 103.53], std=[58.395, 57.12, 57.375], to_rgb=True) +train_pipeline = [ + dict(type='LoadImageFromFile'), + dict( + type='LoadPanopticAnnotations', + with_bbox=True, + with_mask=True, + with_seg=True), + dict(type='Resize', img_scale=(1333, 800), keep_ratio=True), + dict(type='RandomFlip', flip_ratio=0.5), + dict(type='Normalize', **img_norm_cfg), + dict(type='Pad', size_divisor=32), + dict(type='SegRescale', scale_factor=1 / 4), + dict(type='DefaultFormatBundle'), + dict( + type='Collect', + keys=['img', 'gt_bboxes', 'gt_labels', 'gt_masks', 'gt_semantic_seg']), +] +test_pipeline = [ + dict(type='LoadImageFromFile'), + dict( + type='MultiScaleFlipAug', + img_scale=(1333, 800), + flip=False, + transforms=[ + dict(type='Resize', keep_ratio=True), + dict(type='RandomFlip'), + dict(type='Normalize', **img_norm_cfg), + dict(type='Pad', size_divisor=32), + dict(type='ImageToTensor', keys=['img']), + dict(type='Collect', keys=['img']), + ]) +] +data = dict( + samples_per_gpu=2, + workers_per_gpu=2, + train=dict( + type=dataset_type, + ann_file=data_root + 'annotations/panoptic_train2017.json', + img_prefix=data_root + 'train2017/', + seg_prefix=data_root + 'annotations/panoptic_train2017/', + pipeline=train_pipeline), + val=dict( + type=dataset_type, + ann_file=data_root + 'annotations/panoptic_val2017.json', + img_prefix=data_root + 'val2017/', + seg_prefix=data_root + 'annotations/panoptic_val2017/', + pipeline=test_pipeline), + test=dict( + type=dataset_type, + ann_file=data_root + 'annotations/panoptic_val2017.json', + img_prefix=data_root + 'val2017/', + seg_prefix=data_root + 'annotations/panoptic_val2017/', + pipeline=test_pipeline)) +evaluation = dict(interval=1, metric=['PQ']) diff --git a/configs/_base_/datasets/lvis_v0.5_instance.py b/configs/_base_/datasets/lvis_v0.5_instance.py index f3da861..207e005 100644 --- a/configs/_base_/datasets/lvis_v0.5_instance.py +++ b/configs/_base_/datasets/lvis_v0.5_instance.py @@ -1,3 +1,4 @@ +# dataset settings _base_ = 'coco_instance.py' dataset_type = 'LVISV05Dataset' data_root = 'data/lvis_v0.5/' diff --git a/configs/_base_/datasets/lvis_v1_instance.py b/configs/_base_/datasets/lvis_v1_instance.py index e8c5d1b..be791ed 100644 --- a/configs/_base_/datasets/lvis_v1_instance.py +++ b/configs/_base_/datasets/lvis_v1_instance.py @@ -1,3 +1,4 @@ +# dataset settings _base_ = 'coco_instance.py' dataset_type = 'LVISV1Dataset' data_root = 'data/lvis_v1/' diff --git a/configs/_base_/models/cascade_mask_rcnn_r50_fpn.py b/configs/_base_/models/cascade_mask_rcnn_r50_fpn.py index 9ef6673..2902cca 100644 --- a/configs/_base_/models/cascade_mask_rcnn_r50_fpn.py +++ b/configs/_base_/models/cascade_mask_rcnn_r50_fpn.py @@ -1,7 +1,6 @@ # model settings model = dict( type='CascadeRCNN', - pretrained='torchvision://resnet50', backbone=dict( type='ResNet', depth=50, @@ -10,7 +9,8 @@ frozen_stages=1, norm_cfg=dict(type='BN', requires_grad=True), norm_eval=True, - style='pytorch'), + style='pytorch', + init_cfg=dict(type='Pretrained', checkpoint='torchvision://resnet50')), neck=dict( type='FPN', in_channels=[256, 512, 1024, 2048], diff --git a/configs/_base_/models/cascade_rcnn_r50_fpn.py b/configs/_base_/models/cascade_rcnn_r50_fpn.py index cde2a96..42f74ae 100644 --- a/configs/_base_/models/cascade_rcnn_r50_fpn.py +++ b/configs/_base_/models/cascade_rcnn_r50_fpn.py @@ -1,7 +1,6 @@ # model settings model = dict( type='CascadeRCNN', - pretrained='torchvision://resnet50', backbone=dict( type='ResNet', depth=50, @@ -10,7 +9,8 @@ frozen_stages=1, norm_cfg=dict(type='BN', requires_grad=True), norm_eval=True, - style='pytorch'), + style='pytorch', + init_cfg=dict(type='Pretrained', checkpoint='torchvision://resnet50')), neck=dict( type='FPN', in_channels=[256, 512, 1024, 2048], diff --git a/configs/_base_/models/fast_rcnn_r50_fpn.py b/configs/_base_/models/fast_rcnn_r50_fpn.py index 1099165..9982fe0 100644 --- a/configs/_base_/models/fast_rcnn_r50_fpn.py +++ b/configs/_base_/models/fast_rcnn_r50_fpn.py @@ -1,7 +1,6 @@ # model settings model = dict( type='FastRCNN', - pretrained='torchvision://resnet50', backbone=dict( type='ResNet', depth=50, @@ -10,7 +9,8 @@ frozen_stages=1, norm_cfg=dict(type='BN', requires_grad=True), norm_eval=True, - style='pytorch'), + style='pytorch', + init_cfg=dict(type='Pretrained', checkpoint='torchvision://resnet50')), neck=dict( type='FPN', in_channels=[256, 512, 1024, 2048], diff --git a/configs/_base_/models/faster_rcnn_r50_caffe_c4.py b/configs/_base_/models/faster_rcnn_r50_caffe_c4.py index 6e18f71..51b5db4 100644 --- a/configs/_base_/models/faster_rcnn_r50_caffe_c4.py +++ b/configs/_base_/models/faster_rcnn_r50_caffe_c4.py @@ -2,7 +2,6 @@ norm_cfg = dict(type='BN', requires_grad=False) model = dict( type='FasterRCNN', - pretrained='open-mmlab://detectron2/resnet50_caffe', backbone=dict( type='ResNet', depth=50, @@ -13,7 +12,10 @@ frozen_stages=1, norm_cfg=norm_cfg, norm_eval=True, - style='caffe'), + style='caffe', + init_cfg=dict( + type='Pretrained', + checkpoint='open-mmlab://detectron2/resnet50_caffe')), rpn_head=dict( type='RPNHead', in_channels=1024, diff --git a/configs/_base_/models/faster_rcnn_r50_caffe_dc5.py b/configs/_base_/models/faster_rcnn_r50_caffe_dc5.py index 5089f0e..a377a6f 100644 --- a/configs/_base_/models/faster_rcnn_r50_caffe_dc5.py +++ b/configs/_base_/models/faster_rcnn_r50_caffe_dc5.py @@ -2,7 +2,6 @@ norm_cfg = dict(type='BN', requires_grad=False) model = dict( type='FasterRCNN', - pretrained='open-mmlab://detectron2/resnet50_caffe', backbone=dict( type='ResNet', depth=50, @@ -13,7 +12,10 @@ frozen_stages=1, norm_cfg=norm_cfg, norm_eval=True, - style='caffe'), + style='caffe', + init_cfg=dict( + type='Pretrained', + checkpoint='open-mmlab://detectron2/resnet50_caffe')), rpn_head=dict( type='RPNHead', in_channels=2048, diff --git a/configs/_base_/models/faster_rcnn_r50_fpn.py b/configs/_base_/models/faster_rcnn_r50_fpn.py index 0f038d1..1ef8e7b 100644 --- a/configs/_base_/models/faster_rcnn_r50_fpn.py +++ b/configs/_base_/models/faster_rcnn_r50_fpn.py @@ -1,6 +1,6 @@ +# model settings model = dict( type='FasterRCNN', - pretrained='torchvision://resnet50', backbone=dict( type='ResNet', depth=50, @@ -9,7 +9,8 @@ frozen_stages=1, norm_cfg=dict(type='BN', requires_grad=True), norm_eval=True, - style='pytorch'), + style='pytorch', + init_cfg=dict(type='Pretrained', checkpoint='torchvision://resnet50')), neck=dict( type='FPN', in_channels=[256, 512, 1024, 2048], diff --git a/configs/_base_/models/mask_rcnn_r50_caffe_c4.py b/configs/_base_/models/mask_rcnn_r50_caffe_c4.py index eaae134..122202e 100644 --- a/configs/_base_/models/mask_rcnn_r50_caffe_c4.py +++ b/configs/_base_/models/mask_rcnn_r50_caffe_c4.py @@ -2,7 +2,6 @@ norm_cfg = dict(type='BN', requires_grad=False) model = dict( type='MaskRCNN', - pretrained='open-mmlab://detectron2/resnet50_caffe', backbone=dict( type='ResNet', depth=50, @@ -13,7 +12,10 @@ frozen_stages=1, norm_cfg=norm_cfg, norm_eval=True, - style='caffe'), + style='caffe', + init_cfg=dict( + type='Pretrained', + checkpoint='open-mmlab://detectron2/resnet50_caffe')), rpn_head=dict( type='RPNHead', in_channels=1024, diff --git a/configs/_base_/models/mask_rcnn_r50_fpn.py b/configs/_base_/models/mask_rcnn_r50_fpn.py index 6fc7908..d903e55 100644 --- a/configs/_base_/models/mask_rcnn_r50_fpn.py +++ b/configs/_base_/models/mask_rcnn_r50_fpn.py @@ -1,7 +1,6 @@ # model settings model = dict( type='MaskRCNN', - pretrained='torchvision://resnet50', backbone=dict( type='ResNet', depth=50, @@ -10,7 +9,8 @@ frozen_stages=1, norm_cfg=dict(type='BN', requires_grad=True), norm_eval=True, - style='pytorch'), + style='pytorch', + init_cfg=dict(type='Pretrained', checkpoint='torchvision://resnet50')), neck=dict( type='FPN', in_channels=[256, 512, 1024, 2048], diff --git a/configs/_base_/models/retinanet_r50_fpn.py b/configs/_base_/models/retinanet_r50_fpn.py index 47fe98c..56e43fa 100644 --- a/configs/_base_/models/retinanet_r50_fpn.py +++ b/configs/_base_/models/retinanet_r50_fpn.py @@ -1,7 +1,6 @@ # model settings model = dict( type='RetinaNet', - pretrained='torchvision://resnet50', backbone=dict( type='ResNet', depth=50, @@ -10,7 +9,8 @@ frozen_stages=1, norm_cfg=dict(type='BN', requires_grad=True), norm_eval=True, - style='pytorch'), + style='pytorch', + init_cfg=dict(type='Pretrained', checkpoint='torchvision://resnet50')), neck=dict( type='FPN', in_channels=[256, 512, 1024, 2048], @@ -41,7 +41,7 @@ alpha=0.25, loss_weight=1.0), loss_bbox=dict(type='L1Loss', loss_weight=1.0)), - # training and testing settings + # model training and testing settings train_cfg=dict( assigner=dict( type='MaxIoUAssigner', diff --git a/configs/_base_/models/rpn_r50_caffe_c4.py b/configs/_base_/models/rpn_r50_caffe_c4.py index 9c32a55..8b32ca9 100644 --- a/configs/_base_/models/rpn_r50_caffe_c4.py +++ b/configs/_base_/models/rpn_r50_caffe_c4.py @@ -1,7 +1,6 @@ # model settings model = dict( type='RPN', - pretrained='open-mmlab://detectron2/resnet50_caffe', backbone=dict( type='ResNet', depth=50, @@ -12,7 +11,10 @@ frozen_stages=1, norm_cfg=dict(type='BN', requires_grad=False), norm_eval=True, - style='caffe'), + style='caffe', + init_cfg=dict( + type='Pretrained', + checkpoint='open-mmlab://detectron2/resnet50_caffe')), neck=None, rpn_head=dict( type='RPNHead', diff --git a/configs/_base_/models/rpn_r50_fpn.py b/configs/_base_/models/rpn_r50_fpn.py index 22193c1..edaf4d4 100644 --- a/configs/_base_/models/rpn_r50_fpn.py +++ b/configs/_base_/models/rpn_r50_fpn.py @@ -1,8 +1,6 @@ # model settings - model = dict( type='RPN', - pretrained='torchvision://resnet50', backbone=dict( type='ResNet', depth=50, @@ -11,7 +9,8 @@ frozen_stages=1, norm_cfg=dict(type='BN', requires_grad=True), norm_eval=True, - style='pytorch'), + style='pytorch', + init_cfg=dict(type='Pretrained', checkpoint='torchvision://resnet50')), neck=dict( type='FPN', in_channels=[256, 512, 1024, 2048], diff --git a/configs/_base_/models/ssd300.py b/configs/_base_/models/ssd300.py index 4ea7975..f17df01 100644 --- a/configs/_base_/models/ssd300.py +++ b/configs/_base_/models/ssd300.py @@ -2,17 +2,22 @@ input_size = 300 model = dict( type='SingleStageDetector', - pretrained='open-mmlab://vgg16_caffe', backbone=dict( type='SSDVGG', - input_size=input_size, depth=16, with_last_pool=False, ceil_mode=True, out_indices=(3, 4), out_feature_indices=(22, 34), + init_cfg=dict( + type='Pretrained', checkpoint='open-mmlab://vgg16_caffe')), + neck=dict( + type='SSDNeck', + in_channels=(512, 1024), + out_channels=(512, 1024, 512, 256, 256, 256), + level_strides=(2, 2, 1, 1), + level_paddings=(1, 1, 0, 0), l2_norm_scale=20), - neck=None, bbox_head=dict( type='SSDHead', in_channels=(512, 1024, 512, 256, 256, 256), @@ -28,6 +33,7 @@ type='DeltaXYWHBBoxCoder', target_means=[.0, .0, .0, .0], target_stds=[0.1, 0.1, 0.2, 0.2])), + # model training and testing settings train_cfg=dict( assigner=dict( type='MaxIoUAssigner', @@ -42,6 +48,7 @@ neg_pos_ratio=3, debug=False), test_cfg=dict( + nms_pre=1000, nms=dict(type='nms', iou_threshold=0.45), min_bbox_size=0, score_thr=0.02, diff --git a/configs/albu_example/README.md b/configs/albu_example/README.md index bf35a9b..b11ae56 100644 --- a/configs/albu_example/README.md +++ b/configs/albu_example/README.md @@ -1,6 +1,6 @@ # Albu Example -[OTHERS] + ``` @article{2018arXiv180906839B, @@ -16,4 +16,4 @@ | Backbone | Style | Lr schd | Mem (GB) | Inf time (fps) | box AP | mask AP | Config | Download | |:---------:|:-------:|:-------:|:--------:|:--------------:|:------:|:-------:|:------:|:--------:| -| R-50 | pytorch | 1x | 4.4 | 16.6 | 38.0 | 34.5 |[config](https://github.com/open-mmlab/mmdetection/tree/master/configs/albu_example/mask_rcnn_r50_fpn_albu_1x_coco.py) | [model](http://download.openmmlab.com/mmdetection/v2.0/albu_example/mask_rcnn_r50_fpn_albu_1x_coco/mask_rcnn_r50_fpn_albu_1x_coco_20200208-ab203bcd.pth) | [log](http://download.openmmlab.com/mmdetection/v2.0/albu_example/mask_rcnn_r50_fpn_albu_1x_coco/mask_rcnn_r50_fpn_albu_1x_coco_20200208_225520.log.json) | +| R-50 | pytorch | 1x | 4.4 | 16.6 | 38.0 | 34.5 |[config](https://github.com/open-mmlab/mmdetection/tree/master/configs/albu_example/mask_rcnn_r50_fpn_albu_1x_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/albu_example/mask_rcnn_r50_fpn_albu_1x_coco/mask_rcnn_r50_fpn_albu_1x_coco_20200208-ab203bcd.pth) | [log](https://download.openmmlab.com/mmdetection/v2.0/albu_example/mask_rcnn_r50_fpn_albu_1x_coco/mask_rcnn_r50_fpn_albu_1x_coco_20200208_225520.log.json) | diff --git a/configs/atss/README.md b/configs/atss/README.md index 4ba9150..8aa7746 100644 --- a/configs/atss/README.md +++ b/configs/atss/README.md @@ -2,7 +2,7 @@ ## Introduction -[ALGORITHM] + ```latex @article{zhang2019bridging, @@ -17,5 +17,5 @@ | Backbone | Style | Lr schd | Mem (GB) | Inf time (fps) | box AP | Config | Download | |:---------:|:-------:|:-------:|:--------:|:--------------:|:------:|:------:|:--------:| -| R-50 | pytorch | 1x | 3.7 | 19.7 | 39.4 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/atss/atss_r50_fpn_1x_coco.py) | [model](http://download.openmmlab.com/mmdetection/v2.0/atss/atss_r50_fpn_1x_coco/atss_r50_fpn_1x_coco_20200209-985f7bd0.pth) | [log](http://download.openmmlab.com/mmdetection/v2.0/atss/atss_r50_fpn_1x_coco/atss_r50_fpn_1x_coco_20200209_102539.log.json) | -| R-101 | pytorch | 1x | 5.6 | 12.3 | 41.5 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/atss/atss_r101_fpn_1x_coco.py) | [model](http://download.openmmlab.com/mmdetection/v2.0/atss/atss_r101_fpn_1x_coco/atss_r101_fpn_1x_20200825-dfcadd6f.pth) | [log](http://download.openmmlab.com/mmdetection/v2.0/atss/atss_r101_fpn_1x_coco/atss_r101_fpn_1x_20200825-dfcadd6f.log.json) | +| R-50 | pytorch | 1x | 3.7 | 19.7 | 39.4 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/atss/atss_r50_fpn_1x_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/atss/atss_r50_fpn_1x_coco/atss_r50_fpn_1x_coco_20200209-985f7bd0.pth) | [log](https://download.openmmlab.com/mmdetection/v2.0/atss/atss_r50_fpn_1x_coco/atss_r50_fpn_1x_coco_20200209_102539.log.json) | +| R-101 | pytorch | 1x | 5.6 | 12.3 | 41.5 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/atss/atss_r101_fpn_1x_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/atss/atss_r101_fpn_1x_coco/atss_r101_fpn_1x_20200825-dfcadd6f.pth) | [log](https://download.openmmlab.com/mmdetection/v2.0/atss/atss_r101_fpn_1x_coco/atss_r101_fpn_1x_20200825-dfcadd6f.log.json) | diff --git a/configs/atss/atss_r101_fpn_1x_coco.py b/configs/atss/atss_r101_fpn_1x_coco.py index 695779a..5225d2a 100644 --- a/configs/atss/atss_r101_fpn_1x_coco.py +++ b/configs/atss/atss_r101_fpn_1x_coco.py @@ -1,5 +1,6 @@ _base_ = './atss_r50_fpn_1x_coco.py' model = dict( - pretrained='torchvision://resnet101', - backbone=dict(depth=101), -) + backbone=dict( + depth=101, + init_cfg=dict(type='Pretrained', + checkpoint='torchvision://resnet101'))) diff --git a/configs/atss/atss_r50_fpn_1x_coco.py b/configs/atss/atss_r50_fpn_1x_coco.py index cfd70ed..42ff4c5 100644 --- a/configs/atss/atss_r50_fpn_1x_coco.py +++ b/configs/atss/atss_r50_fpn_1x_coco.py @@ -4,7 +4,6 @@ ] model = dict( type='ATSS', - pretrained='torchvision://resnet50', backbone=dict( type='ResNet', depth=50, @@ -13,7 +12,8 @@ frozen_stages=1, norm_cfg=dict(type='BN', requires_grad=True), norm_eval=True, - style='pytorch'), + style='pytorch', + init_cfg=dict(type='Pretrained', checkpoint='torchvision://resnet50')), neck=dict( type='FPN', in_channels=[256, 512, 1024, 2048], diff --git a/configs/atss/metafile.yml b/configs/atss/metafile.yml new file mode 100644 index 0000000..f4c567e --- /dev/null +++ b/configs/atss/metafile.yml @@ -0,0 +1,60 @@ +Collections: + - Name: ATSS + Metadata: + Training Data: COCO + Training Techniques: + - SGD with Momentum + - Weight Decay + Training Resources: 8x V100 GPUs + Architecture: + - ATSS + - FPN + - ResNet + Paper: + URL: https://arxiv.org/abs/1912.02424 + Title: 'Bridging the Gap Between Anchor-based and Anchor-free Detection via Adaptive Training Sample Selection' + README: configs/atss/README.md + Code: + URL: https://github.com/open-mmlab/mmdetection/blob/v2.0.0/mmdet/models/detectors/atss.py#L6 + Version: v2.0.0 + +Models: + - Name: atss_r50_fpn_1x_coco + In Collection: ATSS + Config: configs/atss/atss_r50_fpn_1x_coco.py + Metadata: + Training Memory (GB): 3.7 + inference time (ms/im): + - value: 50.76 + hardware: V100 + backend: PyTorch + batch size: 1 + mode: FP32 + resolution: (800, 1333) + Epochs: 12 + Results: + - Task: Object Detection + Dataset: COCO + Metrics: + box AP: 39.4 + Weights: https://download.openmmlab.com/mmdetection/v2.0/atss/atss_r50_fpn_1x_coco/atss_r50_fpn_1x_coco_20200209-985f7bd0.pth + + - Name: atss_r101_fpn_1x_coco + In Collection: ATSS + Config: configs/atss/atss_r101_fpn_1x_coco.py + Metadata: + Training Memory (GB): 5.6 + inference time (ms/im): + - value: 81.3 + hardware: V100 + backend: PyTorch + batch size: 1 + mode: FP32 + resolution: (800, 1333) + Epochs: 12 + Results: + - Task: Object Detection + Dataset: COCO + Metrics: + box AP: 41.5 + Weights: https://download.openmmlab.com/mmdetection/v2.0/atss/atss_r101_fpn_1x_coco/atss_r101_fpn_1x_20200825-dfcadd6f.pth diff --git a/configs/autoassign/README.md b/configs/autoassign/README.md new file mode 100644 index 0000000..4d5f376 --- /dev/null +++ b/configs/autoassign/README.md @@ -0,0 +1,25 @@ +# AutoAssign: Differentiable Label Assignment for Dense Object Detection + +## Introduction + + + +``` +@article{zhu2020autoassign, + title={AutoAssign: Differentiable Label Assignment for Dense Object Detection}, + author={Zhu, Benjin and Wang, Jianfeng and Jiang, Zhengkai and Zong, Fuhang and Liu, Songtao and Li, Zeming and Sun, Jian}, + journal={arXiv preprint arXiv:2007.03496}, + year={2020} +} +``` + +## Results and Models + +| Backbone | Style | Lr schd | Mem (GB) | box AP | Config | Download | +|:---------:|:-------:|:-------:|:--------:|:------:|:------:|:--------:| +| R-50 | caffe | 1x | 4.08 | 40.4 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/autoassign/autoassign_r50_fpn_8x2_1x_coco.py) |[model](https://download.openmmlab.com/mmdetection/v2.0/autoassign/auto_assign_r50_fpn_1x_coco/auto_assign_r50_fpn_1x_coco_20210413_115540-5e17991f.pth) | [log](https://download.openmmlab.com/mmdetection/v2.0/autoassign/auto_assign_r50_fpn_1x_coco/auto_assign_r50_fpn_1x_coco_20210413_115540-5e17991f.log.json) | + +**Note**: + +1. We find that the performance is unstable with 1x setting and may fluctuate by about 0.3 mAP. mAP 40.3 ~ 40.6 is acceptable. Such fluctuation can also be found in the original implementation. +2. You can get a more stable results ~ mAP 40.6 with a schedule total 13 epoch, and learning rate is divided by 10 at 10th and 13th epoch. diff --git a/configs/autoassign/autoassign_r50_fpn_8x2_1x_coco.py b/configs/autoassign/autoassign_r50_fpn_8x2_1x_coco.py new file mode 100644 index 0000000..db548dc --- /dev/null +++ b/configs/autoassign/autoassign_r50_fpn_8x2_1x_coco.py @@ -0,0 +1,85 @@ +# We follow the original implementation which +# adopts the Caffe pre-trained backbone. +_base_ = [ + '../_base_/datasets/coco_detection.py', + '../_base_/schedules/schedule_1x.py', '../_base_/default_runtime.py' +] +model = dict( + type='AutoAssign', + backbone=dict( + type='ResNet', + depth=50, + num_stages=4, + out_indices=(0, 1, 2, 3), + frozen_stages=1, + norm_cfg=dict(type='BN', requires_grad=False), + norm_eval=True, + style='caffe', + init_cfg=dict( + type='Pretrained', + checkpoint='open-mmlab://detectron2/resnet50_caffe')), + neck=dict( + type='FPN', + in_channels=[256, 512, 1024, 2048], + out_channels=256, + start_level=1, + add_extra_convs=True, + num_outs=5, + relu_before_extra_convs=True, + init_cfg=dict(type='Caffe2Xavier', layer='Conv2d')), + bbox_head=dict( + type='AutoAssignHead', + num_classes=80, + in_channels=256, + stacked_convs=4, + feat_channels=256, + strides=[8, 16, 32, 64, 128], + loss_bbox=dict(type='GIoULoss', loss_weight=5.0)), + train_cfg=None, + test_cfg=dict( + nms_pre=1000, + min_bbox_size=0, + score_thr=0.05, + nms=dict(type='nms', iou_threshold=0.6), + max_per_img=100)) +img_norm_cfg = dict( + mean=[102.9801, 115.9465, 122.7717], std=[1.0, 1.0, 1.0], to_rgb=False) +train_pipeline = [ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations', with_bbox=True), + dict(type='Resize', img_scale=(1333, 800), keep_ratio=True), + dict(type='RandomFlip', flip_ratio=0.5), + dict(type='Normalize', **img_norm_cfg), + dict(type='Pad', size_divisor=32), + dict(type='DefaultFormatBundle'), + dict(type='Collect', keys=['img', 'gt_bboxes', 'gt_labels']) +] +test_pipeline = [ + dict(type='LoadImageFromFile'), + dict( + type='MultiScaleFlipAug', + img_scale=(1333, 800), + flip=False, + transforms=[ + dict(type='Resize', keep_ratio=True), + dict(type='RandomFlip'), + dict(type='Normalize', **img_norm_cfg), + dict(type='Pad', size_divisor=32), + dict(type='ImageToTensor', keys=['img']), + dict(type='Collect', keys=['img']) + ]) +] +data = dict( + train=dict(pipeline=train_pipeline), + val=dict(pipeline=test_pipeline), + test=dict(pipeline=test_pipeline)) +# optimizer +optimizer = dict(lr=0.01, paramwise_cfg=dict(norm_decay_mult=0.)) +# learning policy +lr_config = dict( + policy='step', + warmup='linear', + warmup_iters=1000, + warmup_ratio=1.0 / 1000, + step=[8, 11]) +total_epochs = 12 diff --git a/configs/autoassign/metafile.yml b/configs/autoassign/metafile.yml new file mode 100644 index 0000000..f1e9051 --- /dev/null +++ b/configs/autoassign/metafile.yml @@ -0,0 +1,33 @@ +Collections: + - Name: AutoAssign + Metadata: + Training Data: COCO + Training Techniques: + - SGD with Momentum + - Weight Decay + Training Resources: 8x V100 GPUs + Architecture: + - AutoAssign + - FPN + - ResNet + Paper: + URL: https://arxiv.org/abs/2007.03496 + Title: 'AutoAssign: Differentiable Label Assignment for Dense Object Detection' + README: configs/autoassign/README.md + Code: + URL: https://github.com/open-mmlab/mmdetection/blob/v2.12.0/mmdet/models/detectors/autoassign.py#L6 + Version: v2.12.0 + +Models: + - Name: autoassign_r50_fpn_8x2_1x_coco + In Collection: AutoAssign + Config: configs/autoassign/autoassign_r50_fpn_8x2_1x_coco.py + Metadata: + Training Memory (GB): 4.08 + Epochs: 12 + Results: + - Task: Object Detection + Dataset: COCO + Metrics: + box AP: 40.4 + Weights: https://download.openmmlab.com/mmdetection/v2.0/autoassign/auto_assign_r50_fpn_1x_coco/auto_assign_r50_fpn_1x_coco_20210413_115540-5e17991f.pth diff --git a/configs/carafe/README.md b/configs/carafe/README.md index d9ca664..768cb98 100644 --- a/configs/carafe/README.md +++ b/configs/carafe/README.md @@ -2,7 +2,7 @@ ## Introduction -[ALGORITHM] + We provide config files to reproduce the object detection & instance segmentation results in the ICCV 2019 Oral paper for [CARAFE: Content-Aware ReAssembly of FEatures](https://arxiv.org/abs/1905.02188). @@ -22,9 +22,9 @@ The results on COCO 2017 val is shown in the below table. | Method | Backbone | Style | Lr schd | Test Proposal Num | Inf time (fps) | Box AP | Mask AP | Config | Download | |:--------------------:|:--------:|:-------:|:-------:|:-----------------:|:--------------:|:------:|:-------:|:------:|:--------:| -| Faster R-CNN w/ CARAFE | R-50-FPN | pytorch | 1x | 1000 | 16.5 | 38.6 | 38.6 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/carafe/faster_rcnn_r50_fpn_carafe_1x_coco.py) | [model](http://download.openmmlab.com/mmdetection/v2.0/carafe/faster_rcnn_r50_fpn_carafe_1x_coco/faster_rcnn_r50_fpn_carafe_1x_coco_bbox_mAP-0.386_20200504_175733-385a75b7.pth) | [log](http://download.openmmlab.com/mmdetection/v2.0/carafe/faster_rcnn_r50_fpn_carafe_1x_coco/faster_rcnn_r50_fpn_carafe_1x_coco_20200504_175733.log.json) | +| Faster R-CNN w/ CARAFE | R-50-FPN | pytorch | 1x | 1000 | 16.5 | 38.6 | 38.6 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/carafe/faster_rcnn_r50_fpn_carafe_1x_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/carafe/faster_rcnn_r50_fpn_carafe_1x_coco/faster_rcnn_r50_fpn_carafe_1x_coco_bbox_mAP-0.386_20200504_175733-385a75b7.pth) | [log](https://download.openmmlab.com/mmdetection/v2.0/carafe/faster_rcnn_r50_fpn_carafe_1x_coco/faster_rcnn_r50_fpn_carafe_1x_coco_20200504_175733.log.json) | | - | - | - | - | 2000 | | | | | -| Mask R-CNN w/ CARAFE | R-50-FPN | pytorch | 1x | 1000 | 14.0 | 39.3 | 35.8 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/carafe/mask_rcnn_r50_fpn_carafe_1x_coco.py) | [model](http://download.openmmlab.com/mmdetection/v2.0/carafe/mask_rcnn_r50_fpn_carafe_1x_coco/mask_rcnn_r50_fpn_carafe_1x_coco_bbox_mAP-0.393__segm_mAP-0.358_20200503_135957-8687f195.pth) | [log](http://download.openmmlab.com/mmdetection/v2.0/carafe/mask_rcnn_r50_fpn_carafe_1x_coco/mask_rcnn_r50_fpn_carafe_1x_coco_20200503_135957.log.json) | +| Mask R-CNN w/ CARAFE | R-50-FPN | pytorch | 1x | 1000 | 14.0 | 39.3 | 35.8 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/carafe/mask_rcnn_r50_fpn_carafe_1x_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/carafe/mask_rcnn_r50_fpn_carafe_1x_coco/mask_rcnn_r50_fpn_carafe_1x_coco_bbox_mAP-0.393__segm_mAP-0.358_20200503_135957-8687f195.pth) | [log](https://download.openmmlab.com/mmdetection/v2.0/carafe/mask_rcnn_r50_fpn_carafe_1x_coco/mask_rcnn_r50_fpn_carafe_1x_coco_20200503_135957.log.json) | | - | - | - | - | 2000 | | | | | ## Implementation diff --git a/configs/cascade_rcnn/README.md b/configs/cascade_rcnn/README.md index 15e6191..7d18ab1 100644 --- a/configs/cascade_rcnn/README.md +++ b/configs/cascade_rcnn/README.md @@ -2,7 +2,7 @@ ## Introduction -[ALGORITHM] + ```latex @article{Cai_2019, @@ -24,32 +24,46 @@ | Backbone | Style | Lr schd | Mem (GB) | Inf time (fps) | box AP | Config | Download | | :-------------: | :-----: | :-----: | :------: | :------------: | :----: |:------:|:--------:| -| R-50-FPN | caffe | 1x | 4.2 | | 40.4 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/cascade_rcnn/cascade_rcnn_r50_caffe_fpn_1x_coco.py) | [model](http://download.openmmlab.com/mmdetection/v2.0/cascade_rcnn/cascade_rcnn_r50_caffe_fpn_1x_coco/cascade_rcnn_r50_caffe_fpn_1x_coco_bbox_mAP-0.404_20200504_174853-b857be87.pth) | [log](http://download.openmmlab.com/mmdetection/v2.0/cascade_rcnn/cascade_rcnn_r50_caffe_fpn_1x_coco/cascade_rcnn_r50_caffe_fpn_1x_coco_20200504_174853.log.json) | -| R-50-FPN | pytorch | 1x | 4.4 | 16.1 | 40.3 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/cascade_rcnn/cascade_rcnn_r50_fpn_1x_coco.py) | [model](http://download.openmmlab.com/mmdetection/v2.0/cascade_rcnn/cascade_rcnn_r50_fpn_1x_coco/cascade_rcnn_r50_fpn_1x_coco_20200316-3dc56deb.pth) | [log](http://download.openmmlab.com/mmdetection/v2.0/cascade_rcnn/cascade_rcnn_r50_fpn_1x_coco/cascade_rcnn_r50_fpn_1x_coco_20200316_214748.log.json) | -| R-50-FPN | pytorch | 20e | - | - | 41.0 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/cascade_rcnn/cascade_rcnn_r50_fpn_20e_coco.py) | [model](http://download.openmmlab.com/mmdetection/v2.0/cascade_rcnn/cascade_rcnn_r50_fpn_20e_coco/cascade_rcnn_r50_fpn_20e_coco_bbox_mAP-0.41_20200504_175131-e9872a90.pth) | [log](http://download.openmmlab.com/mmdetection/v2.0/cascade_rcnn/cascade_rcnn_r50_fpn_20e_coco/cascade_rcnn_r50_fpn_20e_coco_20200504_175131.log.json) | -| R-101-FPN | caffe | 1x | 6.2 | | 42.3 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/cascade_rcnn/cascade_rcnn_r101_caffe_fpn_1x_coco.py) | [model](http://download.openmmlab.com/mmdetection/v2.0/cascade_rcnn/cascade_rcnn_r101_caffe_fpn_1x_coco/cascade_rcnn_r101_caffe_fpn_1x_coco_bbox_mAP-0.423_20200504_175649-cab8dbd5.pth) | [log](http://download.openmmlab.com/mmdetection/v2.0/cascade_rcnn/cascade_rcnn_r101_caffe_fpn_1x_coco/cascade_rcnn_r101_caffe_fpn_1x_coco_20200504_175649.log.json) | -| R-101-FPN | pytorch | 1x | 6.4 | 13.5 | 42.0 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/cascade_rcnn/cascade_rcnn_r101_fpn_1x_coco.py) | [model](http://download.openmmlab.com/mmdetection/v2.0/cascade_rcnn/cascade_rcnn_r101_fpn_1x_coco/cascade_rcnn_r101_fpn_1x_coco_20200317-0b6a2fbf.pth) | [log](http://download.openmmlab.com/mmdetection/v2.0/cascade_rcnn/cascade_rcnn_r101_fpn_1x_coco/cascade_rcnn_r101_fpn_1x_coco_20200317_101744.log.json) | -| R-101-FPN | pytorch | 20e | - | - | 42.5 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/cascade_rcnn/cascade_rcnn_r101_fpn_20e_coco.py) | [model](http://download.openmmlab.com/mmdetection/v2.0/cascade_rcnn/cascade_rcnn_r101_fpn_20e_coco/cascade_rcnn_r101_fpn_20e_coco_bbox_mAP-0.425_20200504_231812-5057dcc5.pth) | [log](http://download.openmmlab.com/mmdetection/v2.0/cascade_rcnn/cascade_rcnn_r101_fpn_20e_coco/cascade_rcnn_r101_fpn_20e_coco_20200504_231812.log.json) | -| X-101-32x4d-FPN | pytorch | 1x | 7.6 | 10.9 | 43.7 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/cascade_rcnn/cascade_rcnn_x101_32x4d_fpn_1x_coco.py) | [model](http://download.openmmlab.com/mmdetection/v2.0/cascade_rcnn/cascade_rcnn_x101_32x4d_fpn_1x_coco/cascade_rcnn_x101_32x4d_fpn_1x_coco_20200316-95c2deb6.pth) | [log](http://download.openmmlab.com/mmdetection/v2.0/cascade_rcnn/cascade_rcnn_x101_32x4d_fpn_1x_coco/cascade_rcnn_x101_32x4d_fpn_1x_coco_20200316_055608.log.json) | -| X-101-32x4d-FPN | pytorch | 20e | 7.6 | | 43.7 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/cascade_rcnn/cascade_rcnn_x101_32x4d_fpn_20e_coco.py) | [model](http://download.openmmlab.com/mmdetection/v2.0/cascade_rcnn/cascade_rcnn_x101_32x4d_fpn_20e_coco/cascade_rcnn_x101_32x4d_fpn_20e_coco_20200906_134608-9ae0a720.pth) | [log](http://download.openmmlab.com/mmdetection/v2.0/cascade_rcnn/cascade_rcnn_x101_32x4d_fpn_20e_coco/cascade_rcnn_x101_32x4d_fpn_20e_coco_20200906_134608.log.json) | -| X-101-64x4d-FPN | pytorch | 1x | 10.7 | | 44.7 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/cascade_rcnn/cascade_rcnn_x101_64x4d_fpn_1x_coco.py) | [model](http://download.openmmlab.com/mmdetection/v2.0/cascade_rcnn/cascade_rcnn_x101_64x4d_fpn_1x_coco/cascade_rcnn_x101_64x4d_fpn_1x_coco_20200515_075702-43ce6a30.pth) | [log](http://download.openmmlab.com/mmdetection/v2.0/cascade_rcnn/cascade_rcnn_x101_64x4d_fpn_1x_coco/cascade_rcnn_x101_64x4d_fpn_1x_coco_20200515_075702.log.json) | -| X-101-64x4d-FPN | pytorch | 20e | 10.7 | | 44.5 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/cascade_rcnn/cascade_rcnn_x101_64x4d_fpn_20e_coco.py) | [model](http://download.openmmlab.com/mmdetection/v2.0/cascade_rcnn/cascade_rcnn_x101_64x4d_fpn_20e_coco/cascade_rcnn_x101_64x4d_fpn_20e_coco_20200509_224357-051557b1.pth) | [log](http://download.openmmlab.com/mmdetection/v2.0/cascade_rcnn/cascade_rcnn_x101_64x4d_fpn_20e_coco/cascade_rcnn_x101_64x4d_fpn_20e_coco_20200509_224357.log.json)| +| R-50-FPN | caffe | 1x | 4.2 | | 40.4 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/cascade_rcnn/cascade_rcnn_r50_caffe_fpn_1x_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/cascade_rcnn/cascade_rcnn_r50_caffe_fpn_1x_coco/cascade_rcnn_r50_caffe_fpn_1x_coco_bbox_mAP-0.404_20200504_174853-b857be87.pth) | [log](https://download.openmmlab.com/mmdetection/v2.0/cascade_rcnn/cascade_rcnn_r50_caffe_fpn_1x_coco/cascade_rcnn_r50_caffe_fpn_1x_coco_20200504_174853.log.json) | +| R-50-FPN | pytorch | 1x | 4.4 | 16.1 | 40.3 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/cascade_rcnn/cascade_rcnn_r50_fpn_1x_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/cascade_rcnn/cascade_rcnn_r50_fpn_1x_coco/cascade_rcnn_r50_fpn_1x_coco_20200316-3dc56deb.pth) | [log](https://download.openmmlab.com/mmdetection/v2.0/cascade_rcnn/cascade_rcnn_r50_fpn_1x_coco/cascade_rcnn_r50_fpn_1x_coco_20200316_214748.log.json) | +| R-50-FPN | pytorch | 20e | - | - | 41.0 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/cascade_rcnn/cascade_rcnn_r50_fpn_20e_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/cascade_rcnn/cascade_rcnn_r50_fpn_20e_coco/cascade_rcnn_r50_fpn_20e_coco_bbox_mAP-0.41_20200504_175131-e9872a90.pth) | [log](https://download.openmmlab.com/mmdetection/v2.0/cascade_rcnn/cascade_rcnn_r50_fpn_20e_coco/cascade_rcnn_r50_fpn_20e_coco_20200504_175131.log.json) | +| R-101-FPN | caffe | 1x | 6.2 | | 42.3 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/cascade_rcnn/cascade_rcnn_r101_caffe_fpn_1x_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/cascade_rcnn/cascade_rcnn_r101_caffe_fpn_1x_coco/cascade_rcnn_r101_caffe_fpn_1x_coco_bbox_mAP-0.423_20200504_175649-cab8dbd5.pth) | [log](https://download.openmmlab.com/mmdetection/v2.0/cascade_rcnn/cascade_rcnn_r101_caffe_fpn_1x_coco/cascade_rcnn_r101_caffe_fpn_1x_coco_20200504_175649.log.json) | +| R-101-FPN | pytorch | 1x | 6.4 | 13.5 | 42.0 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/cascade_rcnn/cascade_rcnn_r101_fpn_1x_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/cascade_rcnn/cascade_rcnn_r101_fpn_1x_coco/cascade_rcnn_r101_fpn_1x_coco_20200317-0b6a2fbf.pth) | [log](https://download.openmmlab.com/mmdetection/v2.0/cascade_rcnn/cascade_rcnn_r101_fpn_1x_coco/cascade_rcnn_r101_fpn_1x_coco_20200317_101744.log.json) | +| R-101-FPN | pytorch | 20e | - | - | 42.5 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/cascade_rcnn/cascade_rcnn_r101_fpn_20e_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/cascade_rcnn/cascade_rcnn_r101_fpn_20e_coco/cascade_rcnn_r101_fpn_20e_coco_bbox_mAP-0.425_20200504_231812-5057dcc5.pth) | [log](https://download.openmmlab.com/mmdetection/v2.0/cascade_rcnn/cascade_rcnn_r101_fpn_20e_coco/cascade_rcnn_r101_fpn_20e_coco_20200504_231812.log.json) | +| X-101-32x4d-FPN | pytorch | 1x | 7.6 | 10.9 | 43.7 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/cascade_rcnn/cascade_rcnn_x101_32x4d_fpn_1x_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/cascade_rcnn/cascade_rcnn_x101_32x4d_fpn_1x_coco/cascade_rcnn_x101_32x4d_fpn_1x_coco_20200316-95c2deb6.pth) | [log](https://download.openmmlab.com/mmdetection/v2.0/cascade_rcnn/cascade_rcnn_x101_32x4d_fpn_1x_coco/cascade_rcnn_x101_32x4d_fpn_1x_coco_20200316_055608.log.json) | +| X-101-32x4d-FPN | pytorch | 20e | 7.6 | | 43.7 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/cascade_rcnn/cascade_rcnn_x101_32x4d_fpn_20e_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/cascade_rcnn/cascade_rcnn_x101_32x4d_fpn_20e_coco/cascade_rcnn_x101_32x4d_fpn_20e_coco_20200906_134608-9ae0a720.pth) | [log](https://download.openmmlab.com/mmdetection/v2.0/cascade_rcnn/cascade_rcnn_x101_32x4d_fpn_20e_coco/cascade_rcnn_x101_32x4d_fpn_20e_coco_20200906_134608.log.json) | +| X-101-64x4d-FPN | pytorch | 1x | 10.7 | | 44.7 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/cascade_rcnn/cascade_rcnn_x101_64x4d_fpn_1x_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/cascade_rcnn/cascade_rcnn_x101_64x4d_fpn_1x_coco/cascade_rcnn_x101_64x4d_fpn_1x_coco_20200515_075702-43ce6a30.pth) | [log](https://download.openmmlab.com/mmdetection/v2.0/cascade_rcnn/cascade_rcnn_x101_64x4d_fpn_1x_coco/cascade_rcnn_x101_64x4d_fpn_1x_coco_20200515_075702.log.json) | +| X-101-64x4d-FPN | pytorch | 20e | 10.7 | | 44.5 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/cascade_rcnn/cascade_rcnn_x101_64x4d_fpn_20e_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/cascade_rcnn/cascade_rcnn_x101_64x4d_fpn_20e_coco/cascade_rcnn_x101_64x4d_fpn_20e_coco_20200509_224357-051557b1.pth) | [log](https://download.openmmlab.com/mmdetection/v2.0/cascade_rcnn/cascade_rcnn_x101_64x4d_fpn_20e_coco/cascade_rcnn_x101_64x4d_fpn_20e_coco_20200509_224357.log.json)| ### Cascade Mask R-CNN | Backbone | Style | Lr schd | Mem (GB) | Inf time (fps) | box AP | mask AP | Config | Download | | :-------------: | :-----: | :-----: | :------: | :------------: | :----: | :-----: | :------: | :--------: | -| R-50-FPN | caffe | 1x | 5.9 | | 41.2 | 36.0 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/cascade_rcnn/cascade_mask_rcnn_r50_caffe_fpn_1x_coco.py) | [model](http://download.openmmlab.com/mmdetection/v2.0/cascade_rcnn/cascade_mask_rcnn_r50_caffe_fpn_1x_coco/cascade_mask_rcnn_r50_caffe_fpn_1x_coco_bbox_mAP-0.412__segm_mAP-0.36_20200504_174659-5004b251.pth) | [log](http://download.openmmlab.com/mmdetection/v2.0/cascade_rcnn/cascade_mask_rcnn_r50_caffe_fpn_1x_coco/cascade_mask_rcnn_r50_caffe_fpn_1x_coco_20200504_174659.log.json) | -| R-50-FPN | pytorch | 1x | 6.0 | 11.2 | 41.2 | 35.9 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/cascade_rcnn/cascade_mask_rcnn_r50_fpn_1x_coco.py) | [model](http://download.openmmlab.com/mmdetection/v2.0/cascade_rcnn/cascade_mask_rcnn_r50_fpn_1x_coco/cascade_mask_rcnn_r50_fpn_1x_coco_20200203-9d4dcb24.pth) | [log](http://download.openmmlab.com/mmdetection/v2.0/cascade_rcnn/cascade_mask_rcnn_r50_fpn_1x_coco/cascade_mask_rcnn_r50_fpn_1x_coco_20200203_170449.log.json) | -| R-50-FPN | pytorch | 20e | - | - | 41.9 | 36.5 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/cascade_rcnn/cascade_mask_rcnn_r50_fpn_20e_coco.py) | [model](http://download.openmmlab.com/mmdetection/v2.0/cascade_rcnn/cascade_mask_rcnn_r50_fpn_20e_coco/cascade_mask_rcnn_r50_fpn_20e_coco_bbox_mAP-0.419__segm_mAP-0.365_20200504_174711-4af8e66e.pth) | [log](http://download.openmmlab.com/mmdetection/v2.0/cascade_rcnn/cascade_mask_rcnn_r50_fpn_20e_coco/cascade_mask_rcnn_r50_fpn_20e_coco_20200504_174711.log.json)| -| R-101-FPN | caffe | 1x | 7.8 | | 43.2 | 37.6 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/cascade_rcnn/cascade_mask_rcnn_r101_caffe_fpn_1x_coco.py) | [model](http://download.openmmlab.com/mmdetection/v2.0/cascade_rcnn/cascade_mask_rcnn_r101_caffe_fpn_1x_coco/cascade_mask_rcnn_r101_caffe_fpn_1x_coco_bbox_mAP-0.432__segm_mAP-0.376_20200504_174813-5c1e9599.pth) | [log](http://download.openmmlab.com/mmdetection/v2.0/cascade_rcnn/cascade_mask_rcnn_r101_caffe_fpn_1x_coco/cascade_mask_rcnn_r101_caffe_fpn_1x_coco_20200504_174813.log.json)| -| R-101-FPN | pytorch | 1x | 7.9 | 9.8 | 42.9 | 37.3 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/cascade_rcnn/cascade_mask_rcnn_r101_fpn_1x_coco.py) | [model](http://download.openmmlab.com/mmdetection/v2.0/cascade_rcnn/cascade_mask_rcnn_r101_fpn_1x_coco/cascade_mask_rcnn_r101_fpn_1x_coco_20200203-befdf6ee.pth) | [log](http://download.openmmlab.com/mmdetection/v2.0/cascade_rcnn/cascade_mask_rcnn_r101_fpn_1x_coco/cascade_mask_rcnn_r101_fpn_1x_coco_20200203_092521.log.json) | -| R-101-FPN | pytorch | 20e | - | - | 43.4 | 37.8 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/cascade_rcnn/cascade_mask_rcnn_r101_fpn_20e_coco.py) | [model](http://download.openmmlab.com/mmdetection/v2.0/cascade_rcnn/cascade_mask_rcnn_r101_fpn_20e_coco/cascade_mask_rcnn_r101_fpn_20e_coco_bbox_mAP-0.434__segm_mAP-0.378_20200504_174836-005947da.pth) | [log](http://download.openmmlab.com/mmdetection/v2.0/cascade_rcnn/cascade_mask_rcnn_r101_fpn_20e_coco/cascade_mask_rcnn_r101_fpn_20e_coco_20200504_174836.log.json)| -| X-101-32x4d-FPN | pytorch | 1x | 9.2 | 8.6 | 44.3 | 38.3 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/cascade_rcnn/cascade_mask_rcnn_x101_32x4d_fpn_1x_coco.py) | [model](http://download.openmmlab.com/mmdetection/v2.0/cascade_rcnn/cascade_mask_rcnn_x101_32x4d_fpn_1x_coco/cascade_mask_rcnn_x101_32x4d_fpn_1x_coco_20200201-0f411b1f.pth) | [log](http://download.openmmlab.com/mmdetection/v2.0/cascade_rcnn/cascade_mask_rcnn_x101_32x4d_fpn_1x_coco/cascade_mask_rcnn_x101_32x4d_fpn_1x_coco_20200201_052416.log.json) | -| X-101-32x4d-FPN | pytorch | 20e | 9.2 | - | 45.0 | 39.0 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/cascade_rcnn/cascade_mask_rcnn_x101_32x4d_fpn_20e_coco.py) | [model](http://download.openmmlab.com/mmdetection/v2.0/cascade_rcnn/cascade_mask_rcnn_x101_32x4d_fpn_20e_coco/cascade_mask_rcnn_x101_32x4d_fpn_20e_coco_20200528_083917-ed1f4751.pth) | [log](http://download.openmmlab.com/mmdetection/v2.0/cascade_rcnn/cascade_mask_rcnn_x101_32x4d_fpn_20e_coco/cascade_mask_rcnn_x101_32x4d_fpn_20e_coco_20200528_083917.log.json) | -| X-101-64x4d-FPN | pytorch | 1x | 12.2 | 6.7 | 45.3 | 39.2 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/cascade_rcnn/cascade_mask_rcnn_x101_64x4d_fpn_1x_coco.py) | [model](http://download.openmmlab.com/mmdetection/v2.0/cascade_rcnn/cascade_mask_rcnn_x101_64x4d_fpn_1x_coco/cascade_mask_rcnn_x101_64x4d_fpn_1x_coco_20200203-9a2db89d.pth) | [log](http://download.openmmlab.com/mmdetection/v2.0/cascade_rcnn/cascade_mask_rcnn_x101_64x4d_fpn_1x_coco/cascade_mask_rcnn_x101_64x4d_fpn_1x_coco_20200203_044059.log.json) | -| X-101-64x4d-FPN | pytorch | 20e | 12.2 | | 45.6 |39.5 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/cascade_rcnn/cascade_mask_rcnn_x101_64x4d_fpn_20e_coco.py) | [model](http://download.openmmlab.com/mmdetection/v2.0/cascade_rcnn/cascade_mask_rcnn_x101_64x4d_fpn_20e_coco/cascade_mask_rcnn_x101_64x4d_fpn_20e_coco_20200512_161033-bdb5126a.pth) | [log](http://download.openmmlab.com/mmdetection/v2.0/cascade_rcnn/cascade_mask_rcnn_x101_64x4d_fpn_20e_coco/cascade_mask_rcnn_x101_64x4d_fpn_20e_coco_20200512_161033.log.json)| +| R-50-FPN | caffe | 1x | 5.9 | | 41.2 | 36.0 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/cascade_rcnn/cascade_mask_rcnn_r50_caffe_fpn_1x_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/cascade_rcnn/cascade_mask_rcnn_r50_caffe_fpn_1x_coco/cascade_mask_rcnn_r50_caffe_fpn_1x_coco_bbox_mAP-0.412__segm_mAP-0.36_20200504_174659-5004b251.pth) | [log](https://download.openmmlab.com/mmdetection/v2.0/cascade_rcnn/cascade_mask_rcnn_r50_caffe_fpn_1x_coco/cascade_mask_rcnn_r50_caffe_fpn_1x_coco_20200504_174659.log.json) | +| R-50-FPN | pytorch | 1x | 6.0 | 11.2 | 41.2 | 35.9 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/cascade_rcnn/cascade_mask_rcnn_r50_fpn_1x_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/cascade_rcnn/cascade_mask_rcnn_r50_fpn_1x_coco/cascade_mask_rcnn_r50_fpn_1x_coco_20200203-9d4dcb24.pth) | [log](https://download.openmmlab.com/mmdetection/v2.0/cascade_rcnn/cascade_mask_rcnn_r50_fpn_1x_coco/cascade_mask_rcnn_r50_fpn_1x_coco_20200203_170449.log.json) | +| R-50-FPN | pytorch | 20e | - | - | 41.9 | 36.5 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/cascade_rcnn/cascade_mask_rcnn_r50_fpn_20e_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/cascade_rcnn/cascade_mask_rcnn_r50_fpn_20e_coco/cascade_mask_rcnn_r50_fpn_20e_coco_bbox_mAP-0.419__segm_mAP-0.365_20200504_174711-4af8e66e.pth) | [log](https://download.openmmlab.com/mmdetection/v2.0/cascade_rcnn/cascade_mask_rcnn_r50_fpn_20e_coco/cascade_mask_rcnn_r50_fpn_20e_coco_20200504_174711.log.json)| +| R-101-FPN | caffe | 1x | 7.8 | | 43.2 | 37.6 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/cascade_rcnn/cascade_mask_rcnn_r101_caffe_fpn_1x_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/cascade_rcnn/cascade_mask_rcnn_r101_caffe_fpn_1x_coco/cascade_mask_rcnn_r101_caffe_fpn_1x_coco_bbox_mAP-0.432__segm_mAP-0.376_20200504_174813-5c1e9599.pth) | [log](https://download.openmmlab.com/mmdetection/v2.0/cascade_rcnn/cascade_mask_rcnn_r101_caffe_fpn_1x_coco/cascade_mask_rcnn_r101_caffe_fpn_1x_coco_20200504_174813.log.json)| +| R-101-FPN | pytorch | 1x | 7.9 | 9.8 | 42.9 | 37.3 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/cascade_rcnn/cascade_mask_rcnn_r101_fpn_1x_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/cascade_rcnn/cascade_mask_rcnn_r101_fpn_1x_coco/cascade_mask_rcnn_r101_fpn_1x_coco_20200203-befdf6ee.pth) | [log](https://download.openmmlab.com/mmdetection/v2.0/cascade_rcnn/cascade_mask_rcnn_r101_fpn_1x_coco/cascade_mask_rcnn_r101_fpn_1x_coco_20200203_092521.log.json) | +| R-101-FPN | pytorch | 20e | - | - | 43.4 | 37.8 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/cascade_rcnn/cascade_mask_rcnn_r101_fpn_20e_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/cascade_rcnn/cascade_mask_rcnn_r101_fpn_20e_coco/cascade_mask_rcnn_r101_fpn_20e_coco_bbox_mAP-0.434__segm_mAP-0.378_20200504_174836-005947da.pth) | [log](https://download.openmmlab.com/mmdetection/v2.0/cascade_rcnn/cascade_mask_rcnn_r101_fpn_20e_coco/cascade_mask_rcnn_r101_fpn_20e_coco_20200504_174836.log.json)| +| X-101-32x4d-FPN | pytorch | 1x | 9.2 | 8.6 | 44.3 | 38.3 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/cascade_rcnn/cascade_mask_rcnn_x101_32x4d_fpn_1x_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/cascade_rcnn/cascade_mask_rcnn_x101_32x4d_fpn_1x_coco/cascade_mask_rcnn_x101_32x4d_fpn_1x_coco_20200201-0f411b1f.pth) | [log](https://download.openmmlab.com/mmdetection/v2.0/cascade_rcnn/cascade_mask_rcnn_x101_32x4d_fpn_1x_coco/cascade_mask_rcnn_x101_32x4d_fpn_1x_coco_20200201_052416.log.json) | +| X-101-32x4d-FPN | pytorch | 20e | 9.2 | - | 45.0 | 39.0 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/cascade_rcnn/cascade_mask_rcnn_x101_32x4d_fpn_20e_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/cascade_rcnn/cascade_mask_rcnn_x101_32x4d_fpn_20e_coco/cascade_mask_rcnn_x101_32x4d_fpn_20e_coco_20200528_083917-ed1f4751.pth) | [log](https://download.openmmlab.com/mmdetection/v2.0/cascade_rcnn/cascade_mask_rcnn_x101_32x4d_fpn_20e_coco/cascade_mask_rcnn_x101_32x4d_fpn_20e_coco_20200528_083917.log.json) | +| X-101-64x4d-FPN | pytorch | 1x | 12.2 | 6.7 | 45.3 | 39.2 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/cascade_rcnn/cascade_mask_rcnn_x101_64x4d_fpn_1x_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/cascade_rcnn/cascade_mask_rcnn_x101_64x4d_fpn_1x_coco/cascade_mask_rcnn_x101_64x4d_fpn_1x_coco_20200203-9a2db89d.pth) | [log](https://download.openmmlab.com/mmdetection/v2.0/cascade_rcnn/cascade_mask_rcnn_x101_64x4d_fpn_1x_coco/cascade_mask_rcnn_x101_64x4d_fpn_1x_coco_20200203_044059.log.json) | +| X-101-64x4d-FPN | pytorch | 20e | 12.2 | | 45.6 |39.5 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/cascade_rcnn/cascade_mask_rcnn_x101_64x4d_fpn_20e_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/cascade_rcnn/cascade_mask_rcnn_x101_64x4d_fpn_20e_coco/cascade_mask_rcnn_x101_64x4d_fpn_20e_coco_20200512_161033-bdb5126a.pth) | [log](https://download.openmmlab.com/mmdetection/v2.0/cascade_rcnn/cascade_mask_rcnn_x101_64x4d_fpn_20e_coco/cascade_mask_rcnn_x101_64x4d_fpn_20e_coco_20200512_161033.log.json)| **Notes:** - The `20e` schedule in Cascade (Mask) R-CNN indicates decreasing the lr at 16 and 19 epochs, with a total of 20 epochs. + +## Pre-trained Models + +We also train some models with longer schedules and multi-scale training for Cascade Mask R-CNN. The users could finetune them for downstream tasks. + +| Backbone | Style | Lr schd | Mem (GB) | Inf time (fps) | box AP | mask AP | Config | Download | +| :----------------: | :-----: | :-----: | :------: | :------------: | :----: | :-----: | :------: | :--------: | +| R-50-FPN | caffe | 3x | 5.7 | | 44.0 | 38.1 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/cascade_rcnn/cascade_mask_rcnn_r50_caffe_fpn_mstrain_3x_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/cascade_rcnn/cascade_mask_rcnn_r50_caffe_fpn_mstrain_3x_coco/cascade_mask_rcnn_r50_caffe_fpn_mstrain_3x_coco_20210707_002651-6e29b3a6.pth) | [log](https://download.openmmlab.com/mmdetection/v2.0/cascade_rcnn/cascade_mask_rcnn_r50_caffe_fpn_mstrain_3x_coco/cascade_mask_rcnn_r50_caffe_fpn_mstrain_3x_coco_20210707_002651.log.json) +| R-50-FPN | pytorch| 3x | 5.9 | | 44.3 | 38.5 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/cascade_rcnn/cascade_mask_rcnn_r50_fpn_mstrain_3x_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/cascade_rcnn/cascade_mask_rcnn_r50_fpn_mstrain_3x_coco/cascade_mask_rcnn_r50_fpn_mstrain_3x_coco_20210628_164719-5bdc3824.pth) | [log](https://download.openmmlab.com/mmdetection/v2.0/cascade_rcnn/cascade_mask_rcnn_r50_fpn_mstrain_3x_coco/cascade_mask_rcnn_r50_fpn_mstrain_3x_coco_20210628_164719.log.json) +| R-101-FPN | caffe | 3x | 7.7 | | 45.4 | 39.5 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/cascade_rcnn/cascade_mask_rcnn_r101_caffe_fpn_mstrain_3x_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/cascade_rcnn/cascade_mask_rcnn_r101_caffe_fpn_mstrain_3x_coco/cascade_mask_rcnn_r101_caffe_fpn_mstrain_3x_coco_20210707_002620-a5bd2389.pth) | [log](https://download.openmmlab.com/mmdetection/v2.0/cascade_rcnn/cascade_mask_rcnn_r101_caffe_fpn_mstrain_3x_coco/cascade_mask_rcnn_r101_caffe_fpn_mstrain_3x_coco_20210707_002620.log.json) +| R-101-FPN | pytorch| 3x | 7.8 | | 45.5 | 39.6 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/cascade_rcnn/cascade_mask_rcnn_r101_fpn_mstrain_3x_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/cascade_rcnn/cascade_mask_rcnn_r101_fpn_mstrain_3x_coco/cascade_mask_rcnn_r101_fpn_mstrain_3x_coco_20210628_165236-51a2d363.pth) | [log](https://download.openmmlab.com/mmdetection/v2.0/cascade_rcnn/cascade_mask_rcnn_r101_fpn_mstrain_3x_coco/cascade_mask_rcnn_r101_fpn_mstrain_3x_coco_20210628_165236.log.json) +| X-101-32x4d-FPN | pytorch| 3x | 9.0 | | 46.3 | 40.1 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/cascade_rcnn/cascade_mask_rcnn_x101_32x4d_fpn_mstrain_3x_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/cascade_rcnn/cascade_mask_rcnn_x101_32x4d_fpn_mstrain_3x_coco/cascade_mask_rcnn_x101_32x4d_fpn_mstrain_3x_coco_20210706_225234-40773067.pth) | [log](https://download.openmmlab.com/mmdetection/v2.0/cascade_rcnn/cascade_mask_rcnn_x101_32x4d_fpn_mstrain_3x_coco/cascade_mask_rcnn_x101_32x4d_fpn_mstrain_3x_coco_20210706_225234.log.json) +| X-101-32x8d-FPN | pytorch| 3x | 12.1 | | 46.1 | 39.9 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/cascade_rcnn/cascade_mask_rcnn_x101_32x8d_fpn_mstrain_3x_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/cascade_rcnn/cascade_mask_rcnn_x101_32x8d_fpn_mstrain_3x_coco/cascade_mask_rcnn_x101_32x8d_fpn_mstrain_3x_coco_20210719_180640-9ff7e76f.pth) | [log](https://download.openmmlab.com/mmdetection/v2.0/cascade_rcnn/cascade_mask_rcnn_x101_32x8d_fpn_mstrain_3x_coco/cascade_mask_rcnn_x101_32x8d_fpn_mstrain_3x_coco_20210719_180640.log.json) +| X-101-64x4d-FPN | pytorch| 3x | 12.0 | | 46.6 | 40.3 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/cascade_rcnn/cascade_mask_rcnn_x101_64x4d_fpn_mstrain_3x_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/cascade_rcnn/cascade_mask_rcnn_x101_64x4d_fpn_mstrain_3x_coco/cascade_mask_rcnn_x101_64x4d_fpn_mstrain_3x_coco_20210719_210311-d3e64ba0.pth) | [log](https://download.openmmlab.com/mmdetection/v2.0/cascade_rcnn/cascade_mask_rcnn_x101_64x4d_fpn_mstrain_3x_coco/cascade_mask_rcnn_x101_64x4d_fpn_mstrain_3x_coco_20210719_210311.log.json) diff --git a/configs/cascade_rcnn/cascade_mask_rcnn_r101_caffe_fpn_1x_coco.py b/configs/cascade_rcnn/cascade_mask_rcnn_r101_caffe_fpn_1x_coco.py index f42165d..5ee6231 100644 --- a/configs/cascade_rcnn/cascade_mask_rcnn_r101_caffe_fpn_1x_coco.py +++ b/configs/cascade_rcnn/cascade_mask_rcnn_r101_caffe_fpn_1x_coco.py @@ -1,4 +1,7 @@ _base_ = './cascade_mask_rcnn_r50_caffe_fpn_1x_coco.py' model = dict( - pretrained='open-mmlab://detectron2/resnet101_caffe', - backbone=dict(depth=101)) + backbone=dict( + depth=101, + init_cfg=dict( + type='Pretrained', + checkpoint='open-mmlab://detectron2/resnet101_caffe'))) diff --git a/configs/cascade_rcnn/cascade_mask_rcnn_r101_caffe_fpn_mstrain_3x_coco.py b/configs/cascade_rcnn/cascade_mask_rcnn_r101_caffe_fpn_mstrain_3x_coco.py new file mode 100644 index 0000000..1df87fc --- /dev/null +++ b/configs/cascade_rcnn/cascade_mask_rcnn_r101_caffe_fpn_mstrain_3x_coco.py @@ -0,0 +1,7 @@ +_base_ = './cascade_mask_rcnn_r50_caffe_fpn_mstrain_3x_coco.py' +model = dict( + backbone=dict( + depth=101, + init_cfg=dict( + type='Pretrained', + checkpoint='open-mmlab://detectron2/resnet101_caffe'))) diff --git a/configs/cascade_rcnn/cascade_mask_rcnn_r101_fpn_1x_coco.py b/configs/cascade_rcnn/cascade_mask_rcnn_r101_fpn_1x_coco.py index 9212dda..f59c155 100644 --- a/configs/cascade_rcnn/cascade_mask_rcnn_r101_fpn_1x_coco.py +++ b/configs/cascade_rcnn/cascade_mask_rcnn_r101_fpn_1x_coco.py @@ -1,2 +1,6 @@ _base_ = './cascade_mask_rcnn_r50_fpn_1x_coco.py' -model = dict(pretrained='torchvision://resnet101', backbone=dict(depth=101)) +model = dict( + backbone=dict( + depth=101, + init_cfg=dict(type='Pretrained', + checkpoint='torchvision://resnet101'))) diff --git a/configs/cascade_rcnn/cascade_mask_rcnn_r101_fpn_20e_coco.py b/configs/cascade_rcnn/cascade_mask_rcnn_r101_fpn_20e_coco.py index d069f8c..45ab7ed 100644 --- a/configs/cascade_rcnn/cascade_mask_rcnn_r101_fpn_20e_coco.py +++ b/configs/cascade_rcnn/cascade_mask_rcnn_r101_fpn_20e_coco.py @@ -1,2 +1,6 @@ _base_ = './cascade_mask_rcnn_r50_fpn_20e_coco.py' -model = dict(pretrained='torchvision://resnet101', backbone=dict(depth=101)) +model = dict( + backbone=dict( + depth=101, + init_cfg=dict(type='Pretrained', + checkpoint='torchvision://resnet101'))) diff --git a/configs/cascade_rcnn/cascade_mask_rcnn_r101_fpn_mstrain_3x_coco.py b/configs/cascade_rcnn/cascade_mask_rcnn_r101_fpn_mstrain_3x_coco.py new file mode 100644 index 0000000..1b20f16 --- /dev/null +++ b/configs/cascade_rcnn/cascade_mask_rcnn_r101_fpn_mstrain_3x_coco.py @@ -0,0 +1,6 @@ +_base_ = './cascade_mask_rcnn_r50_fpn_mstrain_3x_coco.py' +model = dict( + backbone=dict( + depth=101, + init_cfg=dict(type='Pretrained', + checkpoint='torchvision://resnet101'))) diff --git a/configs/cascade_rcnn/cascade_mask_rcnn_r50_caffe_fpn_1x_coco.py b/configs/cascade_rcnn/cascade_mask_rcnn_r50_caffe_fpn_1x_coco.py index b371ed7..12d37ef 100644 --- a/configs/cascade_rcnn/cascade_mask_rcnn_r50_caffe_fpn_1x_coco.py +++ b/configs/cascade_rcnn/cascade_mask_rcnn_r50_caffe_fpn_1x_coco.py @@ -1,10 +1,13 @@ _base_ = ['./cascade_mask_rcnn_r50_fpn_1x_coco.py'] model = dict( - pretrained='open-mmlab://detectron2/resnet50_caffe', backbone=dict( - norm_cfg=dict(requires_grad=False), norm_eval=True, style='caffe')) - + norm_cfg=dict(requires_grad=False), + norm_eval=True, + style='caffe', + init_cfg=dict( + type='Pretrained', + checkpoint='open-mmlab://detectron2/resnet50_caffe'))) img_norm_cfg = dict( mean=[103.530, 116.280, 123.675], std=[1.0, 1.0, 1.0], to_rgb=False) train_pipeline = [ diff --git a/configs/cascade_rcnn/cascade_mask_rcnn_r50_caffe_fpn_mstrain_3x_coco.py b/configs/cascade_rcnn/cascade_mask_rcnn_r50_caffe_fpn_mstrain_3x_coco.py new file mode 100644 index 0000000..9fb817e --- /dev/null +++ b/configs/cascade_rcnn/cascade_mask_rcnn_r50_caffe_fpn_mstrain_3x_coco.py @@ -0,0 +1,49 @@ +_base_ = ['./cascade_mask_rcnn_r50_fpn_mstrain_3x_coco.py'] +model = dict( + backbone=dict( + norm_cfg=dict(requires_grad=False), + norm_eval=True, + style='caffe', + init_cfg=dict( + type='Pretrained', + checkpoint='open-mmlab://detectron2/resnet50_caffe'))) + +# use caffe img_norm +img_norm_cfg = dict( + mean=[103.530, 116.280, 123.675], std=[1.0, 1.0, 1.0], to_rgb=False) +# In mstrain 3x config, img_scale=[(1333, 640), (1333, 800)], +# multiscale_mode='range' +train_pipeline = [ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations', with_bbox=True, with_mask=True), + dict( + type='Resize', + img_scale=[(1333, 640), (1333, 800)], + multiscale_mode='range', + keep_ratio=True), + dict(type='RandomFlip', flip_ratio=0.5), + dict(type='Normalize', **img_norm_cfg), + dict(type='Pad', size_divisor=32), + dict(type='DefaultFormatBundle'), + dict(type='Collect', keys=['img', 'gt_bboxes', 'gt_labels', 'gt_masks']), +] +test_pipeline = [ + dict(type='LoadImageFromFile'), + dict( + type='MultiScaleFlipAug', + img_scale=(1333, 800), + flip=False, + transforms=[ + dict(type='Resize', keep_ratio=True), + dict(type='RandomFlip'), + dict(type='Normalize', **img_norm_cfg), + dict(type='Pad', size_divisor=32), + dict(type='ImageToTensor', keys=['img']), + dict(type='Collect', keys=['img']), + ]) +] + +data = dict( + train=dict(dataset=dict(pipeline=train_pipeline)), + val=dict(pipeline=test_pipeline), + test=dict(pipeline=test_pipeline)) diff --git a/configs/cascade_rcnn/cascade_mask_rcnn_r50_fpn_mstrain_3x_coco.py b/configs/cascade_rcnn/cascade_mask_rcnn_r50_fpn_mstrain_3x_coco.py new file mode 100644 index 0000000..ed0c6d1 --- /dev/null +++ b/configs/cascade_rcnn/cascade_mask_rcnn_r50_fpn_mstrain_3x_coco.py @@ -0,0 +1,4 @@ +_base_ = [ + '../common/mstrain_3x_coco_instance.py', + '../_base_/models/cascade_mask_rcnn_r50_fpn.py' +] diff --git a/configs/cascade_rcnn/cascade_mask_rcnn_x101_32x4d_fpn_1x_coco.py b/configs/cascade_rcnn/cascade_mask_rcnn_x101_32x4d_fpn_1x_coco.py index d05eb50..06cbbe7 100644 --- a/configs/cascade_rcnn/cascade_mask_rcnn_x101_32x4d_fpn_1x_coco.py +++ b/configs/cascade_rcnn/cascade_mask_rcnn_x101_32x4d_fpn_1x_coco.py @@ -1,6 +1,5 @@ _base_ = './cascade_mask_rcnn_r50_fpn_1x_coco.py' model = dict( - pretrained='open-mmlab://resnext101_32x4d', backbone=dict( type='ResNeXt', depth=101, @@ -10,4 +9,6 @@ out_indices=(0, 1, 2, 3), frozen_stages=1, norm_cfg=dict(type='BN', requires_grad=True), - style='pytorch')) + style='pytorch', + init_cfg=dict( + type='Pretrained', checkpoint='open-mmlab://resnext101_32x4d'))) diff --git a/configs/cascade_rcnn/cascade_mask_rcnn_x101_32x4d_fpn_20e_coco.py b/configs/cascade_rcnn/cascade_mask_rcnn_x101_32x4d_fpn_20e_coco.py index 0cfc7d7..4e35236 100644 --- a/configs/cascade_rcnn/cascade_mask_rcnn_x101_32x4d_fpn_20e_coco.py +++ b/configs/cascade_rcnn/cascade_mask_rcnn_x101_32x4d_fpn_20e_coco.py @@ -1,6 +1,5 @@ _base_ = './cascade_mask_rcnn_r50_fpn_20e_coco.py' model = dict( - pretrained='open-mmlab://resnext101_32x4d', backbone=dict( type='ResNeXt', depth=101, @@ -10,4 +9,6 @@ out_indices=(0, 1, 2, 3), frozen_stages=1, norm_cfg=dict(type='BN', requires_grad=True), - style='pytorch')) + style='pytorch', + init_cfg=dict( + type='Pretrained', checkpoint='open-mmlab://resnext101_32x4d'))) diff --git a/configs/cascade_rcnn/cascade_mask_rcnn_x101_32x4d_fpn_mstrain_3x_coco.py b/configs/cascade_rcnn/cascade_mask_rcnn_x101_32x4d_fpn_mstrain_3x_coco.py new file mode 100644 index 0000000..7d37d17 --- /dev/null +++ b/configs/cascade_rcnn/cascade_mask_rcnn_x101_32x4d_fpn_mstrain_3x_coco.py @@ -0,0 +1,14 @@ +_base_ = './cascade_mask_rcnn_r50_fpn_mstrain_3x_coco.py' +model = dict( + backbone=dict( + type='ResNeXt', + depth=101, + groups=32, + base_width=4, + num_stages=4, + out_indices=(0, 1, 2, 3), + frozen_stages=1, + norm_cfg=dict(type='BN', requires_grad=True), + style='pytorch', + init_cfg=dict( + type='Pretrained', checkpoint='open-mmlab://resnext101_32x4d'))) diff --git a/configs/cascade_rcnn/cascade_mask_rcnn_x101_32x8d_fpn_mstrain_3x_coco.py b/configs/cascade_rcnn/cascade_mask_rcnn_x101_32x8d_fpn_mstrain_3x_coco.py new file mode 100644 index 0000000..eeec1aa --- /dev/null +++ b/configs/cascade_rcnn/cascade_mask_rcnn_x101_32x8d_fpn_mstrain_3x_coco.py @@ -0,0 +1,60 @@ +_base_ = './cascade_mask_rcnn_r50_fpn_mstrain_3x_coco.py' + +model = dict( + backbone=dict( + type='ResNeXt', + depth=101, + groups=32, + base_width=8, + num_stages=4, + out_indices=(0, 1, 2, 3), + frozen_stages=1, + norm_cfg=dict(type='BN', requires_grad=False), + style='pytorch', + init_cfg=dict( + type='Pretrained', + checkpoint='open-mmlab://detectron2/resnext101_32x8d'))) + +# ResNeXt-101-32x8d model trained with Caffe2 at FB, +# so the mean and std need to be changed. +img_norm_cfg = dict( + mean=[103.530, 116.280, 123.675], + std=[57.375, 57.120, 58.395], + to_rgb=False) + +# In mstrain 3x config, img_scale=[(1333, 640), (1333, 800)], +# multiscale_mode='range' +train_pipeline = [ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations', with_bbox=True, with_mask=True), + dict( + type='Resize', + img_scale=[(1333, 640), (1333, 800)], + multiscale_mode='range', + keep_ratio=True), + dict(type='RandomFlip', flip_ratio=0.5), + dict(type='Normalize', **img_norm_cfg), + dict(type='Pad', size_divisor=32), + dict(type='DefaultFormatBundle'), + dict(type='Collect', keys=['img', 'gt_bboxes', 'gt_labels', 'gt_masks']), +] +test_pipeline = [ + dict(type='LoadImageFromFile'), + dict( + type='MultiScaleFlipAug', + img_scale=(1333, 800), + flip=False, + transforms=[ + dict(type='Resize', keep_ratio=True), + dict(type='RandomFlip'), + dict(type='Normalize', **img_norm_cfg), + dict(type='Pad', size_divisor=32), + dict(type='ImageToTensor', keys=['img']), + dict(type='Collect', keys=['img']), + ]) +] + +data = dict( + train=dict(dataset=dict(pipeline=train_pipeline)), + val=dict(pipeline=test_pipeline), + test=dict(pipeline=test_pipeline)) diff --git a/configs/cascade_rcnn/cascade_mask_rcnn_x101_64x4d_fpn_1x_coco.py b/configs/cascade_rcnn/cascade_mask_rcnn_x101_64x4d_fpn_1x_coco.py index 33629ee..7dbef5f 100644 --- a/configs/cascade_rcnn/cascade_mask_rcnn_x101_64x4d_fpn_1x_coco.py +++ b/configs/cascade_rcnn/cascade_mask_rcnn_x101_64x4d_fpn_1x_coco.py @@ -1,6 +1,5 @@ _base_ = './cascade_mask_rcnn_r50_fpn_1x_coco.py' model = dict( - pretrained='open-mmlab://resnext101_64x4d', backbone=dict( type='ResNeXt', depth=101, @@ -10,4 +9,6 @@ out_indices=(0, 1, 2, 3), frozen_stages=1, norm_cfg=dict(type='BN', requires_grad=True), - style='pytorch')) + style='pytorch', + init_cfg=dict( + type='Pretrained', checkpoint='open-mmlab://resnext101_64x4d'))) diff --git a/configs/cascade_rcnn/cascade_mask_rcnn_x101_64x4d_fpn_20e_coco.py b/configs/cascade_rcnn/cascade_mask_rcnn_x101_64x4d_fpn_20e_coco.py index e64c22c..579b1ac 100644 --- a/configs/cascade_rcnn/cascade_mask_rcnn_x101_64x4d_fpn_20e_coco.py +++ b/configs/cascade_rcnn/cascade_mask_rcnn_x101_64x4d_fpn_20e_coco.py @@ -1,6 +1,5 @@ _base_ = './cascade_mask_rcnn_r50_fpn_20e_coco.py' model = dict( - pretrained='open-mmlab://resnext101_64x4d', backbone=dict( type='ResNeXt', depth=101, @@ -10,4 +9,6 @@ out_indices=(0, 1, 2, 3), frozen_stages=1, norm_cfg=dict(type='BN', requires_grad=True), - style='pytorch')) + style='pytorch', + init_cfg=dict( + type='Pretrained', checkpoint='open-mmlab://resnext101_64x4d'))) diff --git a/configs/cascade_rcnn/cascade_mask_rcnn_x101_64x4d_fpn_mstrain_3x_coco.py b/configs/cascade_rcnn/cascade_mask_rcnn_x101_64x4d_fpn_mstrain_3x_coco.py new file mode 100644 index 0000000..ed6cf4b --- /dev/null +++ b/configs/cascade_rcnn/cascade_mask_rcnn_x101_64x4d_fpn_mstrain_3x_coco.py @@ -0,0 +1,14 @@ +_base_ = './cascade_mask_rcnn_r50_fpn_mstrain_3x_coco.py' +model = dict( + backbone=dict( + type='ResNeXt', + depth=101, + groups=64, + base_width=4, + num_stages=4, + out_indices=(0, 1, 2, 3), + frozen_stages=1, + norm_cfg=dict(type='BN', requires_grad=True), + style='pytorch', + init_cfg=dict( + type='Pretrained', checkpoint='open-mmlab://resnext101_64x4d'))) diff --git a/configs/cascade_rcnn/cascade_rcnn_r101_caffe_fpn_1x_coco.py b/configs/cascade_rcnn/cascade_rcnn_r101_caffe_fpn_1x_coco.py index 8e8b830..1e90f4b 100644 --- a/configs/cascade_rcnn/cascade_rcnn_r101_caffe_fpn_1x_coco.py +++ b/configs/cascade_rcnn/cascade_rcnn_r101_caffe_fpn_1x_coco.py @@ -1,4 +1,7 @@ _base_ = './cascade_rcnn_r50_caffe_fpn_1x_coco.py' model = dict( - pretrained='open-mmlab://detectron2/resnet101_caffe', - backbone=dict(depth=101)) + backbone=dict( + depth=101, + init_cfg=dict( + type='Pretrained', + checkpoint='open-mmlab://detectron2/resnet101_caffe'))) diff --git a/configs/cascade_rcnn/cascade_rcnn_r101_fpn_1x_coco.py b/configs/cascade_rcnn/cascade_rcnn_r101_fpn_1x_coco.py index 6666651..5c07776 100644 --- a/configs/cascade_rcnn/cascade_rcnn_r101_fpn_1x_coco.py +++ b/configs/cascade_rcnn/cascade_rcnn_r101_fpn_1x_coco.py @@ -1,2 +1,6 @@ _base_ = './cascade_rcnn_r50_fpn_1x_coco.py' -model = dict(pretrained='torchvision://resnet101', backbone=dict(depth=101)) +model = dict( + backbone=dict( + depth=101, + init_cfg=dict(type='Pretrained', + checkpoint='torchvision://resnet101'))) diff --git a/configs/cascade_rcnn/cascade_rcnn_r101_fpn_20e_coco.py b/configs/cascade_rcnn/cascade_rcnn_r101_fpn_20e_coco.py index 9cb3581..b1719c2 100644 --- a/configs/cascade_rcnn/cascade_rcnn_r101_fpn_20e_coco.py +++ b/configs/cascade_rcnn/cascade_rcnn_r101_fpn_20e_coco.py @@ -1,2 +1,6 @@ _base_ = './cascade_rcnn_r50_fpn_20e_coco.py' -model = dict(pretrained='torchvision://resnet101', backbone=dict(depth=101)) +model = dict( + backbone=dict( + depth=101, + init_cfg=dict(type='Pretrained', + checkpoint='torchvision://resnet101'))) diff --git a/configs/cascade_rcnn/cascade_rcnn_r50_caffe_fpn_1x_coco.py b/configs/cascade_rcnn/cascade_rcnn_r50_caffe_fpn_1x_coco.py index c576c74..696bcfb 100644 --- a/configs/cascade_rcnn/cascade_rcnn_r50_caffe_fpn_1x_coco.py +++ b/configs/cascade_rcnn/cascade_rcnn_r50_caffe_fpn_1x_coco.py @@ -1,8 +1,12 @@ _base_ = './cascade_rcnn_r50_fpn_1x_coco.py' model = dict( - pretrained='open-mmlab://detectron2/resnet50_caffe', - backbone=dict(norm_cfg=dict(requires_grad=False), style='caffe')) + backbone=dict( + norm_cfg=dict(requires_grad=False), + style='caffe', + init_cfg=dict( + type='Pretrained', + checkpoint='open-mmlab://detectron2/resnet50_caffe'))) # use caffe img_norm img_norm_cfg = dict( diff --git a/configs/cascade_rcnn/cascade_rcnn_x101_32x4d_fpn_1x_coco.py b/configs/cascade_rcnn/cascade_rcnn_x101_32x4d_fpn_1x_coco.py index 1fbe6ce..5ac02c1 100644 --- a/configs/cascade_rcnn/cascade_rcnn_x101_32x4d_fpn_1x_coco.py +++ b/configs/cascade_rcnn/cascade_rcnn_x101_32x4d_fpn_1x_coco.py @@ -1,6 +1,5 @@ _base_ = './cascade_rcnn_r50_fpn_1x_coco.py' model = dict( - pretrained='open-mmlab://resnext101_32x4d', backbone=dict( type='ResNeXt', depth=101, @@ -10,4 +9,6 @@ out_indices=(0, 1, 2, 3), frozen_stages=1, norm_cfg=dict(type='BN', requires_grad=True), - style='pytorch')) + style='pytorch', + init_cfg=dict( + type='Pretrained', checkpoint='open-mmlab://resnext101_32x4d'))) diff --git a/configs/cascade_rcnn/cascade_rcnn_x101_32x4d_fpn_20e_coco.py b/configs/cascade_rcnn/cascade_rcnn_x101_32x4d_fpn_20e_coco.py index 1afeeef..486e45e 100644 --- a/configs/cascade_rcnn/cascade_rcnn_x101_32x4d_fpn_20e_coco.py +++ b/configs/cascade_rcnn/cascade_rcnn_x101_32x4d_fpn_20e_coco.py @@ -1,6 +1,5 @@ _base_ = './cascade_rcnn_r50_fpn_20e_coco.py' model = dict( - pretrained='open-mmlab://resnext101_32x4d', backbone=dict( type='ResNeXt', depth=101, @@ -10,4 +9,6 @@ out_indices=(0, 1, 2, 3), frozen_stages=1, norm_cfg=dict(type='BN', requires_grad=True), - style='pytorch')) + style='pytorch', + init_cfg=dict( + type='Pretrained', checkpoint='open-mmlab://resnext101_32x4d'))) diff --git a/configs/cascade_rcnn/cascade_rcnn_x101_64x4d_fpn_1x_coco.py b/configs/cascade_rcnn/cascade_rcnn_x101_64x4d_fpn_1x_coco.py index b249bfa..78229f0 100644 --- a/configs/cascade_rcnn/cascade_rcnn_x101_64x4d_fpn_1x_coco.py +++ b/configs/cascade_rcnn/cascade_rcnn_x101_64x4d_fpn_1x_coco.py @@ -1,7 +1,6 @@ _base_ = './cascade_rcnn_r50_fpn_1x_coco.py' model = dict( type='CascadeRCNN', - pretrained='open-mmlab://resnext101_64x4d', backbone=dict( type='ResNeXt', depth=101, @@ -11,4 +10,6 @@ out_indices=(0, 1, 2, 3), frozen_stages=1, norm_cfg=dict(type='BN', requires_grad=True), - style='pytorch')) + style='pytorch', + init_cfg=dict( + type='Pretrained', checkpoint='open-mmlab://resnext101_64x4d'))) diff --git a/configs/cascade_rcnn/cascade_rcnn_x101_64x4d_fpn_20e_coco.py b/configs/cascade_rcnn/cascade_rcnn_x101_64x4d_fpn_20e_coco.py index 500b48c..58812de 100644 --- a/configs/cascade_rcnn/cascade_rcnn_x101_64x4d_fpn_20e_coco.py +++ b/configs/cascade_rcnn/cascade_rcnn_x101_64x4d_fpn_20e_coco.py @@ -1,7 +1,6 @@ _base_ = './cascade_rcnn_r50_fpn_20e_coco.py' model = dict( type='CascadeRCNN', - pretrained='open-mmlab://resnext101_64x4d', backbone=dict( type='ResNeXt', depth=101, @@ -11,4 +10,6 @@ out_indices=(0, 1, 2, 3), frozen_stages=1, norm_cfg=dict(type='BN', requires_grad=True), - style='pytorch')) + style='pytorch', + init_cfg=dict( + type='Pretrained', checkpoint='open-mmlab://resnext101_64x4d'))) diff --git a/configs/cascade_rcnn/metafile.yml b/configs/cascade_rcnn/metafile.yml new file mode 100644 index 0000000..1007f2e --- /dev/null +++ b/configs/cascade_rcnn/metafile.yml @@ -0,0 +1,525 @@ +Collections: + - Name: Cascade R-CNN + Metadata: + Training Data: COCO + Training Techniques: + - SGD with Momentum + - Weight Decay + Training Resources: 8x V100 GPUs + Architecture: + - Cascade R-CNN + - FPN + - RPN + - ResNet + - RoIAlign + Paper: + URL: http://dx.doi.org/10.1109/tpami.2019.2956516 + Title: 'Cascade R-CNN: Delving into High Quality Object Detection' + README: configs/cascade_rcnn/README.md + Code: + URL: https://github.com/open-mmlab/mmdetection/blob/v2.0.0/mmdet/models/detectors/cascade_rcnn.py#L6 + Version: v2.0.0 + +Models: + - Name: cascade_rcnn_r50_caffe_fpn_1x_coco + In Collection: Cascade R-CNN + Config: configs/cascade_rcnn/cascade_rcnn_r50_caffe_fpn_1x_coco.py + Metadata: + Training Memory (GB): 4.2 + Epochs: 12 + Results: + - Task: Object Detection + Dataset: COCO + Metrics: + box AP: 40.4 + Weights: https://download.openmmlab.com/mmdetection/v2.0/cascade_rcnn/cascade_rcnn_r50_caffe_fpn_1x_coco/cascade_rcnn_r50_caffe_fpn_1x_coco_bbox_mAP-0.404_20200504_174853-b857be87.pth + + - Name: cascade_rcnn_r50_fpn_1x_coco + In Collection: Cascade R-CNN + Config: configs/cascade_rcnn/cascade_rcnn_r50_fpn_1x_coco.py + Metadata: + Training Memory (GB): 4.4 + inference time (ms/im): + - value: 62.11 + hardware: V100 + backend: PyTorch + batch size: 1 + mode: FP32 + resolution: (800, 1333) + Epochs: 12 + Results: + - Task: Object Detection + Dataset: COCO + Metrics: + box AP: 40.3 + Weights: https://download.openmmlab.com/mmdetection/v2.0/cascade_rcnn/cascade_rcnn_r50_fpn_1x_coco/cascade_rcnn_r50_fpn_1x_coco_20200316-3dc56deb.pth + + - Name: cascade_rcnn_r50_fpn_20e_coco + In Collection: Cascade R-CNN + Config: configs/cascade_rcnn/cascade_rcnn_r50_fpn_20e_coco.py + Metadata: + Training Memory (GB): 4.4 + inference time (ms/im): + - value: 62.11 + hardware: V100 + backend: PyTorch + batch size: 1 + mode: FP32 + resolution: (800, 1333) + Epochs: 20 + Results: + - Task: Object Detection + Dataset: COCO + Metrics: + box AP: 41.0 + Weights: https://download.openmmlab.com/mmdetection/v2.0/cascade_rcnn/cascade_rcnn_r50_fpn_20e_coco/cascade_rcnn_r50_fpn_20e_coco_bbox_mAP-0.41_20200504_175131-e9872a90.pth + + - Name: cascade_rcnn_r101_caffe_fpn_1x_coco + In Collection: Cascade R-CNN + Config: configs/cascade_rcnn/cascade_rcnn_r101_caffe_fpn_1x_coco.py + Metadata: + Training Memory (GB): 6.2 + Epochs: 12 + Results: + - Task: Object Detection + Dataset: COCO + Metrics: + box AP: 42.3 + Weights: https://download.openmmlab.com/mmdetection/v2.0/cascade_rcnn/cascade_rcnn_r101_caffe_fpn_1x_coco/cascade_rcnn_r101_caffe_fpn_1x_coco_bbox_mAP-0.423_20200504_175649-cab8dbd5.pth + + - Name: cascade_rcnn_r101_fpn_1x_coco + In Collection: Cascade R-CNN + Config: configs/cascade_rcnn/cascade_rcnn_r101_fpn_1x_coco.py + Metadata: + Training Memory (GB): 6.4 + inference time (ms/im): + - value: 74.07 + hardware: V100 + backend: PyTorch + batch size: 1 + mode: FP32 + resolution: (800, 1333) + Epochs: 12 + Results: + - Task: Object Detection + Dataset: COCO + Metrics: + box AP: 42.0 + Weights: https://download.openmmlab.com/mmdetection/v2.0/cascade_rcnn/cascade_rcnn_r101_fpn_1x_coco/cascade_rcnn_r101_fpn_1x_coco_20200317-0b6a2fbf.pth + + - Name: cascade_rcnn_r101_fpn_20e_coco + In Collection: Cascade R-CNN + Config: configs/cascade_rcnn/cascade_rcnn_r101_fpn_20e_coco.py + Metadata: + Training Memory (GB): 6.4 + inference time (ms/im): + - value: 74.07 + hardware: V100 + backend: PyTorch + batch size: 1 + mode: FP32 + resolution: (800, 1333) + Epochs: 20 + Results: + - Task: Object Detection + Dataset: COCO + Metrics: + box AP: 42.5 + Weights: https://download.openmmlab.com/mmdetection/v2.0/cascade_rcnn/cascade_rcnn_r101_fpn_20e_coco/cascade_rcnn_r101_fpn_20e_coco_bbox_mAP-0.425_20200504_231812-5057dcc5.pth + + - Name: cascade_rcnn_x101_32x4d_fpn_1x_coco + In Collection: Cascade R-CNN + Config: configs/cascade_rcnn/cascade_rcnn_x101_32x4d_fpn_1x_coco.py + Metadata: + Training Memory (GB): 7.6 + inference time (ms/im): + - value: 91.74 + hardware: V100 + backend: PyTorch + batch size: 1 + mode: FP32 + resolution: (800, 1333) + Epochs: 12 + Results: + - Task: Object Detection + Dataset: COCO + Metrics: + box AP: 43.7 + Weights: https://download.openmmlab.com/mmdetection/v2.0/cascade_rcnn/cascade_rcnn_x101_32x4d_fpn_1x_coco/cascade_rcnn_x101_32x4d_fpn_1x_coco_20200316-95c2deb6.pth + + - Name: cascade_rcnn_x101_32x4d_fpn_20e_coco + In Collection: Cascade R-CNN + Config: configs/cascade_rcnn/cascade_rcnn_x101_32x4d_fpn_20e_coco.py + Metadata: + Training Memory (GB): 7.6 + Epochs: 20 + Results: + - Task: Object Detection + Dataset: COCO + Metrics: + box AP: 43.7 + Weights: https://download.openmmlab.com/mmdetection/v2.0/cascade_rcnn/cascade_rcnn_x101_32x4d_fpn_20e_coco/cascade_rcnn_x101_32x4d_fpn_20e_coco_20200906_134608-9ae0a720.pth + + - Name: cascade_rcnn_x101_64x4d_fpn_1x_coco + In Collection: Cascade R-CNN + Config: configs/cascade_rcnn/cascade_rcnn_x101_64x4d_fpn_1x_coco.py + Metadata: + Training Memory (GB): 10.7 + Epochs: 12 + Results: + - Task: Object Detection + Dataset: COCO + Metrics: + box AP: 44.7 + Weights: https://download.openmmlab.com/mmdetection/v2.0/cascade_rcnn/cascade_rcnn_x101_64x4d_fpn_1x_coco/cascade_rcnn_x101_64x4d_fpn_1x_coco_20200515_075702-43ce6a30.pth + + - Name: cascade_rcnn_x101_64x4d_fpn_20e_coco + In Collection: Cascade R-CNN + Config: configs/cascade_rcnn/cascade_rcnn_x101_64x4d_fpn_20e_coco.py + Metadata: + Training Memory (GB): 10.7 + Epochs: 20 + Results: + - Task: Object Detection + Dataset: COCO + Metrics: + box AP: 44.5 + Weights: https://download.openmmlab.com/mmdetection/v2.0/cascade_rcnn/cascade_rcnn_x101_64x4d_fpn_20e_coco/cascade_rcnn_x101_64x4d_fpn_20e_coco_20200509_224357-051557b1.pth + + - Name: cascade_mask_rcnn_r50_caffe_fpn_1x_coco + In Collection: Cascade R-CNN + Config: configs/cascade_rcnn/cascade_mask_rcnn_r50_caffe_fpn_1x_coco.py + Metadata: + Training Memory (GB): 5.9 + Epochs: 12 + Results: + - Task: Object Detection + Dataset: COCO + Metrics: + box AP: 41.2 + - Task: Instance Segmentation + Dataset: COCO + Metrics: + mask AP: 36.0 + Weights: https://download.openmmlab.com/mmdetection/v2.0/cascade_rcnn/cascade_mask_rcnn_r50_caffe_fpn_1x_coco/cascade_mask_rcnn_r50_caffe_fpn_1x_coco_bbox_mAP-0.412__segm_mAP-0.36_20200504_174659-5004b251.pth + + - Name: cascade_mask_rcnn_r50_fpn_1x_coco + In Collection: Cascade R-CNN + Config: configs/cascade_rcnn/cascade_mask_rcnn_r50_fpn_1x_coco.py + Metadata: + Training Memory (GB): 6.0 + inference time (ms/im): + - value: 89.29 + hardware: V100 + backend: PyTorch + batch size: 1 + mode: FP32 + resolution: (800, 1333) + Epochs: 12 + Results: + - Task: Object Detection + Dataset: COCO + Metrics: + box AP: 41.2 + - Task: Instance Segmentation + Dataset: COCO + Metrics: + mask AP: 35.9 + Weights: https://download.openmmlab.com/mmdetection/v2.0/cascade_rcnn/cascade_mask_rcnn_r50_fpn_1x_coco/cascade_mask_rcnn_r50_fpn_1x_coco_20200203-9d4dcb24.pth + + - Name: cascade_mask_rcnn_r50_fpn_20e_coco + In Collection: Cascade R-CNN + Config: configs/cascade_rcnn/cascade_mask_rcnn_r50_fpn_20e_coco.py + Metadata: + Training Memory (GB): 6.0 + inference time (ms/im): + - value: 89.29 + hardware: V100 + backend: PyTorch + batch size: 1 + mode: FP32 + resolution: (800, 1333) + Epochs: 20 + Results: + - Task: Object Detection + Dataset: COCO + Metrics: + box AP: 41.9 + - Task: Instance Segmentation + Dataset: COCO + Metrics: + mask AP: 36.5 + Weights: https://download.openmmlab.com/mmdetection/v2.0/cascade_rcnn/cascade_mask_rcnn_r50_fpn_20e_coco/cascade_mask_rcnn_r50_fpn_20e_coco_bbox_mAP-0.419__segm_mAP-0.365_20200504_174711-4af8e66e.pth + + - Name: cascade_mask_rcnn_r101_caffe_fpn_1x_coco + In Collection: Cascade R-CNN + Config: configs/cascade_rcnn/cascade_mask_rcnn_r101_caffe_fpn_1x_coco.py + Metadata: + Training Memory (GB): 7.8 + Epochs: 12 + Results: + - Task: Object Detection + Dataset: COCO + Metrics: + box AP: 43.2 + - Task: Instance Segmentation + Dataset: COCO + Metrics: + mask AP: 37.6 + Weights: https://download.openmmlab.com/mmdetection/v2.0/cascade_rcnn/cascade_mask_rcnn_r101_caffe_fpn_1x_coco/cascade_mask_rcnn_r101_caffe_fpn_1x_coco_bbox_mAP-0.432__segm_mAP-0.376_20200504_174813-5c1e9599.pth + + - Name: cascade_mask_rcnn_r101_fpn_1x_coco + In Collection: Cascade R-CNN + Config: configs/cascade_rcnn/cascade_mask_rcnn_r101_fpn_1x_coco.py + Metadata: + Training Memory (GB): 7.9 + inference time (ms/im): + - value: 102.04 + hardware: V100 + backend: PyTorch + batch size: 1 + mode: FP32 + resolution: (800, 1333) + Epochs: 12 + Results: + - Task: Object Detection + Dataset: COCO + Metrics: + box AP: 42.9 + - Task: Instance Segmentation + Dataset: COCO + Metrics: + mask AP: 37.3 + Weights: https://download.openmmlab.com/mmdetection/v2.0/cascade_rcnn/cascade_mask_rcnn_r101_fpn_1x_coco/cascade_mask_rcnn_r101_fpn_1x_coco_20200203-befdf6ee.pth + + - Name: cascade_mask_rcnn_r101_fpn_20e_coco + In Collection: Cascade R-CNN + Config: configs/cascade_rcnn/cascade_mask_rcnn_r101_fpn_20e_coco.py + Metadata: + Training Memory (GB): 7.9 + inference time (ms/im): + - value: 102.04 + hardware: V100 + backend: PyTorch + batch size: 1 + mode: FP32 + resolution: (800, 1333) + Epochs: 20 + Results: + - Task: Object Detection + Dataset: COCO + Metrics: + box AP: 43.4 + - Task: Instance Segmentation + Dataset: COCO + Metrics: + mask AP: 37.8 + Weights: https://download.openmmlab.com/mmdetection/v2.0/cascade_rcnn/cascade_mask_rcnn_r101_fpn_20e_coco/cascade_mask_rcnn_r101_fpn_20e_coco_bbox_mAP-0.434__segm_mAP-0.378_20200504_174836-005947da.pth + + - Name: cascade_mask_rcnn_x101_32x4d_fpn_1x_coco + In Collection: Cascade R-CNN + Config: configs/cascade_rcnn/cascade_mask_rcnn_x101_32x4d_fpn_1x_coco.py + Metadata: + Training Memory (GB): 9.2 + inference time (ms/im): + - value: 116.28 + hardware: V100 + backend: PyTorch + batch size: 1 + mode: FP32 + resolution: (800, 1333) + Epochs: 12 + Results: + - Task: Object Detection + Dataset: COCO + Metrics: + box AP: 44.3 + - Task: Instance Segmentation + Dataset: COCO + Metrics: + mask AP: 38.3 + Weights: https://download.openmmlab.com/mmdetection/v2.0/cascade_rcnn/cascade_mask_rcnn_x101_32x4d_fpn_1x_coco/cascade_mask_rcnn_x101_32x4d_fpn_1x_coco_20200201-0f411b1f.pth + + - Name: cascade_mask_rcnn_x101_32x4d_fpn_20e_coco + In Collection: Cascade R-CNN + Config: configs/cascade_rcnn/cascade_mask_rcnn_x101_32x4d_fpn_20e_coco.py + Metadata: + Training Memory (GB): 9.2 + inference time (ms/im): + - value: 116.28 + hardware: V100 + backend: PyTorch + batch size: 1 + mode: FP32 + resolution: (800, 1333) + Epochs: 20 + Results: + - Task: Object Detection + Dataset: COCO + Metrics: + box AP: 45.0 + - Task: Instance Segmentation + Dataset: COCO + Metrics: + mask AP: 39.0 + Weights: https://download.openmmlab.com/mmdetection/v2.0/cascade_rcnn/cascade_mask_rcnn_x101_32x4d_fpn_20e_coco/cascade_mask_rcnn_x101_32x4d_fpn_20e_coco_20200528_083917-ed1f4751.pth + + - Name: cascade_mask_rcnn_x101_64x4d_fpn_1x_coco + In Collection: Cascade R-CNN + Config: configs/cascade_rcnn/cascade_mask_rcnn_x101_64x4d_fpn_1x_coco.py + Metadata: + Training Memory (GB): 12.2 + inference time (ms/im): + - value: 149.25 + hardware: V100 + backend: PyTorch + batch size: 1 + mode: FP32 + resolution: (800, 1333) + Epochs: 12 + Results: + - Task: Object Detection + Dataset: COCO + Metrics: + box AP: 45.3 + - Task: Instance Segmentation + Dataset: COCO + Metrics: + mask AP: 39.2 + Weights: https://download.openmmlab.com/mmdetection/v2.0/cascade_rcnn/cascade_mask_rcnn_x101_64x4d_fpn_1x_coco/cascade_mask_rcnn_x101_64x4d_fpn_1x_coco_20200203-9a2db89d.pth + + - Name: cascade_mask_rcnn_x101_64x4d_fpn_20e_coco + In Collection: Cascade R-CNN + Config: configs/cascade_rcnn/cascade_mask_rcnn_x101_64x4d_fpn_20e_coco.py + Metadata: + Training Memory (GB): 12.2 + Epochs: 20 + Results: + - Task: Object Detection + Dataset: COCO + Metrics: + box AP: 45.6 + - Task: Instance Segmentation + Dataset: COCO + Metrics: + mask AP: 39.5 + Weights: https://download.openmmlab.com/mmdetection/v2.0/cascade_rcnn/cascade_mask_rcnn_x101_64x4d_fpn_20e_coco/cascade_mask_rcnn_x101_64x4d_fpn_20e_coco_20200512_161033-bdb5126a.pth + + - Name: cascade_mask_rcnn_r50_caffe_fpn_mstrain_3x_coco + In Collection: Cascade R-CNN + Config: configs/cascade_rcnn/cascade_mask_rcnn_r50_caffe_fpn_mstrain_3x_coco.py + Metadata: + Training Memory (GB): 5.7 + Epochs: 36 + Results: + - Task: Object Detection + Dataset: COCO + Metrics: + box AP: 44.0 + - Task: Instance Segmentation + Dataset: COCO + Metrics: + mask AP: 38.1 + Weights: https://download.openmmlab.com/mmdetection/v2.0/cascade_rcnn/cascade_mask_rcnn_r50_caffe_fpn_mstrain_3x_coco/cascade_mask_rcnn_r50_caffe_fpn_mstrain_3x_coco_20210707_002651-6e29b3a6.pth + + - Name: cascade_mask_rcnn_r50_fpn_mstrain_3x_coco + In Collection: Cascade R-CNN + Config: configs/cascade_rcnn/cascade_mask_rcnn_r50_fpn_mstrain_3x_coco.py + Metadata: + Training Memory (GB): 5.9 + Epochs: 36 + Results: + - Task: Object Detection + Dataset: COCO + Metrics: + box AP: 44.3 + - Task: Instance Segmentation + Dataset: COCO + Metrics: + mask AP: 38.5 + Weights: https://download.openmmlab.com/mmdetection/v2.0/cascade_rcnn/cascade_mask_rcnn_r50_fpn_mstrain_3x_coco/cascade_mask_rcnn_r50_fpn_mstrain_3x_coco_20210628_164719-5bdc3824.pth + + - Name: cascade_mask_rcnn_r101_caffe_fpn_mstrain_3x_coco + In Collection: Cascade R-CNN + Config: configs/cascade_rcnn/cascade_mask_rcnn_r101_caffe_fpn_mstrain_3x_coco.py + Metadata: + Training Memory (GB): 7.7 + Epochs: 36 + Results: + - Task: Object Detection + Dataset: COCO + Metrics: + box AP: 45.4 + - Task: Instance Segmentation + Dataset: COCO + Metrics: + mask AP: 39.5 + Weights: https://download.openmmlab.com/mmdetection/v2.0/cascade_rcnn/cascade_mask_rcnn_r101_caffe_fpn_mstrain_3x_coco/cascade_mask_rcnn_r101_caffe_fpn_mstrain_3x_coco_20210707_002620-a5bd2389.pth + + - Name: cascade_mask_rcnn_r101_fpn_mstrain_3x_coco + In Collection: Cascade R-CNN + Config: configs/cascade_rcnn/cascade_mask_rcnn_r101_fpn_mstrain_3x_coco.py + Metadata: + Training Memory (GB): 7.8 + Epochs: 36 + Results: + - Task: Object Detection + Dataset: COCO + Metrics: + box AP: 45.5 + - Task: Instance Segmentation + Dataset: COCO + Metrics: + mask AP: 39.6 + Weights: https://download.openmmlab.com/mmdetection/v2.0/cascade_rcnn/cascade_mask_rcnn_r101_fpn_mstrain_3x_coco/cascade_mask_rcnn_r101_fpn_mstrain_3x_coco_20210628_165236-51a2d363.pth + + - Name: cascade_mask_rcnn_x101_32x4d_fpn_mstrain_3x_coco + In Collection: Cascade R-CNN + Config: configs/cascade_rcnn/cascade_mask_rcnn_x101_32x4d_fpn_mstrain_3x_coco.py + Metadata: + Training Memory (GB): 9.0 + Epochs: 36 + Results: + - Task: Object Detection + Dataset: COCO + Metrics: + box AP: 46.3 + - Task: Instance Segmentation + Dataset: COCO + Metrics: + mask AP: 40.1 + Weights: https://download.openmmlab.com/mmdetection/v2.0/cascade_rcnn/cascade_mask_rcnn_x101_32x4d_fpn_mstrain_3x_coco/cascade_mask_rcnn_x101_32x4d_fpn_mstrain_3x_coco_20210706_225234-40773067.pth + + - Name: cascade_mask_rcnn_x101_32x8d_fpn_mstrain_3x_coco + In Collection: Cascade R-CNN + Config: configs/cascade_rcnn/cascade_mask_rcnn_x101_32x8d_fpn_mstrain_3x_coco.py + Metadata: + Training Memory (GB): 12.1 + Epochs: 36 + Results: + - Task: Object Detection + Dataset: COCO + Metrics: + box AP: 46.1 + - Task: Instance Segmentation + Dataset: COCO + Metrics: + mask AP: 39.9 + Weights: https://download.openmmlab.com/mmdetection/v2.0/cascade_rcnn/cascade_mask_rcnn_x101_32x8d_fpn_mstrain_3x_coco/cascade_mask_rcnn_x101_32x8d_fpn_mstrain_3x_coco_20210719_180640-9ff7e76f.pth + + - Name: cascade_mask_rcnn_x101_64x4d_fpn_mstrain_3x_coco + In Collection: Cascade R-CNN + Config: configs/cascade_rcnn/cascade_mask_rcnn_x101_64x4d_fpn_mstrain_3x_coco.py + Metadata: + Training Memory (GB): 12.0 + Epochs: 36 + Results: + - Task: Object Detection + Dataset: COCO + Metrics: + box AP: 46.6 + - Task: Instance Segmentation + Dataset: COCO + Metrics: + mask AP: 40.3 + Weights: https://download.openmmlab.com/mmdetection/v2.0/cascade_rcnn/cascade_mask_rcnn_x101_64x4d_fpn_mstrain_3x_coco/cascade_mask_rcnn_x101_64x4d_fpn_mstrain_3x_coco_20210719_210311-d3e64ba0.pth diff --git a/configs/cascade_rpn/README.md b/configs/cascade_rpn/README.md index 2b0c6de..aa7782c 100644 --- a/configs/cascade_rpn/README.md +++ b/configs/cascade_rpn/README.md @@ -1,6 +1,6 @@ # Cascade RPN -[ALGORITHM] + We provide the code for reproducing experiment results of [Cascade RPN](https://arxiv.org/abs/1909.06720). diff --git a/configs/cascade_rpn/crpn_fast_rcnn_r50_caffe_fpn_1x_coco.py b/configs/cascade_rpn/crpn_fast_rcnn_r50_caffe_fpn_1x_coco.py index 68c57df..29f5d07 100644 --- a/configs/cascade_rpn/crpn_fast_rcnn_r50_caffe_fpn_1x_coco.py +++ b/configs/cascade_rpn/crpn_fast_rcnn_r50_caffe_fpn_1x_coco.py @@ -1,6 +1,5 @@ _base_ = '../fast_rcnn/fast_rcnn_r50_fpn_1x_coco.py' model = dict( - pretrained='open-mmlab://detectron2/resnet50_caffe', backbone=dict( type='ResNet', depth=50, @@ -9,7 +8,10 @@ frozen_stages=1, norm_cfg=dict(type='BN', requires_grad=False), norm_eval=True, - style='caffe'), + style='caffe', + init_cfg=dict( + type='Pretrained', + checkpoint='open-mmlab://detectron2/resnet50_caffe')), roi_head=dict( bbox_head=dict( bbox_coder=dict(target_stds=[0.04, 0.04, 0.08, 0.08]), diff --git a/configs/centernet/README.md b/configs/centernet/README.md new file mode 100644 index 0000000..a1fcf3b --- /dev/null +++ b/configs/centernet/README.md @@ -0,0 +1,30 @@ +# CenterNet + +## Introduction + + + +```latex +@article{zhou2019objects, + title={Objects as Points}, + author={Zhou, Xingyi and Wang, Dequan and Kr{\"a}henb{\"u}hl, Philipp}, + booktitle={arXiv preprint arXiv:1904.07850}, + year={2019} +} +``` + +## Results and models + +| Backbone | DCN | Mem (GB) | Box AP | Flip box AP| Config | Download | +| :-------------: | :--------: |:----------------: | :------: | :------------: | :----: | :----: | +| ResNet-18 | N | 3.45 | 25.9 | 27.3 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/centernet/centernet_resnet18_140e_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/centernet/centernet_resnet18_140e_coco/centernet_resnet18_140e_coco_20210705_093630-bb5b3bf7.pth) | [log](https://download.openmmlab.com/mmdetection/v2.0/centernet/centernet_resnet18_140e_coco/centernet_resnet18_140e_coco_20210705_093630.log.json) | +| ResNet-18 | Y | 3.47 | 29.5 | 30.9 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/centernet/centernet_resnet18_dcnv2_140e_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/centernet/centernet_resnet18_dcnv2_140e_coco/centernet_resnet18_dcnv2_140e_coco_20210702_155131-c8cd631f.pth) | [log](https://download.openmmlab.com/mmdetection/v2.0/centernet/centernet_resnet18_dcnv2_140e_coco/centernet_resnet18_dcnv2_140e_coco_20210702_155131.log.json) | + +Note: + +- Flip box AP setting is single-scale and `flip=True`. +- Due to complex data enhancement, we find that the performance is unstable and may fluctuate by about 0.4 mAP. mAP 29.4 ~ 29.8 is acceptable in ResNet-18-DCNv2. +- Compared to the source code, we refer to [CenterNet-Better](https://github.com/FateScript/CenterNet-better), and make the following changes + - fix wrong image mean and variance in image normalization to be compatible with the pre-trained backbone. + - Use SGD rather than ADAM optimizer and add warmup and grad clip. + - Use DistributedDataParallel as other models in MMDetection rather than using DataParallel. diff --git a/configs/centernet/centernet_resnet18_140e_coco.py b/configs/centernet/centernet_resnet18_140e_coco.py new file mode 100644 index 0000000..52c86a5 --- /dev/null +++ b/configs/centernet/centernet_resnet18_140e_coco.py @@ -0,0 +1,3 @@ +_base_ = './centernet_resnet18_dcnv2_140e_coco.py' + +model = dict(neck=dict(use_dcn=False)) diff --git a/configs/centernet/centernet_resnet18_dcnv2_140e_coco.py b/configs/centernet/centernet_resnet18_dcnv2_140e_coco.py new file mode 100644 index 0000000..9eb1db7 --- /dev/null +++ b/configs/centernet/centernet_resnet18_dcnv2_140e_coco.py @@ -0,0 +1,122 @@ +_base_ = [ + '../_base_/datasets/coco_detection.py', + '../_base_/schedules/schedule_1x.py', '../_base_/default_runtime.py' +] + +model = dict( + type='CenterNet', + backbone=dict( + type='ResNet', + depth=18, + norm_eval=False, + norm_cfg=dict(type='BN'), + init_cfg=dict(type='Pretrained', checkpoint='torchvision://resnet18')), + neck=dict( + type='CTResNetNeck', + in_channel=512, + num_deconv_filters=(256, 128, 64), + num_deconv_kernels=(4, 4, 4), + use_dcn=True), + bbox_head=dict( + type='CenterNetHead', + num_classes=80, + in_channel=64, + feat_channel=64, + loss_center_heatmap=dict(type='GaussianFocalLoss', loss_weight=1.0), + loss_wh=dict(type='L1Loss', loss_weight=0.1), + loss_offset=dict(type='L1Loss', loss_weight=1.0)), + train_cfg=None, + test_cfg=dict(topk=100, local_maximum_kernel=3, max_per_img=100)) + +# We fixed the incorrect img_norm_cfg problem in the source code. +img_norm_cfg = dict( + mean=[123.675, 116.28, 103.53], std=[58.395, 57.12, 57.375], to_rgb=True) + +train_pipeline = [ + dict(type='LoadImageFromFile', to_float32=True, color_type='color'), + dict(type='LoadAnnotations', with_bbox=True), + dict( + type='PhotoMetricDistortion', + brightness_delta=32, + contrast_range=(0.5, 1.5), + saturation_range=(0.5, 1.5), + hue_delta=18), + dict( + type='RandomCenterCropPad', + crop_size=(512, 512), + ratios=(0.6, 0.7, 0.8, 0.9, 1.0, 1.1, 1.2, 1.3), + mean=[0, 0, 0], + std=[1, 1, 1], + to_rgb=True, + test_pad_mode=None), + dict(type='Resize', img_scale=(512, 512), keep_ratio=True), + dict(type='RandomFlip', flip_ratio=0.5), + dict(type='Normalize', **img_norm_cfg), + dict(type='DefaultFormatBundle'), + dict(type='Collect', keys=['img', 'gt_bboxes', 'gt_labels']) +] +test_pipeline = [ + dict(type='LoadImageFromFile', to_float32=True), + dict( + type='MultiScaleFlipAug', + scale_factor=1.0, + flip=False, + transforms=[ + dict(type='Resize', keep_ratio=True), + dict( + type='RandomCenterCropPad', + ratios=None, + border=None, + mean=[0, 0, 0], + std=[1, 1, 1], + to_rgb=True, + test_mode=True, + test_pad_mode=['logical_or', 31], + test_pad_add_pix=1), + dict(type='RandomFlip'), + dict(type='Normalize', **img_norm_cfg), + dict(type='DefaultFormatBundle'), + dict( + type='Collect', + meta_keys=('filename', 'ori_shape', 'img_shape', 'pad_shape', + 'scale_factor', 'flip', 'flip_direction', + 'img_norm_cfg', 'border'), + keys=['img']) + ]) +] + +dataset_type = 'CocoDataset' +data_root = 'data/coco/' + +# Use RepeatDataset to speed up training +data = dict( + samples_per_gpu=16, + workers_per_gpu=4, + train=dict( + _delete_=True, + type='RepeatDataset', + times=5, + dataset=dict( + type=dataset_type, + ann_file=data_root + 'annotations/instances_train2017.json', + img_prefix=data_root + 'train2017/', + pipeline=train_pipeline)), + val=dict(pipeline=test_pipeline), + test=dict(pipeline=test_pipeline)) + +# optimizer +# Based on the default settings of modern detectors, the SGD effect is better +# than the Adam in the source code, so we use SGD default settings and +# if you use adam+lr5e-4, the map is 29.1. +optimizer_config = dict( + _delete_=True, grad_clip=dict(max_norm=35, norm_type=2)) + +# learning policy +# Based on the default settings of modern detectors, we added warmup settings. +lr_config = dict( + policy='step', + warmup='linear', + warmup_iters=1000, + warmup_ratio=1.0 / 1000, + step=[18, 24]) # the real step is [18*5, 24*5] +runner = dict(max_epochs=28) # the real epoch is 28*5=140 diff --git a/configs/centernet/metafile.yml b/configs/centernet/metafile.yml new file mode 100644 index 0000000..e86e57b --- /dev/null +++ b/configs/centernet/metafile.yml @@ -0,0 +1,46 @@ +Collections: + - Name: CenterNet + Metadata: + Training Data: COCO + Training Techniques: + - SGD with Momentum + - Weight Decay + Training Resources: 8x TITANXP GPUs + Architecture: + - ResNet + Paper: + URL: https://arxiv.org/abs/1904.07850 + Title: 'Objects as Points' + README: configs/centernet/README.md + Code: + URL: https://github.com/open-mmlab/mmdetection/blob/v2.13.0/mmdet/models/detectors/centernet.py#L10 + Version: v2.13.0 + +Models: + - Name: centernet_resnet18_dcnv2_140e_coco + In Collection: CenterNet + Config: configs/centernet/centernet_resnet18_dcnv2_140e_coco.py + Metadata: + Batch Size: 128 + Training Memory (GB): 3.47 + Epochs: 140 + Results: + - Task: Object Detection + Dataset: COCO + Metrics: + box AP: 29.5 + Weights: https://download.openmmlab.com/mmdetection/v2.0/centernet/centernet_resnet18_dcnv2_140e_coco/centernet_resnet18_dcnv2_140e_coco_20210702_155131-c8cd631f.pth + + - Name: centernet_resnet18_140e_coco + In Collection: CenterNet + Config: configs/centernet/centernet_resnet18_140e_coco.py + Metadata: + Batch Size: 128 + Training Memory (GB): 3.45 + Epochs: 140 + Results: + - Task: Object Detection + Dataset: COCO + Metrics: + box AP: 25.9 + Weights: https://download.openmmlab.com/mmdetection/v2.0/centernet/centernet_resnet18_140e_coco/centernet_resnet18_140e_coco_20210705_093630-bb5b3bf7.pth diff --git a/configs/centripetalnet/README.md b/configs/centripetalnet/README.md index 18631da..543cf06 100644 --- a/configs/centripetalnet/README.md +++ b/configs/centripetalnet/README.md @@ -2,7 +2,7 @@ ## Introduction -[ALGORITHM] + ```latex @InProceedings{Dong_2020_CVPR, @@ -18,7 +18,7 @@ year = {2020} | Backbone | Batch Size | Step/Total Epochs | Mem (GB) | Inf time (fps) | box AP | Config | Download | | :-------------: | :--------: |:----------------: | :------: | :------------: | :----: | :------: | :--------: | -| HourglassNet-104 | [16 x 6](./centripetalnet_hourglass104_mstest_16x6_210e_coco.py) | 190/210 | 16.7 | 3.7 | 44.8 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/centripetalnet/centripetalnet_hourglass104_mstest_16x6_210e_coco.py) | [model](http://download.openmmlab.com/mmdetection/v2.0/centripetalnet/centripetalnet_hourglass104_mstest_16x6_210e_coco/centripetalnet_hourglass104_mstest_16x6_210e_coco_20200915_204804-3ccc61e5.pth) | [log](http://download.openmmlab.com/mmdetection/v2.0/centripetalnet/centripetalnet_hourglass104_mstest_16x6_210e_coco/centripetalnet_hourglass104_mstest_16x6_210e_coco_20200915_204804.log.json) | +| HourglassNet-104 | [16 x 6](./centripetalnet_hourglass104_mstest_16x6_210e_coco.py) | 190/210 | 16.7 | 3.7 | 44.8 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/centripetalnet/centripetalnet_hourglass104_mstest_16x6_210e_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/centripetalnet/centripetalnet_hourglass104_mstest_16x6_210e_coco/centripetalnet_hourglass104_mstest_16x6_210e_coco_20200915_204804-3ccc61e5.pth) | [log](https://download.openmmlab.com/mmdetection/v2.0/centripetalnet/centripetalnet_hourglass104_mstest_16x6_210e_coco/centripetalnet_hourglass104_mstest_16x6_210e_coco_20200915_204804.log.json) | Note: diff --git a/configs/centripetalnet/metafile.yml b/configs/centripetalnet/metafile.yml new file mode 100644 index 0000000..61aed3e --- /dev/null +++ b/configs/centripetalnet/metafile.yml @@ -0,0 +1,39 @@ +Collections: + - Name: CentripetalNet + Metadata: + Training Data: COCO + Training Techniques: + - Adam + Training Resources: 16x V100 GPUs + Architecture: + - Corner Pooling + - Stacked Hourglass Network + Paper: + URL: https://arxiv.org/abs/2003.09119 + Title: 'CentripetalNet: Pursuing High-quality Keypoint Pairs for Object Detection' + README: configs/centripetalnet/README.md + Code: + URL: https://github.com/open-mmlab/mmdetection/blob/v2.5.0/mmdet/models/detectors/cornernet.py#L9 + Version: v2.5.0 + +Models: + - Name: centripetalnet_hourglass104_mstest_16x6_210e_coco + In Collection: CentripetalNet + Config: configs/centripetalnet/centripetalnet_hourglass104_mstest_16x6_210e_coco.py + Metadata: + Batch Size: 96 + Training Memory (GB): 16.7 + inference time (ms/im): + - value: 270.27 + hardware: V100 + backend: PyTorch + batch size: 1 + mode: FP32 + resolution: (800, 1333) + Epochs: 210 + Results: + - Task: Object Detection + Dataset: COCO + Metrics: + box AP: 44.8 + Weights: https://download.openmmlab.com/mmdetection/v2.0/centripetalnet/centripetalnet_hourglass104_mstest_16x6_210e_coco/centripetalnet_hourglass104_mstest_16x6_210e_coco_20200915_204804-3ccc61e5.pth diff --git a/configs/cityscapes/README.md b/configs/cityscapes/README.md index d892fc9..b790771 100644 --- a/configs/cityscapes/README.md +++ b/configs/cityscapes/README.md @@ -1,6 +1,6 @@ # Cityscapes Dataset -[DATASET] + ``` @inproceedings{Cordts2016Cityscapes, @@ -24,7 +24,7 @@ | Backbone | Style | Lr schd | Scale | Mem (GB) | Inf time (fps) | box AP | Config | Download | | :-------------: | :-----: | :-----: | :---: | :------: | :------------: | :----: | :------: | :--------: | -| R-50-FPN | pytorch | 1x | 800-1024 | 5.2 | - | 40.3 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/cityscapes/faster_rcnn_r50_fpn_1x_cityscapes.py) | [model](http://download.openmmlab.com/mmdetection/v2.0/cityscapes/faster_rcnn_r50_fpn_1x_cityscapes_20200502-829424c0.pth) | [log](http://download.openmmlab.com/mmdetection/v2.0/cityscapes/faster_rcnn_r50_fpn_1x_cityscapes_20200502_114915.log.json) | +| R-50-FPN | pytorch | 1x | 800-1024 | 5.2 | - | 40.3 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/cityscapes/faster_rcnn_r50_fpn_1x_cityscapes.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/cityscapes/faster_rcnn_r50_fpn_1x_cityscapes_20200502-829424c0.pth) | [log](https://download.openmmlab.com/mmdetection/v2.0/cityscapes/faster_rcnn_r50_fpn_1x_cityscapes_20200502_114915.log.json) | ### Mask R-CNN diff --git a/configs/cityscapes/faster_rcnn_r50_fpn_1x_cityscapes.py b/configs/cityscapes/faster_rcnn_r50_fpn_1x_cityscapes.py index 5b17451..c6da80c 100644 --- a/configs/cityscapes/faster_rcnn_r50_fpn_1x_cityscapes.py +++ b/configs/cityscapes/faster_rcnn_r50_fpn_1x_cityscapes.py @@ -4,7 +4,7 @@ '../_base_/default_runtime.py' ] model = dict( - pretrained=None, + backbone=dict(init_cfg=None), roi_head=dict( bbox_head=dict( type='Shared2FCBBoxHead', diff --git a/configs/cityscapes/mask_rcnn_r50_fpn_1x_cityscapes.py b/configs/cityscapes/mask_rcnn_r50_fpn_1x_cityscapes.py index 0a4d7ca..679890d 100644 --- a/configs/cityscapes/mask_rcnn_r50_fpn_1x_cityscapes.py +++ b/configs/cityscapes/mask_rcnn_r50_fpn_1x_cityscapes.py @@ -3,7 +3,7 @@ '../_base_/datasets/cityscapes_instance.py', '../_base_/default_runtime.py' ] model = dict( - pretrained=None, + backbone=dict(init_cfg=None), roi_head=dict( bbox_head=dict( type='Shared2FCBBoxHead', diff --git a/configs/common/mstrain-poly_3x_coco_instance.py b/configs/common/mstrain-poly_3x_coco_instance.py new file mode 100644 index 0000000..c22ed94 --- /dev/null +++ b/configs/common/mstrain-poly_3x_coco_instance.py @@ -0,0 +1,80 @@ +_base_ = '../_base_/default_runtime.py' +# dataset settings +dataset_type = 'CocoDataset' +data_root = 'data/coco/' +img_norm_cfg = dict( + mean=[123.675, 116.28, 103.53], std=[58.395, 57.12, 57.375], to_rgb=True) + +# In mstrain 3x config, img_scale=[(1333, 640), (1333, 800)], +# multiscale_mode='range' +train_pipeline = [ + dict(type='LoadImageFromFile'), + dict( + type='LoadAnnotations', + with_bbox=True, + with_mask=True, + poly2mask=False), + dict( + type='Resize', + img_scale=[(1333, 640), (1333, 800)], + multiscale_mode='range', + keep_ratio=True), + dict(type='RandomFlip', flip_ratio=0.5), + dict(type='Normalize', **img_norm_cfg), + dict(type='Pad', size_divisor=32), + dict(type='DefaultFormatBundle'), + dict(type='Collect', keys=['img', 'gt_bboxes', 'gt_labels', 'gt_masks']), +] +test_pipeline = [ + dict(type='LoadImageFromFile'), + dict( + type='MultiScaleFlipAug', + img_scale=(1333, 800), + flip=False, + transforms=[ + dict(type='Resize', keep_ratio=True), + dict(type='RandomFlip'), + dict(type='Normalize', **img_norm_cfg), + dict(type='Pad', size_divisor=32), + dict(type='ImageToTensor', keys=['img']), + dict(type='Collect', keys=['img']), + ]) +] + +# Use RepeatDataset to speed up training +data = dict( + samples_per_gpu=2, + workers_per_gpu=2, + train=dict( + type='RepeatDataset', + times=3, + dataset=dict( + type=dataset_type, + ann_file=data_root + 'annotations/instances_train2017.json', + img_prefix=data_root + 'train2017/', + pipeline=train_pipeline)), + val=dict( + type=dataset_type, + ann_file=data_root + 'annotations/instances_val2017.json', + img_prefix=data_root + 'val2017/', + pipeline=test_pipeline), + test=dict( + type=dataset_type, + ann_file=data_root + 'annotations/instances_val2017.json', + img_prefix=data_root + 'val2017/', + pipeline=test_pipeline)) +evaluation = dict(interval=1, metric=['bbox', 'segm']) + +# optimizer +optimizer = dict(type='SGD', lr=0.02, momentum=0.9, weight_decay=0.0001) +optimizer_config = dict(grad_clip=None) + +# learning policy +# Experiments show that using step=[9, 11] has higher performance +lr_config = dict( + policy='step', + warmup='linear', + warmup_iters=500, + warmup_ratio=0.001, + step=[9, 11]) +runner = dict(type='EpochBasedRunner', max_epochs=12) diff --git a/configs/common/mstrain_3x_coco.py b/configs/common/mstrain_3x_coco.py new file mode 100644 index 0000000..80ec8b8 --- /dev/null +++ b/configs/common/mstrain_3x_coco.py @@ -0,0 +1,76 @@ +_base_ = '../_base_/default_runtime.py' +# dataset settings +dataset_type = 'CocoDataset' +data_root = 'data/coco/' +img_norm_cfg = dict( + mean=[123.675, 116.28, 103.53], std=[58.395, 57.12, 57.375], to_rgb=True) + +# In mstrain 3x config, img_scale=[(1333, 640), (1333, 800)], +# multiscale_mode='range' +train_pipeline = [ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations', with_bbox=True), + dict( + type='Resize', + img_scale=[(1333, 640), (1333, 800)], + multiscale_mode='range', + keep_ratio=True), + dict(type='RandomFlip', flip_ratio=0.5), + dict(type='Normalize', **img_norm_cfg), + dict(type='Pad', size_divisor=32), + dict(type='DefaultFormatBundle'), + dict(type='Collect', keys=['img', 'gt_bboxes', 'gt_labels']), +] +test_pipeline = [ + dict(type='LoadImageFromFile'), + dict( + type='MultiScaleFlipAug', + img_scale=(1333, 800), + flip=False, + transforms=[ + dict(type='Resize', keep_ratio=True), + dict(type='RandomFlip'), + dict(type='Normalize', **img_norm_cfg), + dict(type='Pad', size_divisor=32), + dict(type='ImageToTensor', keys=['img']), + dict(type='Collect', keys=['img']), + ]) +] + +# Use RepeatDataset to speed up training +data = dict( + samples_per_gpu=2, + workers_per_gpu=2, + train=dict( + type='RepeatDataset', + times=3, + dataset=dict( + type=dataset_type, + ann_file=data_root + 'annotations/instances_train2017.json', + img_prefix=data_root + 'train2017/', + pipeline=train_pipeline)), + val=dict( + type=dataset_type, + ann_file=data_root + 'annotations/instances_val2017.json', + img_prefix=data_root + 'val2017/', + pipeline=test_pipeline), + test=dict( + type=dataset_type, + ann_file=data_root + 'annotations/instances_val2017.json', + img_prefix=data_root + 'val2017/', + pipeline=test_pipeline)) +evaluation = dict(interval=1, metric='bbox') + +# optimizer +optimizer = dict(type='SGD', lr=0.02, momentum=0.9, weight_decay=0.0001) +optimizer_config = dict(grad_clip=None) + +# learning policy +# Experiments show that using step=[9, 11] has higher performance +lr_config = dict( + policy='step', + warmup='linear', + warmup_iters=500, + warmup_ratio=0.001, + step=[9, 11]) +runner = dict(type='EpochBasedRunner', max_epochs=12) diff --git a/configs/common/mstrain_3x_coco_instance.py b/configs/common/mstrain_3x_coco_instance.py new file mode 100644 index 0000000..50f39be --- /dev/null +++ b/configs/common/mstrain_3x_coco_instance.py @@ -0,0 +1,76 @@ +_base_ = '../_base_/default_runtime.py' +# dataset settings +dataset_type = 'CocoDataset' +data_root = 'data/coco/' +img_norm_cfg = dict( + mean=[123.675, 116.28, 103.53], std=[58.395, 57.12, 57.375], to_rgb=True) + +# In mstrain 3x config, img_scale=[(1333, 640), (1333, 800)], +# multiscale_mode='range' +train_pipeline = [ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations', with_bbox=True, with_mask=True), + dict( + type='Resize', + img_scale=[(1333, 640), (1333, 800)], + multiscale_mode='range', + keep_ratio=True), + dict(type='RandomFlip', flip_ratio=0.5), + dict(type='Normalize', **img_norm_cfg), + dict(type='Pad', size_divisor=32), + dict(type='DefaultFormatBundle'), + dict(type='Collect', keys=['img', 'gt_bboxes', 'gt_labels', 'gt_masks']), +] +test_pipeline = [ + dict(type='LoadImageFromFile'), + dict( + type='MultiScaleFlipAug', + img_scale=(1333, 800), + flip=False, + transforms=[ + dict(type='Resize', keep_ratio=True), + dict(type='RandomFlip'), + dict(type='Normalize', **img_norm_cfg), + dict(type='Pad', size_divisor=32), + dict(type='ImageToTensor', keys=['img']), + dict(type='Collect', keys=['img']), + ]) +] + +# Use RepeatDataset to speed up training +data = dict( + samples_per_gpu=2, + workers_per_gpu=2, + train=dict( + type='RepeatDataset', + times=3, + dataset=dict( + type=dataset_type, + ann_file=data_root + 'annotations/instances_train2017.json', + img_prefix=data_root + 'train2017/', + pipeline=train_pipeline)), + val=dict( + type=dataset_type, + ann_file=data_root + 'annotations/instances_val2017.json', + img_prefix=data_root + 'val2017/', + pipeline=test_pipeline), + test=dict( + type=dataset_type, + ann_file=data_root + 'annotations/instances_val2017.json', + img_prefix=data_root + 'val2017/', + pipeline=test_pipeline)) +evaluation = dict(interval=1, metric=['bbox', 'segm']) + +# optimizer +optimizer = dict(type='SGD', lr=0.02, momentum=0.9, weight_decay=0.0001) +optimizer_config = dict(grad_clip=None) + +# learning policy +# Experiments show that using step=[9, 11] has higher performance +lr_config = dict( + policy='step', + warmup='linear', + warmup_iters=500, + warmup_ratio=0.001, + step=[9, 11]) +runner = dict(type='EpochBasedRunner', max_epochs=12) diff --git a/configs/cornernet/README.md b/configs/cornernet/README.md index 51e5e7a..a060196 100644 --- a/configs/cornernet/README.md +++ b/configs/cornernet/README.md @@ -2,7 +2,7 @@ ## Introduction -[ALGORITHM] + ```latex @inproceedings{law2018cornernet, @@ -19,9 +19,9 @@ | Backbone | Batch Size | Step/Total Epochs | Mem (GB) | Inf time (fps) | box AP | Config | Download | | :-------------: | :--------: |:----------------: | :------: | :------------: | :----: | :------: | :--------: | -| HourglassNet-104 | [10 x 5](./cornernet_hourglass104_mstest_10x5_210e_coco.py) | 180/210 | 13.9 | 4.2 | 41.2 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/cornernet/cornernet_hourglass104_mstest_10x5_210e_coco.py) | [model](http://download.openmmlab.com/mmdetection/v2.0/cornernet/cornernet_hourglass104_mstest_10x5_210e_coco/cornernet_hourglass104_mstest_10x5_210e_coco_20200824_185720-5fefbf1c.pth) | [log](http://download.openmmlab.com/mmdetection/v2.0/cornernet/cornernet_hourglass104_mstest_10x5_210e_coco/cornernet_hourglass104_mstest_10x5_210e_coco_20200824_185720.log.json) | -| HourglassNet-104 | [8 x 6](./cornernet_hourglass104_mstest_8x6_210e_coco.py) | 180/210 | 15.9 | 4.2 | 41.2 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/cornernet/cornernet_hourglass104_mstest_8x6_210e_coco.py) | [model](http://download.openmmlab.com/mmdetection/v2.0/cornernet/cornernet_hourglass104_mstest_8x6_210e_coco/cornernet_hourglass104_mstest_8x6_210e_coco_20200825_150618-79b44c30.pth) | [log](http://download.openmmlab.com/mmdetection/v2.0/cornernet/cornernet_hourglass104_mstest_8x6_210e_coco/cornernet_hourglass104_mstest_8x6_210e_coco_20200825_150618.log.json) | -| HourglassNet-104 | [32 x 3](./cornernet_hourglass104_mstest_32x3_210e_coco.py) | 180/210 | 9.5 | 3.9 | 40.4 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/cornernet/cornernet_hourglass104_mstest_32x3_210e_coco.py) | [model](http://download.openmmlab.com/mmdetection/v2.0/cornernet/cornernet_hourglass104_mstest_32x3_210e_coco/cornernet_hourglass104_mstest_32x3_210e_coco_20200819_203110-1efaea91.pth) | [log](http://download.openmmlab.com/mmdetection/v2.0/cornernet/cornernet_hourglass104_mstest_32x3_210e_coco/cornernet_hourglass104_mstest_32x3_210e_coco_20200819_203110.log.json) | +| HourglassNet-104 | [10 x 5](./cornernet_hourglass104_mstest_10x5_210e_coco.py) | 180/210 | 13.9 | 4.2 | 41.2 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/cornernet/cornernet_hourglass104_mstest_10x5_210e_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/cornernet/cornernet_hourglass104_mstest_10x5_210e_coco/cornernet_hourglass104_mstest_10x5_210e_coco_20200824_185720-5fefbf1c.pth) | [log](https://download.openmmlab.com/mmdetection/v2.0/cornernet/cornernet_hourglass104_mstest_10x5_210e_coco/cornernet_hourglass104_mstest_10x5_210e_coco_20200824_185720.log.json) | +| HourglassNet-104 | [8 x 6](./cornernet_hourglass104_mstest_8x6_210e_coco.py) | 180/210 | 15.9 | 4.2 | 41.2 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/cornernet/cornernet_hourglass104_mstest_8x6_210e_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/cornernet/cornernet_hourglass104_mstest_8x6_210e_coco/cornernet_hourglass104_mstest_8x6_210e_coco_20200825_150618-79b44c30.pth) | [log](https://download.openmmlab.com/mmdetection/v2.0/cornernet/cornernet_hourglass104_mstest_8x6_210e_coco/cornernet_hourglass104_mstest_8x6_210e_coco_20200825_150618.log.json) | +| HourglassNet-104 | [32 x 3](./cornernet_hourglass104_mstest_32x3_210e_coco.py) | 180/210 | 9.5 | 3.9 | 40.4 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/cornernet/cornernet_hourglass104_mstest_32x3_210e_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/cornernet/cornernet_hourglass104_mstest_32x3_210e_coco/cornernet_hourglass104_mstest_32x3_210e_coco_20200819_203110-1efaea91.pth) | [log](https://download.openmmlab.com/mmdetection/v2.0/cornernet/cornernet_hourglass104_mstest_32x3_210e_coco/cornernet_hourglass104_mstest_32x3_210e_coco_20200819_203110.log.json) | Note: diff --git a/configs/cornernet/metafile.yml b/configs/cornernet/metafile.yml new file mode 100644 index 0000000..c2f6143 --- /dev/null +++ b/configs/cornernet/metafile.yml @@ -0,0 +1,83 @@ +Collections: + - Name: CornerNet + Metadata: + Training Data: COCO + Training Techniques: + - Adam + Training Resources: 8x V100 GPUs + Architecture: + - Corner Pooling + - Stacked Hourglass Network + Paper: + URL: https://arxiv.org/abs/1808.01244 + Title: 'CornerNet: Detecting Objects as Paired Keypoints' + README: configs/cornernet/README.md + Code: + URL: https://github.com/open-mmlab/mmdetection/blob/v2.3.0/mmdet/models/detectors/cornernet.py#L9 + Version: v2.3.0 + +Models: + - Name: cornernet_hourglass104_mstest_10x5_210e_coco + In Collection: CornerNet + Config: configs/cornernet/cornernet_hourglass104_mstest_10x5_210e_coco.py + Metadata: + Training Resources: 10x V100 GPUs + Batch Size: 50 + Training Memory (GB): 13.9 + inference time (ms/im): + - value: 238.1 + hardware: V100 + backend: PyTorch + batch size: 1 + mode: FP32 + resolution: (800, 1333) + Epochs: 210 + Results: + - Task: Object Detection + Dataset: COCO + Metrics: + box AP: 41.2 + Weights: https://download.openmmlab.com/mmdetection/v2.0/cornernet/cornernet_hourglass104_mstest_10x5_210e_coco/cornernet_hourglass104_mstest_10x5_210e_coco_20200824_185720-5fefbf1c.pth + + - Name: cornernet_hourglass104_mstest_8x6_210e_coco + In Collection: CornerNet + Config: configs/cornernet/cornernet_hourglass104_mstest_8x6_210e_coco.py + Metadata: + Batch Size: 48 + Training Memory (GB): 15.9 + inference time (ms/im): + - value: 238.1 + hardware: V100 + backend: PyTorch + batch size: 1 + mode: FP32 + resolution: (800, 1333) + Epochs: 210 + Results: + - Task: Object Detection + Dataset: COCO + Metrics: + box AP: 41.2 + Weights: https://download.openmmlab.com/mmdetection/v2.0/cornernet/cornernet_hourglass104_mstest_8x6_210e_coco/cornernet_hourglass104_mstest_8x6_210e_coco_20200825_150618-79b44c30.pth + + - Name: cornernet_hourglass104_mstest_32x3_210e_coco + In Collection: CornerNet + Config: configs/cornernet/cornernet_hourglass104_mstest_32x3_210e_coco.py + Metadata: + Training Resources: 32x V100 GPUs + Batch Size: 96 + Training Memory (GB): 9.5 + inference time (ms/im): + - value: 256.41 + hardware: V100 + backend: PyTorch + batch size: 1 + mode: FP32 + resolution: (800, 1333) + Epochs: 210 + Results: + - Task: Object Detection + Dataset: COCO + Metrics: + box AP: 40.4 + Weights: https://download.openmmlab.com/mmdetection/v2.0/cornernet/cornernet_hourglass104_mstest_32x3_210e_coco/cornernet_hourglass104_mstest_32x3_210e_coco_20200819_203110-1efaea91.pth diff --git a/configs/dcn/README.md b/configs/dcn/README.md index 78e2dc1..044ff29 100644 --- a/configs/dcn/README.md +++ b/configs/dcn/README.md @@ -2,7 +2,7 @@ ## Introduction -[ALGORITHM] + ```none @inproceedings{dai2017deformable, @@ -13,7 +13,7 @@ } ``` -[ALGORITHM] + ``` @article{zhu2018deformable, @@ -28,21 +28,21 @@ | Backbone | Model | Style | Conv | Pool | Lr schd | Mem (GB) | Inf time (fps) | box AP | mask AP | Config | Download | |:----------------:|:------------:|:-------:|:-------------:|:------:|:-------:|:--------:|:--------------:|:------:|:-------:|:------:|:--------:| -| R-50-FPN | Faster | pytorch | dconv(c3-c5) | - | 1x | 4.0 | 17.8 | 41.3 | | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/dcn/faster_rcnn_r50_fpn_dconv_c3-c5_1x_coco.py) | [model](http://download.openmmlab.com/mmdetection/v2.0/dcn/faster_rcnn_r50_fpn_dconv_c3-c5_1x_coco/faster_rcnn_r50_fpn_dconv_c3-c5_1x_coco_20200130-d68aed1e.pth) | [log](http://download.openmmlab.com/mmdetection/v2.0/dcn/faster_rcnn_r50_fpn_dconv_c3-c5_1x_coco/faster_rcnn_r50_fpn_dconv_c3-c5_1x_coco_20200130_212941.log.json) | -| R-50-FPN | Faster | pytorch | mdconv(c3-c5) | - | 1x | 4.1 | 17.6 | 41.4 | | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/dcn/faster_rcnn_r50_fpn_mdconv_c3-c5_1x_coco.py) | [model](http://download.openmmlab.com/mmdetection/v2.0/dcn/faster_rcnn_r50_fpn_mdconv_c3-c5_1x_coco/faster_rcnn_r50_fpn_mdconv_c3-c5_1x_coco_20200130-d099253b.pth) | [log](http://download.openmmlab.com/mmdetection/v2.0/dcn/faster_rcnn_r50_fpn_mdconv_c3-c5_1x_coco/faster_rcnn_r50_fpn_mdconv_c3-c5_1x_coco_20200130_222144.log.json) | -| *R-50-FPN (dg=4) | Faster | pytorch | mdconv(c3-c5) | - | 1x | 4.2 | 17.4 | 41.5 | | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/dcn/faster_rcnn_r50_fpn_mdconv_c3-c5_group4_1x_coco.py) | [model](http://download.openmmlab.com/mmdetection/v2.0/dcn/faster_rcnn_r50_fpn_mdconv_c3-c5_group4_1x_coco/faster_rcnn_r50_fpn_mdconv_c3-c5_group4_1x_coco_20200130-01262257.pth) | [log](http://download.openmmlab.com/mmdetection/v2.0/dcn/faster_rcnn_r50_fpn_mdconv_c3-c5_group4_1x_coco/faster_rcnn_r50_fpn_mdconv_c3-c5_group4_1x_coco_20200130_222058.log.json) | -| R-50-FPN | Faster | pytorch | - | dpool | 1x | 5.0 | 17.2 | 38.9 | | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/dcn/faster_rcnn_r50_fpn_dpool_1x_coco.py) | [model](http://download.openmmlab.com/mmdetection/v2.0/dcn/faster_rcnn_r50_fpn_dpool_1x_coco/faster_rcnn_r50_fpn_dpool_1x_coco_20200307-90d3c01d.pth) | [log](http://download.openmmlab.com/mmdetection/v2.0/dcn/faster_rcnn_r50_fpn_dpool_1x_coco/faster_rcnn_r50_fpn_dpool_1x_coco_20200307_203250.log.json) | -| R-50-FPN | Faster | pytorch | - | mdpool | 1x | 5.8 | 16.6 | 38.7 | | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/dcn/faster_rcnn_r50_fpn_mdpool_1x_coco.py) | [model](http://download.openmmlab.com/mmdetection/v2.0/dcn/faster_rcnn_r50_fpn_mdpool_1x_coco/faster_rcnn_r50_fpn_mdpool_1x_coco_20200307-c0df27ff.pth) | [log](http://download.openmmlab.com/mmdetection/v2.0/dcn/faster_rcnn_r50_fpn_mdpool_1x_coco/faster_rcnn_r50_fpn_mdpool_1x_coco_20200307_203304.log.json) | -| R-101-FPN | Faster | pytorch | dconv(c3-c5) | - | 1x | 6.0 | 12.5 | 42.7 | | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/dcn/faster_rcnn_r101_fpn_dconv_c3-c5_1x_coco.py) | [model](http://download.openmmlab.com/mmdetection/v2.0/dcn/faster_rcnn_r101_fpn_dconv_c3-c5_1x_coco/faster_rcnn_r101_fpn_dconv_c3-c5_1x_coco_20200203-1377f13d.pth) | [log](http://download.openmmlab.com/mmdetection/v2.0/dcn/faster_rcnn_r101_fpn_dconv_c3-c5_1x_coco/faster_rcnn_r101_fpn_dconv_c3-c5_1x_coco_20200203_230019.log.json) | -| X-101-32x4d-FPN | Faster | pytorch | dconv(c3-c5) | - | 1x | 7.3 | 10.0 | 44.5 | | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/dcn/faster_rcnn_x101_32x4d_fpn_dconv_c3-c5_1x_coco.py) | [model](http://download.openmmlab.com/mmdetection/v2.0/dcn/faster_rcnn_x101_32x4d_fpn_dconv_c3-c5_1x_coco/faster_rcnn_x101_32x4d_fpn_dconv_c3-c5_1x_coco_20200203-4f85c69c.pth) | [log](http://download.openmmlab.com/mmdetection/v2.0/dcn/faster_rcnn_x101_32x4d_fpn_dconv_c3-c5_1x_coco/faster_rcnn_x101_32x4d_fpn_dconv_c3-c5_1x_coco_20200203_001325.log.json) | -| R-50-FPN | Mask | pytorch | dconv(c3-c5) | - | 1x | 4.5 | 15.4 | 41.8 | 37.4 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/dcn/mask_rcnn_r50_fpn_dconv_c3-c5_1x_coco.py) | [model](http://download.openmmlab.com/mmdetection/v2.0/dcn/mask_rcnn_r50_fpn_dconv_c3-c5_1x_coco/mask_rcnn_r50_fpn_dconv_c3-c5_1x_coco_20200203-4d9ad43b.pth) | [log](http://download.openmmlab.com/mmdetection/v2.0/dcn/mask_rcnn_r50_fpn_dconv_c3-c5_1x_coco/mask_rcnn_r50_fpn_dconv_c3-c5_1x_coco_20200203_061339.log.json) | -| R-50-FPN | Mask | pytorch | mdconv(c3-c5) | - | 1x | 4.5 | 15.1 | 41.5 | 37.1 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/dcn/mask_rcnn_r50_fpn_mdconv_c3-c5_1x_coco.py) | [model](http://download.openmmlab.com/mmdetection/v2.0/dcn/mask_rcnn_r50_fpn_mdconv_c3-c5_1x_coco/mask_rcnn_r50_fpn_mdconv_c3-c5_1x_coco_20200203-ad97591f.pth) | [log](http://download.openmmlab.com/mmdetection/v2.0/dcn/mask_rcnn_r50_fpn_mdconv_c3-c5_1x_coco/mask_rcnn_r50_fpn_mdconv_c3-c5_1x_coco_20200203_063443.log.json) | -| R-101-FPN | Mask | pytorch | dconv(c3-c5) | - | 1x | 6.5 | 11.7 | 43.5 | 38.9 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/dcn/mask_rcnn_r101_fpn_dconv_c3-c5_1x_coco.py) | [model](http://download.openmmlab.com/mmdetection/v2.0/dcn/mask_rcnn_r101_fpn_dconv_c3-c5_1x_coco/mask_rcnn_r101_fpn_dconv_c3-c5_1x_coco_20200216-a71f5bce.pth) | [log](http://download.openmmlab.com/mmdetection/v2.0/dcn/mask_rcnn_r101_fpn_dconv_c3-c5_1x_coco/mask_rcnn_r101_fpn_dconv_c3-c5_1x_coco_20200216_191601.log.json) | -| R-50-FPN | Cascade | pytorch | dconv(c3-c5) | - | 1x | 4.5 | 14.6 | 43.8 | | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/dcn/cascade_rcnn_r50_fpn_dconv_c3-c5_1x_coco.py) | [model](http://download.openmmlab.com/mmdetection/v2.0/dcn/cascade_rcnn_r50_fpn_dconv_c3-c5_1x_coco/cascade_rcnn_r50_fpn_dconv_c3-c5_1x_coco_20200130-2f1fca44.pth) | [log](http://download.openmmlab.com/mmdetection/v2.0/dcn/cascade_rcnn_r50_fpn_dconv_c3-c5_1x_coco/cascade_rcnn_r50_fpn_dconv_c3-c5_1x_coco_20200130_220843.log.json) | -| R-101-FPN | Cascade | pytorch | dconv(c3-c5) | - | 1x | 6.4 | 11.0 | 45.0 | | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/dcn/cascade_rcnn_r101_fpn_dconv_c3-c5_1x_coco.py) | [model](http://download.openmmlab.com/mmdetection/v2.0/dcn/cascade_rcnn_r101_fpn_dconv_c3-c5_1x_coco/cascade_rcnn_r101_fpn_dconv_c3-c5_1x_coco_20200203-3b2f0594.pth) | [log](http://download.openmmlab.com/mmdetection/v2.0/dcn/cascade_rcnn_r101_fpn_dconv_c3-c5_1x_coco/cascade_rcnn_r101_fpn_dconv_c3-c5_1x_coco_20200203_224829.log.json) | -| R-50-FPN | Cascade Mask | pytorch | dconv(c3-c5) | - | 1x | 6.0 | 10.0 | 44.4 | 38.6 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/dcn/cascade_mask_rcnn_r50_fpn_dconv_c3-c5_1x_coco.py) | [model](http://download.openmmlab.com/mmdetection/v2.0/dcn/cascade_mask_rcnn_r50_fpn_dconv_c3-c5_1x_coco/cascade_mask_rcnn_r50_fpn_dconv_c3-c5_1x_coco_20200202-42e767a2.pth) | [log](http://download.openmmlab.com/mmdetection/v2.0/dcn/cascade_mask_rcnn_r50_fpn_dconv_c3-c5_1x_coco/cascade_mask_rcnn_r50_fpn_dconv_c3-c5_1x_coco_20200202_010309.log.json) | -| R-101-FPN | Cascade Mask | pytorch | dconv(c3-c5) | - | 1x | 8.0 | 8.6 | 45.8 | 39.7 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/dcn/cascade_mask_rcnn_r101_fpn_dconv_c3-c5_1x_coco.py) | [model](http://download.openmmlab.com/mmdetection/v2.0/dcn/cascade_mask_rcnn_r101_fpn_dconv_c3-c5_1x_coco/cascade_mask_rcnn_r101_fpn_dconv_c3-c5_1x_coco_20200204-df0c5f10.pth) | [log](http://download.openmmlab.com/mmdetection/v2.0/dcn/cascade_mask_rcnn_r101_fpn_dconv_c3-c5_1x_coco/cascade_mask_rcnn_r101_fpn_dconv_c3-c5_1x_coco_20200204_134006.log.json) | -| X-101-32x4d-FPN | Cascade Mask | pytorch | dconv(c3-c5) | - | 1x | 9.2 | | 47.3 | 41.1 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/dcn/cascade_mask_rcnn_x101_32x4d_fpn_dconv_c3-c5_1x_coco.py) | [model](http://download.openmmlab.com/mmdetection/v2.0/dcn/cascade_mask_rcnn_x101_32x4d_fpn_dconv_c3-c5_1x_coco/cascade_mask_rcnn_x101_32x4d_fpn_dconv_c3-c5_1x_coco-e75f90c8.pth) | [log](http://download.openmmlab.com/mmdetection/v2.0/dcn/cascade_mask_rcnn_x101_32x4d_fpn_dconv_c3-c5_1x_coco/cascade_mask_rcnn_x101_32x4d_fpn_dconv_c3-c5_1x_coco-20200606_183737.log.json) | +| R-50-FPN | Faster | pytorch | dconv(c3-c5) | - | 1x | 4.0 | 17.8 | 41.3 | | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/dcn/faster_rcnn_r50_fpn_dconv_c3-c5_1x_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/dcn/faster_rcnn_r50_fpn_dconv_c3-c5_1x_coco/faster_rcnn_r50_fpn_dconv_c3-c5_1x_coco_20200130-d68aed1e.pth) | [log](https://download.openmmlab.com/mmdetection/v2.0/dcn/faster_rcnn_r50_fpn_dconv_c3-c5_1x_coco/faster_rcnn_r50_fpn_dconv_c3-c5_1x_coco_20200130_212941.log.json) | +| R-50-FPN | Faster | pytorch | mdconv(c3-c5) | - | 1x | 4.1 | 17.6 | 41.4 | | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/dcn/faster_rcnn_r50_fpn_mdconv_c3-c5_1x_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/dcn/faster_rcnn_r50_fpn_mdconv_c3-c5_1x_coco/faster_rcnn_r50_fpn_mdconv_c3-c5_1x_coco_20200130-d099253b.pth) | [log](https://download.openmmlab.com/mmdetection/v2.0/dcn/faster_rcnn_r50_fpn_mdconv_c3-c5_1x_coco/faster_rcnn_r50_fpn_mdconv_c3-c5_1x_coco_20200130_222144.log.json) | +| *R-50-FPN (dg=4) | Faster | pytorch | mdconv(c3-c5) | - | 1x | 4.2 | 17.4 | 41.5 | | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/dcn/faster_rcnn_r50_fpn_mdconv_c3-c5_group4_1x_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/dcn/faster_rcnn_r50_fpn_mdconv_c3-c5_group4_1x_coco/faster_rcnn_r50_fpn_mdconv_c3-c5_group4_1x_coco_20200130-01262257.pth) | [log](https://download.openmmlab.com/mmdetection/v2.0/dcn/faster_rcnn_r50_fpn_mdconv_c3-c5_group4_1x_coco/faster_rcnn_r50_fpn_mdconv_c3-c5_group4_1x_coco_20200130_222058.log.json) | +| R-50-FPN | Faster | pytorch | - | dpool | 1x | 5.0 | 17.2 | 38.9 | | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/dcn/faster_rcnn_r50_fpn_dpool_1x_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/dcn/faster_rcnn_r50_fpn_dpool_1x_coco/faster_rcnn_r50_fpn_dpool_1x_coco_20200307-90d3c01d.pth) | [log](https://download.openmmlab.com/mmdetection/v2.0/dcn/faster_rcnn_r50_fpn_dpool_1x_coco/faster_rcnn_r50_fpn_dpool_1x_coco_20200307_203250.log.json) | +| R-50-FPN | Faster | pytorch | - | mdpool | 1x | 5.8 | 16.6 | 38.7 | | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/dcn/faster_rcnn_r50_fpn_mdpool_1x_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/dcn/faster_rcnn_r50_fpn_mdpool_1x_coco/faster_rcnn_r50_fpn_mdpool_1x_coco_20200307-c0df27ff.pth) | [log](https://download.openmmlab.com/mmdetection/v2.0/dcn/faster_rcnn_r50_fpn_mdpool_1x_coco/faster_rcnn_r50_fpn_mdpool_1x_coco_20200307_203304.log.json) | +| R-101-FPN | Faster | pytorch | dconv(c3-c5) | - | 1x | 6.0 | 12.5 | 42.7 | | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/dcn/faster_rcnn_r101_fpn_dconv_c3-c5_1x_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/dcn/faster_rcnn_r101_fpn_dconv_c3-c5_1x_coco/faster_rcnn_r101_fpn_dconv_c3-c5_1x_coco_20200203-1377f13d.pth) | [log](https://download.openmmlab.com/mmdetection/v2.0/dcn/faster_rcnn_r101_fpn_dconv_c3-c5_1x_coco/faster_rcnn_r101_fpn_dconv_c3-c5_1x_coco_20200203_230019.log.json) | +| X-101-32x4d-FPN | Faster | pytorch | dconv(c3-c5) | - | 1x | 7.3 | 10.0 | 44.5 | | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/dcn/faster_rcnn_x101_32x4d_fpn_dconv_c3-c5_1x_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/dcn/faster_rcnn_x101_32x4d_fpn_dconv_c3-c5_1x_coco/faster_rcnn_x101_32x4d_fpn_dconv_c3-c5_1x_coco_20200203-4f85c69c.pth) | [log](https://download.openmmlab.com/mmdetection/v2.0/dcn/faster_rcnn_x101_32x4d_fpn_dconv_c3-c5_1x_coco/faster_rcnn_x101_32x4d_fpn_dconv_c3-c5_1x_coco_20200203_001325.log.json) | +| R-50-FPN | Mask | pytorch | dconv(c3-c5) | - | 1x | 4.5 | 15.4 | 41.8 | 37.4 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/dcn/mask_rcnn_r50_fpn_dconv_c3-c5_1x_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/dcn/mask_rcnn_r50_fpn_dconv_c3-c5_1x_coco/mask_rcnn_r50_fpn_dconv_c3-c5_1x_coco_20200203-4d9ad43b.pth) | [log](https://download.openmmlab.com/mmdetection/v2.0/dcn/mask_rcnn_r50_fpn_dconv_c3-c5_1x_coco/mask_rcnn_r50_fpn_dconv_c3-c5_1x_coco_20200203_061339.log.json) | +| R-50-FPN | Mask | pytorch | mdconv(c3-c5) | - | 1x | 4.5 | 15.1 | 41.5 | 37.1 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/dcn/mask_rcnn_r50_fpn_mdconv_c3-c5_1x_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/dcn/mask_rcnn_r50_fpn_mdconv_c3-c5_1x_coco/mask_rcnn_r50_fpn_mdconv_c3-c5_1x_coco_20200203-ad97591f.pth) | [log](https://download.openmmlab.com/mmdetection/v2.0/dcn/mask_rcnn_r50_fpn_mdconv_c3-c5_1x_coco/mask_rcnn_r50_fpn_mdconv_c3-c5_1x_coco_20200203_063443.log.json) | +| R-101-FPN | Mask | pytorch | dconv(c3-c5) | - | 1x | 6.5 | 11.7 | 43.5 | 38.9 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/dcn/mask_rcnn_r101_fpn_dconv_c3-c5_1x_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/dcn/mask_rcnn_r101_fpn_dconv_c3-c5_1x_coco/mask_rcnn_r101_fpn_dconv_c3-c5_1x_coco_20200216-a71f5bce.pth) | [log](https://download.openmmlab.com/mmdetection/v2.0/dcn/mask_rcnn_r101_fpn_dconv_c3-c5_1x_coco/mask_rcnn_r101_fpn_dconv_c3-c5_1x_coco_20200216_191601.log.json) | +| R-50-FPN | Cascade | pytorch | dconv(c3-c5) | - | 1x | 4.5 | 14.6 | 43.8 | | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/dcn/cascade_rcnn_r50_fpn_dconv_c3-c5_1x_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/dcn/cascade_rcnn_r50_fpn_dconv_c3-c5_1x_coco/cascade_rcnn_r50_fpn_dconv_c3-c5_1x_coco_20200130-2f1fca44.pth) | [log](https://download.openmmlab.com/mmdetection/v2.0/dcn/cascade_rcnn_r50_fpn_dconv_c3-c5_1x_coco/cascade_rcnn_r50_fpn_dconv_c3-c5_1x_coco_20200130_220843.log.json) | +| R-101-FPN | Cascade | pytorch | dconv(c3-c5) | - | 1x | 6.4 | 11.0 | 45.0 | | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/dcn/cascade_rcnn_r101_fpn_dconv_c3-c5_1x_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/dcn/cascade_rcnn_r101_fpn_dconv_c3-c5_1x_coco/cascade_rcnn_r101_fpn_dconv_c3-c5_1x_coco_20200203-3b2f0594.pth) | [log](https://download.openmmlab.com/mmdetection/v2.0/dcn/cascade_rcnn_r101_fpn_dconv_c3-c5_1x_coco/cascade_rcnn_r101_fpn_dconv_c3-c5_1x_coco_20200203_224829.log.json) | +| R-50-FPN | Cascade Mask | pytorch | dconv(c3-c5) | - | 1x | 6.0 | 10.0 | 44.4 | 38.6 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/dcn/cascade_mask_rcnn_r50_fpn_dconv_c3-c5_1x_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/dcn/cascade_mask_rcnn_r50_fpn_dconv_c3-c5_1x_coco/cascade_mask_rcnn_r50_fpn_dconv_c3-c5_1x_coco_20200202-42e767a2.pth) | [log](https://download.openmmlab.com/mmdetection/v2.0/dcn/cascade_mask_rcnn_r50_fpn_dconv_c3-c5_1x_coco/cascade_mask_rcnn_r50_fpn_dconv_c3-c5_1x_coco_20200202_010309.log.json) | +| R-101-FPN | Cascade Mask | pytorch | dconv(c3-c5) | - | 1x | 8.0 | 8.6 | 45.8 | 39.7 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/dcn/cascade_mask_rcnn_r101_fpn_dconv_c3-c5_1x_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/dcn/cascade_mask_rcnn_r101_fpn_dconv_c3-c5_1x_coco/cascade_mask_rcnn_r101_fpn_dconv_c3-c5_1x_coco_20200204-df0c5f10.pth) | [log](https://download.openmmlab.com/mmdetection/v2.0/dcn/cascade_mask_rcnn_r101_fpn_dconv_c3-c5_1x_coco/cascade_mask_rcnn_r101_fpn_dconv_c3-c5_1x_coco_20200204_134006.log.json) | +| X-101-32x4d-FPN | Cascade Mask | pytorch | dconv(c3-c5) | - | 1x | 9.2 | | 47.3 | 41.1 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/dcn/cascade_mask_rcnn_x101_32x4d_fpn_dconv_c3-c5_1x_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/dcn/cascade_mask_rcnn_x101_32x4d_fpn_dconv_c3-c5_1x_coco/cascade_mask_rcnn_x101_32x4d_fpn_dconv_c3-c5_1x_coco-e75f90c8.pth) | [log](https://download.openmmlab.com/mmdetection/v2.0/dcn/cascade_mask_rcnn_x101_32x4d_fpn_dconv_c3-c5_1x_coco/cascade_mask_rcnn_x101_32x4d_fpn_dconv_c3-c5_1x_coco-20200606_183737.log.json) | **Notes:** diff --git a/configs/dcn/faster_rcnn_x101_32x4d_fpn_dconv_c3-c5_1x_coco.py b/configs/dcn/faster_rcnn_x101_32x4d_fpn_dconv_c3-c5_1x_coco.py index 8357766..e3bea19 100644 --- a/configs/dcn/faster_rcnn_x101_32x4d_fpn_dconv_c3-c5_1x_coco.py +++ b/configs/dcn/faster_rcnn_x101_32x4d_fpn_dconv_c3-c5_1x_coco.py @@ -1,6 +1,5 @@ _base_ = '../faster_rcnn/faster_rcnn_r50_fpn_1x_coco.py' model = dict( - pretrained='open-mmlab://resnext101_32x4d', backbone=dict( type='ResNeXt', depth=101, @@ -12,4 +11,6 @@ norm_cfg=dict(type='BN', requires_grad=True), style='pytorch', dcn=dict(type='DCN', deform_groups=1, fallback_on_stride=False), - stage_with_dcn=(False, True, True, True))) + stage_with_dcn=(False, True, True, True), + init_cfg=dict( + type='Pretrained', checkpoint='open-mmlab://resnext101_32x4d'))) diff --git a/configs/dcn/metafile.yml b/configs/dcn/metafile.yml new file mode 100644 index 0000000..4fcbce6 --- /dev/null +++ b/configs/dcn/metafile.yml @@ -0,0 +1,335 @@ +Collections: + - Name: Deformable Convolutional Networks + Metadata: + Training Data: COCO + Training Techniques: + - SGD with Momentum + - Weight Decay + Training Resources: 8x V100 GPUs + Architecture: + - Deformable Convolution + Paper: + URL: https://arxiv.org/abs/1811.11168 + Title: 'Deformable ConvNets v2: More Deformable, Better Results' + README: configs/dcn/README.md + Code: + URL: https://github.com/open-mmlab/mmdetection/blob/v2.0.0/mmdet/ops/dcn/deform_conv.py#L15 + Version: v2.0.0 + +Models: + - Name: faster_rcnn_r50_fpn_dconv_c3-c5_1x_coco + In Collection: Deformable Convolutional Networks + Config: configs/dcn/faster_rcnn_r50_fpn_dconv_c3-c5_1x_coco.py + Metadata: + Training Memory (GB): 4.0 + inference time (ms/im): + - value: 56.18 + hardware: V100 + backend: PyTorch + batch size: 1 + mode: FP32 + resolution: (800, 1333) + Epochs: 12 + Results: + - Task: Object Detection + Dataset: COCO + Metrics: + box AP: 41.3 + Weights: https://download.openmmlab.com/mmdetection/v2.0/dcn/faster_rcnn_r50_fpn_dconv_c3-c5_1x_coco/faster_rcnn_r50_fpn_dconv_c3-c5_1x_coco_20200130-d68aed1e.pth + + - Name: faster_rcnn_r50_fpn_mdconv_c3-c5_1x_coco + In Collection: Deformable Convolutional Networks + Config: configs/dcn/faster_rcnn_r50_fpn_mdconv_c3-c5_1x_coco.py + Metadata: + Training Memory (GB): 4.1 + inference time (ms/im): + - value: 56.82 + hardware: V100 + backend: PyTorch + batch size: 1 + mode: FP32 + resolution: (800, 1333) + Epochs: 12 + Results: + - Task: Object Detection + Dataset: COCO + Metrics: + box AP: 41.4 + Weights: https://download.openmmlab.com/mmdetection/v2.0/dcn/faster_rcnn_r50_fpn_mdconv_c3-c5_1x_coco/faster_rcnn_r50_fpn_mdconv_c3-c5_1x_coco_20200130-d099253b.pth + + - Name: faster_rcnn_r50_fpn_mdconv_c3-c5_group4_1x_coco + In Collection: Deformable Convolutional Networks + Config: configs/dcn/faster_rcnn_r50_fpn_mdconv_c3-c5_group4_1x_coco.py + Metadata: + Training Memory (GB): 4.2 + inference time (ms/im): + - value: 57.47 + hardware: V100 + backend: PyTorch + batch size: 1 + mode: FP32 + resolution: (800, 1333) + Epochs: 12 + Results: + - Task: Object Detection + Dataset: COCO + Metrics: + box AP: 41.5 + Weights: https://download.openmmlab.com/mmdetection/v2.0/dcn/faster_rcnn_r50_fpn_mdconv_c3-c5_group4_1x_coco/faster_rcnn_r50_fpn_mdconv_c3-c5_group4_1x_coco_20200130-01262257.pth + + - Name: faster_rcnn_r50_fpn_dpool_1x_coco + In Collection: Deformable Convolutional Networks + Config: configs/dcn/faster_rcnn_r50_fpn_dpool_1x_coco.py + Metadata: + Training Memory (GB): 5.0 + inference time (ms/im): + - value: 58.14 + hardware: V100 + backend: PyTorch + batch size: 1 + mode: FP32 + resolution: (800, 1333) + Epochs: 12 + Results: + - Task: Object Detection + Dataset: COCO + Metrics: + box AP: 38.9 + Weights: https://download.openmmlab.com/mmdetection/v2.0/dcn/faster_rcnn_r50_fpn_dpool_1x_coco/faster_rcnn_r50_fpn_dpool_1x_coco_20200307-90d3c01d.pth + + - Name: faster_rcnn_r50_fpn_mdpool_1x_coco + In Collection: Deformable Convolutional Networks + Config: configs/dcn/faster_rcnn_r50_fpn_mdpool_1x_coco.py + Metadata: + Training Memory (GB): 5.8 + inference time (ms/im): + - value: 60.24 + hardware: V100 + backend: PyTorch + batch size: 1 + mode: FP32 + resolution: (800, 1333) + Epochs: 12 + Results: + - Task: Object Detection + Dataset: COCO + Metrics: + box AP: 38.7 + Weights: https://download.openmmlab.com/mmdetection/v2.0/dcn/faster_rcnn_r50_fpn_mdpool_1x_coco/faster_rcnn_r50_fpn_mdpool_1x_coco_20200307-c0df27ff.pth + + - Name: faster_rcnn_r101_fpn_dconv_c3-c5_1x_coco + In Collection: Deformable Convolutional Networks + Config: configs/dcn/faster_rcnn_r101_fpn_dconv_c3-c5_1x_coco.py + Metadata: + Training Memory (GB): 6.0 + inference time (ms/im): + - value: 80 + hardware: V100 + backend: PyTorch + batch size: 1 + mode: FP32 + resolution: (800, 1333) + Epochs: 12 + Results: + - Task: Object Detection + Dataset: COCO + Metrics: + box AP: 42.7 + Weights: https://download.openmmlab.com/mmdetection/v2.0/dcn/faster_rcnn_r101_fpn_dconv_c3-c5_1x_coco/faster_rcnn_r101_fpn_dconv_c3-c5_1x_coco_20200203-1377f13d.pth + + - Name: faster_rcnn_x101_32x4d_fpn_dconv_c3-c5_1x_coco + In Collection: Deformable Convolutional Networks + Config: configs/dcn/faster_rcnn_x101_32x4d_fpn_dconv_c3-c5_1x_coco.py + Metadata: + Training Memory (GB): 7.3 + inference time (ms/im): + - value: 100 + hardware: V100 + backend: PyTorch + batch size: 1 + mode: FP32 + resolution: (800, 1333) + Epochs: 12 + Results: + - Task: Object Detection + Dataset: COCO + Metrics: + box AP: 44.5 + Weights: https://download.openmmlab.com/mmdetection/v2.0/dcn/faster_rcnn_x101_32x4d_fpn_dconv_c3-c5_1x_coco/faster_rcnn_x101_32x4d_fpn_dconv_c3-c5_1x_coco_20200203-4f85c69c.pth + + - Name: mask_rcnn_r50_fpn_dconv_c3-c5_1x_coco + In Collection: Deformable Convolutional Networks + Config: configs/dcn/mask_rcnn_r50_fpn_dconv_c3-c5_1x_coco.py + Metadata: + Training Memory (GB): 4.5 + inference time (ms/im): + - value: 64.94 + hardware: V100 + backend: PyTorch + batch size: 1 + mode: FP32 + resolution: (800, 1333) + Epochs: 12 + Results: + - Task: Object Detection + Dataset: COCO + Metrics: + box AP: 41.8 + - Task: Instance Segmentation + Dataset: COCO + Metrics: + mask AP: 37.4 + Weights: https://download.openmmlab.com/mmdetection/v2.0/dcn/mask_rcnn_r50_fpn_dconv_c3-c5_1x_coco/mask_rcnn_r50_fpn_dconv_c3-c5_1x_coco_20200203-4d9ad43b.pth + + - Name: mask_rcnn_r50_fpn_mdconv_c3-c5_1x_coco + In Collection: Deformable Convolutional Networks + Config: configs/dcn/mask_rcnn_r50_fpn_mdconv_c3-c5_1x_coco.py + Metadata: + Training Memory (GB): 4.5 + inference time (ms/im): + - value: 66.23 + hardware: V100 + backend: PyTorch + batch size: 1 + mode: FP32 + resolution: (800, 1333) + Epochs: 12 + Results: + - Task: Object Detection + Dataset: COCO + Metrics: + box AP: 41.5 + - Task: Instance Segmentation + Dataset: COCO + Metrics: + mask AP: 37.1 + Weights: https://download.openmmlab.com/mmdetection/v2.0/dcn/mask_rcnn_r50_fpn_mdconv_c3-c5_1x_coco/mask_rcnn_r50_fpn_mdconv_c3-c5_1x_coco_20200203-ad97591f.pth + + - Name: mask_rcnn_r101_fpn_dconv_c3-c5_1x_coco + In Collection: Deformable Convolutional Networks + Config: configs/dcn/mask_rcnn_r101_fpn_dconv_c3-c5_1x_coco.py + Metadata: + Training Memory (GB): 6.5 + inference time (ms/im): + - value: 85.47 + hardware: V100 + backend: PyTorch + batch size: 1 + mode: FP32 + resolution: (800, 1333) + Epochs: 12 + Results: + - Task: Object Detection + Dataset: COCO + Metrics: + box AP: 43.5 + - Task: Instance Segmentation + Dataset: COCO + Metrics: + mask AP: 38.9 + Weights: https://download.openmmlab.com/mmdetection/v2.0/dcn/mask_rcnn_r101_fpn_dconv_c3-c5_1x_coco/mask_rcnn_r101_fpn_dconv_c3-c5_1x_coco_20200216-a71f5bce.pth + + - Name: cascade_rcnn_r50_fpn_dconv_c3-c5_1x_coco + In Collection: Deformable Convolutional Networks + Config: configs/dcn/cascade_rcnn_r50_fpn_dconv_c3-c5_1x_coco.py + Metadata: + Training Memory (GB): 4.5 + inference time (ms/im): + - value: 68.49 + hardware: V100 + backend: PyTorch + batch size: 1 + mode: FP32 + resolution: (800, 1333) + Epochs: 12 + Results: + - Task: Object Detection + Dataset: COCO + Metrics: + box AP: 43.8 + Weights: https://download.openmmlab.com/mmdetection/v2.0/dcn/cascade_rcnn_r50_fpn_dconv_c3-c5_1x_coco/cascade_rcnn_r50_fpn_dconv_c3-c5_1x_coco_20200130-2f1fca44.pth + + - Name: cascade_rcnn_r101_fpn_dconv_c3-c5_1x_coco + In Collection: Deformable Convolutional Networks + Config: configs/dcn/cascade_rcnn_r101_fpn_dconv_c3-c5_1x_coco.py + Metadata: + Training Memory (GB): 6.4 + inference time (ms/im): + - value: 90.91 + hardware: V100 + backend: PyTorch + batch size: 1 + mode: FP32 + resolution: (800, 1333) + Epochs: 12 + Results: + - Task: Object Detection + Dataset: COCO + Metrics: + box AP: 45.0 + Weights: https://download.openmmlab.com/mmdetection/v2.0/dcn/cascade_rcnn_r101_fpn_dconv_c3-c5_1x_coco/cascade_rcnn_r101_fpn_dconv_c3-c5_1x_coco_20200203-3b2f0594.pth + + - Name: cascade_mask_rcnn_r50_fpn_dconv_c3-c5_1x_coco + In Collection: Deformable Convolutional Networks + Config: configs/dcn/cascade_mask_rcnn_r50_fpn_dconv_c3-c5_1x_coco.py + Metadata: + Training Memory (GB): 6.0 + inference time (ms/im): + - value: 100 + hardware: V100 + backend: PyTorch + batch size: 1 + mode: FP32 + resolution: (800, 1333) + Epochs: 12 + Results: + - Task: Object Detection + Dataset: COCO + Metrics: + box AP: 44.4 + - Task: Instance Segmentation + Dataset: COCO + Metrics: + mask AP: 38.6 + Weights: https://download.openmmlab.com/mmdetection/v2.0/dcn/cascade_mask_rcnn_r50_fpn_dconv_c3-c5_1x_coco/cascade_mask_rcnn_r50_fpn_dconv_c3-c5_1x_coco_20200202-42e767a2.pth + + - Name: cascade_mask_rcnn_r101_fpn_dconv_c3-c5_1x_coco + In Collection: Deformable Convolutional Networks + Config: configs/dcn/cascade_mask_rcnn_r101_fpn_dconv_c3-c5_1x_coco.py + Metadata: + Training Memory (GB): 8.0 + inference time (ms/im): + - value: 116.28 + hardware: V100 + backend: PyTorch + batch size: 1 + mode: FP32 + resolution: (800, 1333) + Epochs: 12 + Results: + - Task: Object Detection + Dataset: COCO + Metrics: + box AP: 45.8 + - Task: Instance Segmentation + Dataset: COCO + Metrics: + mask AP: 39.7 + Weights: https://download.openmmlab.com/mmdetection/v2.0/dcn/cascade_mask_rcnn_r101_fpn_dconv_c3-c5_1x_coco/cascade_mask_rcnn_r101_fpn_dconv_c3-c5_1x_coco_20200204-df0c5f10.pth + + - Name: cascade_mask_rcnn_x101_32x4d_fpn_dconv_c3-c5_1x_coco + In Collection: Deformable Convolutional Networks + Config: configs/dcn/cascade_mask_rcnn_x101_32x4d_fpn_dconv_c3-c5_1x_coco.py + Metadata: + Training Memory (GB): 9.2 + Epochs: 12 + Results: + - Task: Object Detection + Dataset: COCO + Metrics: + box AP: 47.3 + - Task: Instance Segmentation + Dataset: COCO + Metrics: + mask AP: 41.1 + Weights: https://download.openmmlab.com/mmdetection/v2.0/dcn/cascade_mask_rcnn_x101_32x4d_fpn_dconv_c3-c5_1x_coco/cascade_mask_rcnn_x101_32x4d_fpn_dconv_c3-c5_1x_coco-e75f90c8.pth diff --git a/configs/deepfashion/README.md b/configs/deepfashion/README.md index c182bea..68e57e4 100644 --- a/configs/deepfashion/README.md +++ b/configs/deepfashion/README.md @@ -1,6 +1,6 @@ # DeepFashion -[DATASET] + [MMFashion](https://github.com/open-mmlab/mmfashion) develops "fashion parsing and segmentation" module based on the dataset diff --git a/configs/deformable_detr/README.md b/configs/deformable_detr/README.md new file mode 100644 index 0000000..fe68002 --- /dev/null +++ b/configs/deformable_detr/README.md @@ -0,0 +1,31 @@ +# Deformable DETR + +## Introduction + + + +We provide the config files for Deformable DETR: [Deformable DETR: Deformable Transformers for End-to-End Object Detection](https://arxiv.org/abs/2010.04159). + +``` +@inproceedings{ +zhu2021deformable, +title={Deformable DETR: Deformable Transformers for End-to-End Object Detection}, +author={Xizhou Zhu and Weijie Su and Lewei Lu and Bin Li and Xiaogang Wang and Jifeng Dai}, +booktitle={International Conference on Learning Representations}, +year={2021}, +url={https://openreview.net/forum?id=gZ9hCDWe6ke} +} +``` + +## Results and Models + +| Backbone | Model | Lr schd | box AP | Config | Download | +|:------:|:--------:|:--------------:|:------:|:------:|:--------:| +| R-50 | Deformable DETR |50e | 44.5 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/deformable_detr/deformable_detr_r50_16x2_50e_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/deformable_detr/deformable_detr_r50_16x2_50e_coco/deformable_detr_r50_16x2_50e_coco_20210419_220030-a12b9512.pth) | [log](https://download.openmmlab.com/mmdetection/v2.0/deformable_detr/deformable_detr_r50_16x2_50e_coco/deformable_detr_r50_16x2_50e_coco_20210419_220030-a12b9512.log.json) | +| R-50 | + iterative bounding box refinement |50e | 46.1 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/deformable_detr/deformable_detr_refine_r50_16x2_50e_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/deformable_detr/deformable_detr_refine_r50_16x2_50e_coco/deformable_detr_refine_r50_16x2_50e_coco_20210419_220503-5f5dff21.pth) | [log](https://download.openmmlab.com/mmdetection/v2.0/deformable_detr/deformable_detr_refine_r50_16x2_50e_coco/deformable_detr_refine_r50_16x2_50e_coco_20210419_220503-5f5dff21.log.json) | +| R-50 | ++ two-stage Deformable DETR |50e | 46.8 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/deformable_detr/deformable_detr_twostage_refine_r50_16x2_50e_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/deformable_detr/deformable_detr_twostage_refine_r50_16x2_50e_coco/deformable_detr_twostage_refine_r50_16x2_50e_coco_20210419_220613-9d28ab72.pth) | [log](https://download.openmmlab.com/mmdetection/v2.0/deformable_detr/deformable_detr_twostage_refine_r50_16x2_50e_coco/deformable_detr_twostage_refine_r50_16x2_50e_coco_20210419_220613-9d28ab72.log.json) | + +# NOTE + +1. All models are trained with batch size 32. +2. The performance is unstable. `Deformable DETR` and `iterative bounding box refinement` may fluctuate about 0.3 mAP. `two-stage Deformable DETR` may fluctuate about 0.2 mAP. diff --git a/configs/deformable_detr/deformable_detr_r50_16x2_50e_coco.py b/configs/deformable_detr/deformable_detr_r50_16x2_50e_coco.py new file mode 100644 index 0000000..0a58d9a --- /dev/null +++ b/configs/deformable_detr/deformable_detr_r50_16x2_50e_coco.py @@ -0,0 +1,172 @@ +_base_ = [ + '../_base_/datasets/coco_detection.py', '../_base_/default_runtime.py' +] +model = dict( + type='DeformableDETR', + backbone=dict( + type='ResNet', + depth=50, + num_stages=4, + out_indices=(1, 2, 3), + frozen_stages=1, + norm_cfg=dict(type='BN', requires_grad=False), + norm_eval=True, + style='pytorch', + init_cfg=dict(type='Pretrained', checkpoint='torchvision://resnet50')), + neck=dict( + type='ChannelMapper', + in_channels=[512, 1024, 2048], + kernel_size=1, + out_channels=256, + act_cfg=None, + norm_cfg=dict(type='GN', num_groups=32), + num_outs=4), + bbox_head=dict( + type='DeformableDETRHead', + num_query=300, + num_classes=80, + in_channels=2048, + sync_cls_avg_factor=True, + as_two_stage=False, + transformer=dict( + type='DeformableDetrTransformer', + encoder=dict( + type='DetrTransformerEncoder', + num_layers=6, + transformerlayers=dict( + type='BaseTransformerLayer', + attn_cfgs=dict( + type='MultiScaleDeformableAttention', embed_dims=256), + feedforward_channels=1024, + ffn_dropout=0.1, + operation_order=('self_attn', 'norm', 'ffn', 'norm'))), + decoder=dict( + type='DeformableDetrTransformerDecoder', + num_layers=6, + return_intermediate=True, + transformerlayers=dict( + type='DetrTransformerDecoderLayer', + attn_cfgs=[ + dict( + type='MultiheadAttention', + embed_dims=256, + num_heads=8, + dropout=0.1), + dict( + type='MultiScaleDeformableAttention', + embed_dims=256) + ], + feedforward_channels=1024, + ffn_dropout=0.1, + operation_order=('self_attn', 'norm', 'cross_attn', 'norm', + 'ffn', 'norm')))), + positional_encoding=dict( + type='SinePositionalEncoding', + num_feats=128, + normalize=True, + offset=-0.5), + loss_cls=dict( + type='FocalLoss', + use_sigmoid=True, + gamma=2.0, + alpha=0.25, + loss_weight=2.0), + loss_bbox=dict(type='L1Loss', loss_weight=5.0), + loss_iou=dict(type='GIoULoss', loss_weight=2.0)), + # training and testing settings + train_cfg=dict( + assigner=dict( + type='HungarianAssigner', + cls_cost=dict(type='FocalLossCost', weight=2.0), + reg_cost=dict(type='BBoxL1Cost', weight=5.0, box_format='xywh'), + iou_cost=dict(type='IoUCost', iou_mode='giou', weight=2.0))), + test_cfg=dict(max_per_img=100)) +img_norm_cfg = dict( + mean=[123.675, 116.28, 103.53], std=[58.395, 57.12, 57.375], to_rgb=True) +# train_pipeline, NOTE the img_scale and the Pad's size_divisor is different +# from the default setting in mmdet. +train_pipeline = [ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations', with_bbox=True), + dict(type='RandomFlip', flip_ratio=0.5), + dict( + type='AutoAugment', + policies=[ + [ + dict( + type='Resize', + img_scale=[(480, 1333), (512, 1333), (544, 1333), + (576, 1333), (608, 1333), (640, 1333), + (672, 1333), (704, 1333), (736, 1333), + (768, 1333), (800, 1333)], + multiscale_mode='value', + keep_ratio=True) + ], + [ + dict( + type='Resize', + # The radio of all image in train dataset < 7 + # follow the original impl + img_scale=[(400, 4200), (500, 4200), (600, 4200)], + multiscale_mode='value', + keep_ratio=True), + dict( + type='RandomCrop', + crop_type='absolute_range', + crop_size=(384, 600), + allow_negative_crop=True), + dict( + type='Resize', + img_scale=[(480, 1333), (512, 1333), (544, 1333), + (576, 1333), (608, 1333), (640, 1333), + (672, 1333), (704, 1333), (736, 1333), + (768, 1333), (800, 1333)], + multiscale_mode='value', + override=True, + keep_ratio=True) + ] + ]), + dict(type='Normalize', **img_norm_cfg), + dict(type='Pad', size_divisor=1), + dict(type='DefaultFormatBundle'), + dict(type='Collect', keys=['img', 'gt_bboxes', 'gt_labels']) +] +# test_pipeline, NOTE the Pad's size_divisor is different from the default +# setting (size_divisor=32). While there is little effect on the performance +# whether we use the default setting or use size_divisor=1. +test_pipeline = [ + dict(type='LoadImageFromFile'), + dict( + type='MultiScaleFlipAug', + img_scale=(1333, 800), + flip=False, + transforms=[ + dict(type='Resize', keep_ratio=True), + dict(type='RandomFlip'), + dict(type='Normalize', **img_norm_cfg), + dict(type='Pad', size_divisor=1), + dict(type='ImageToTensor', keys=['img']), + dict(type='Collect', keys=['img']) + ]) +] +data = dict( + samples_per_gpu=2, + workers_per_gpu=2, + train=dict(filter_empty_gt=False, pipeline=train_pipeline), + val=dict(pipeline=test_pipeline), + test=dict(pipeline=test_pipeline)) +# optimizer +optimizer = dict( + type='AdamW', + lr=2e-4, + weight_decay=0.0001, + paramwise_cfg=dict( + custom_keys={ + 'backbone': dict(lr_mult=0.1), + 'sampling_offsets': dict(lr_mult=0.1), + 'reference_points': dict(lr_mult=0.1) + })) +optimizer_config = dict(grad_clip=dict(max_norm=0.1, norm_type=2)) +# learning policy +lr_config = dict(policy='step', step=[40]) +runner = dict(type='EpochBasedRunner', max_epochs=50) diff --git a/configs/deformable_detr/deformable_detr_refine_r50_16x2_50e_coco.py b/configs/deformable_detr/deformable_detr_refine_r50_16x2_50e_coco.py new file mode 100644 index 0000000..01f13df --- /dev/null +++ b/configs/deformable_detr/deformable_detr_refine_r50_16x2_50e_coco.py @@ -0,0 +1,2 @@ +_base_ = 'deformable_detr_r50_16x2_50e_coco.py' +model = dict(bbox_head=dict(with_box_refine=True)) diff --git a/configs/deformable_detr/deformable_detr_twostage_refine_r50_16x2_50e_coco.py b/configs/deformable_detr/deformable_detr_twostage_refine_r50_16x2_50e_coco.py new file mode 100644 index 0000000..2aa840d --- /dev/null +++ b/configs/deformable_detr/deformable_detr_twostage_refine_r50_16x2_50e_coco.py @@ -0,0 +1,2 @@ +_base_ = 'deformable_detr_refine_r50_16x2_50e_coco.py' +model = dict(bbox_head=dict(as_two_stage=True)) diff --git a/configs/deformable_detr/metafile.yml b/configs/deformable_detr/metafile.yml new file mode 100644 index 0000000..873292d --- /dev/null +++ b/configs/deformable_detr/metafile.yml @@ -0,0 +1,56 @@ +Collections: + - Name: Deformable DETR + Metadata: + Training Data: COCO + Training Techniques: + - AdamW + - Multi Scale Train + - Gradient Clip + Training Resources: 8x V100 GPUs + Architecture: + - ResNet + - Transformer + Paper: + URL: https://openreview.net/forum?id=gZ9hCDWe6ke + Title: 'Deformable DETR: Deformable Transformers for End-to-End Object Detection' + README: configs/deformable_detr/README.md + Code: + URL: https://github.com/open-mmlab/mmdetection/blob/v2.12.0/mmdet/models/detectors/deformable_detr.py#L6 + Version: v2.12.0 + +Models: + - Name: deformable_detr_r50_16x2_50e_coco + In Collection: Deformable DETR + Config: configs/deformable_detr/deformable_detr_r50_16x2_50e_coco.py + Metadata: + Epochs: 50 + Results: + - Task: Object Detection + Dataset: COCO + Metrics: + box AP: 44.5 + Weights: https://download.openmmlab.com/mmdetection/v2.0/deformable_detr/deformable_detr_r50_16x2_50e_coco/deformable_detr_r50_16x2_50e_coco_20210419_220030-a12b9512.pth + + - Name: deformable_detr_refine_r50_16x2_50e_coco + In Collection: Deformable DETR + Config: configs/deformable_detr/deformable_detr_refine_r50_16x2_50e_coco.py + Metadata: + Epochs: 50 + Results: + - Task: Object Detection + Dataset: COCO + Metrics: + box AP: 46.1 + Weights: https://download.openmmlab.com/mmdetection/v2.0/deformable_detr/deformable_detr_refine_r50_16x2_50e_coco/deformable_detr_refine_r50_16x2_50e_coco_20210419_220503-5f5dff21.pth + + - Name: deformable_detr_twostage_refine_r50_16x2_50e_coco + In Collection: Deformable DETR + Config: configs/deformable_detr/deformable_detr_twostage_refine_r50_16x2_50e_coco.py + Metadata: + Epochs: 50 + Results: + - Task: Object Detection + Dataset: COCO + Metrics: + box AP: 46.8 + Weights: https://download.openmmlab.com/mmdetection/v2.0/deformable_detr/deformable_detr_twostage_refine_r50_16x2_50e_coco/deformable_detr_twostage_refine_r50_16x2_50e_coco_20210419_220613-9d28ab72.pth diff --git a/configs/detectors/README.md b/configs/detectors/README.md index 46dee5e..37c151f 100644 --- a/configs/detectors/README.md +++ b/configs/detectors/README.md @@ -2,7 +2,7 @@ ## Introduction -[ALGORITHM] + We provide the config files for [DetectoRS: Detecting Objects with Recursive Feature Pyramid and Switchable Atrous Convolution](https://arxiv.org/pdf/2006.02334.pdf). @@ -47,12 +47,13 @@ The results on COCO 2017 val are shown in the below table. | Method | Detector | Lr schd | Mem (GB) | Inf time (fps) | box AP | mask AP | Config | Download | |:------:|:--------:|:-------:|:--------:|:--------------:|:------:|:-------:|:------:|:--------:| -| RFP | Cascade + ResNet-50 | 1x | 7.5 | - | 44.8 | | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/detectors/cascade_rcnn_r50_rfp_1x_coco.py) | [model](http://download.openmmlab.com/mmdetection/v2.0/detectors/cascade_rcnn_r50_rfp_1x_coco/cascade_rcnn_r50_rfp_1x_coco-8cf51bfd.pth) | [log](http://download.openmmlab.com/mmdetection/v2.0/detectors/cascade_rcnn_r50_rfp_1x_coco/cascade_rcnn_r50_rfp_1x_coco_20200624_104126.log.json) | -| SAC | Cascade + ResNet-50 | 1x | 5.6 | - | 45.0| | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/detectors/cascade_rcnn_r50_sac_1x_coco.py) | [model](http://download.openmmlab.com/mmdetection/v2.0/detectors/cascade_rcnn_r50_sac_1x_coco/cascade_rcnn_r50_sac_1x_coco-24bfda62.pth) | [log](http://download.openmmlab.com/mmdetection/v2.0/detectors/cascade_rcnn_r50_sac_1x_coco/cascade_rcnn_r50_sac_1x_coco_20200624_104402.log.json) | -| DetectoRS | Cascade + ResNet-50 | 1x | 9.9 | - | 47.4 | | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/detectors/detectors_cascade_rcnn_r50_1x_coco.py) | [model](http://download.openmmlab.com/mmdetection/v2.0/detectors/detectors_cascade_rcnn_r50_1x_coco/detectors_cascade_rcnn_r50_1x_coco-32a10ba0.pth) | [log](http://download.openmmlab.com/mmdetection/v2.0/detectors/detectors_cascade_rcnn_r50_1x_coco/detectors_cascade_rcnn_r50_1x_coco_20200706_001203.log.json) | -| RFP | HTC + ResNet-50 | 1x | 11.2 | - | 46.6 | 40.9 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/detectors/htc_r50_rfp_1x_coco.py) | [model](http://download.openmmlab.com/mmdetection/v2.0/detectors/htc_r50_rfp_1x_coco/htc_r50_rfp_1x_coco-8ff87c51.pth) | [log](http://download.openmmlab.com/mmdetection/v2.0/detectors/htc_r50_rfp_1x_coco/htc_r50_rfp_1x_coco_20200624_103053.log.json) | -| SAC | HTC + ResNet-50 | 1x | 9.3 | - | 46.4 | 40.9 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/detectors/htc_r50_sac_1x_coco.py) | [model](http://download.openmmlab.com/mmdetection/v2.0/detectors/htc_r50_sac_1x_coco/htc_r50_sac_1x_coco-bfa60c54.pth) | [log](http://download.openmmlab.com/mmdetection/v2.0/detectors/htc_r50_sac_1x_coco/htc_r50_sac_1x_coco_20200624_103111.log.json) | -| DetectoRS | HTC + ResNet-50 | 1x | 13.6 | - | 49.1 | 42.6 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/detectors/detectors_htc_r50_1x_coco.py) | [model](http://download.openmmlab.com/mmdetection/v2.0/detectors/detectors_htc_r50_1x_coco/detectors_htc_r50_1x_coco-329b1453.pth) | [log](http://download.openmmlab.com/mmdetection/v2.0/detectors/detectors_htc_r50_1x_coco/detectors_htc_r50_1x_coco_20200624_103659.log.json) | +| RFP | Cascade + ResNet-50 | 1x | 7.5 | - | 44.8 | | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/detectors/cascade_rcnn_r50_rfp_1x_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/detectors/cascade_rcnn_r50_rfp_1x_coco/cascade_rcnn_r50_rfp_1x_coco-8cf51bfd.pth) | [log](https://download.openmmlab.com/mmdetection/v2.0/detectors/cascade_rcnn_r50_rfp_1x_coco/cascade_rcnn_r50_rfp_1x_coco_20200624_104126.log.json) | +| SAC | Cascade + ResNet-50 | 1x | 5.6 | - | 45.0| | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/detectors/cascade_rcnn_r50_sac_1x_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/detectors/cascade_rcnn_r50_sac_1x_coco/cascade_rcnn_r50_sac_1x_coco-24bfda62.pth) | [log](https://download.openmmlab.com/mmdetection/v2.0/detectors/cascade_rcnn_r50_sac_1x_coco/cascade_rcnn_r50_sac_1x_coco_20200624_104402.log.json) | +| DetectoRS | Cascade + ResNet-50 | 1x | 9.9 | - | 47.4 | | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/detectors/detectors_cascade_rcnn_r50_1x_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/detectors/detectors_cascade_rcnn_r50_1x_coco/detectors_cascade_rcnn_r50_1x_coco-32a10ba0.pth) | [log](https://download.openmmlab.com/mmdetection/v2.0/detectors/detectors_cascade_rcnn_r50_1x_coco/detectors_cascade_rcnn_r50_1x_coco_20200706_001203.log.json) | +| RFP | HTC + ResNet-50 | 1x | 11.2 | - | 46.6 | 40.9 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/detectors/htc_r50_rfp_1x_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/detectors/htc_r50_rfp_1x_coco/htc_r50_rfp_1x_coco-8ff87c51.pth) | [log](https://download.openmmlab.com/mmdetection/v2.0/detectors/htc_r50_rfp_1x_coco/htc_r50_rfp_1x_coco_20200624_103053.log.json) | +| SAC | HTC + ResNet-50 | 1x | 9.3 | - | 46.4 | 40.9 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/detectors/htc_r50_sac_1x_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/detectors/htc_r50_sac_1x_coco/htc_r50_sac_1x_coco-bfa60c54.pth) | [log](https://download.openmmlab.com/mmdetection/v2.0/detectors/htc_r50_sac_1x_coco/htc_r50_sac_1x_coco_20200624_103111.log.json) | +| DetectoRS | HTC + ResNet-50 | 1x | 13.6 | - | 49.1 | 42.6 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/detectors/detectors_htc_r50_1x_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/detectors/detectors_htc_r50_1x_coco/detectors_htc_r50_1x_coco-329b1453.pth) | [log](https://download.openmmlab.com/mmdetection/v2.0/detectors/detectors_htc_r50_1x_coco/detectors_htc_r50_1x_coco_20200624_103659.log.json) | +| DetectoRS | HTC + ResNet-101 | 20e | 19.6 | | 50.5 | 43.9 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/detectors/detectors_htc_r101_20e_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/detectors/detectors_htc_r101_20e_coco/detectors_htc_r101_20e_coco_20210419_203638-348d533b.pth) | [log](https://download.openmmlab.com/mmdetection/v2.0/detectors/detectors_htc_r101_20e_coco/detectors_htc_r101_20e_coco_20210419_203638.log.json) | *Note*: This is a re-implementation based on MMDetection-V2. The original implementation is based on MMDetection-V1. diff --git a/configs/detectors/detectors_htc_r101_20e_coco.py b/configs/detectors/detectors_htc_r101_20e_coco.py new file mode 100644 index 0000000..93d7d2b --- /dev/null +++ b/configs/detectors/detectors_htc_r101_20e_coco.py @@ -0,0 +1,28 @@ +_base_ = '../htc/htc_r101_fpn_20e_coco.py' + +model = dict( + backbone=dict( + type='DetectoRS_ResNet', + conv_cfg=dict(type='ConvAWS'), + sac=dict(type='SAC', use_deform=True), + stage_with_sac=(False, True, True, True), + output_img=True), + neck=dict( + type='RFP', + rfp_steps=2, + aspp_out_channels=64, + aspp_dilations=(1, 3, 6, 1), + rfp_backbone=dict( + rfp_inplanes=256, + type='DetectoRS_ResNet', + depth=101, + num_stages=4, + out_indices=(0, 1, 2, 3), + frozen_stages=1, + norm_cfg=dict(type='BN', requires_grad=True), + norm_eval=True, + conv_cfg=dict(type='ConvAWS'), + sac=dict(type='SAC', use_deform=True), + stage_with_sac=(False, True, True, True), + pretrained='torchvision://resnet101', + style='pytorch'))) diff --git a/configs/detectors/metafile.yml b/configs/detectors/metafile.yml new file mode 100644 index 0000000..4bed569 --- /dev/null +++ b/configs/detectors/metafile.yml @@ -0,0 +1,114 @@ +Collections: + - Name: DetectoRS + Metadata: + Training Data: COCO + Training Techniques: + - SGD with Momentum + - Weight Decay + Training Resources: 8x V100 GPUs + Architecture: + - ASPP + - FPN + - RFP + - RPN + - ResNet + - RoIAlign + - SAC + Paper: + URL: https://arxiv.org/abs/2006.02334 + Title: 'DetectoRS: Detecting Objects with Recursive Feature Pyramid and Switchable Atrous Convolution' + README: configs/detectors/README.md + Code: + URL: https://github.com/open-mmlab/mmdetection/blob/v2.2.0/mmdet/models/backbones/detectors_resnet.py#L205 + Version: v2.2.0 + +Models: + - Name: cascade_rcnn_r50_rfp_1x_coco + In Collection: DetectoRS + Config: configs/detectors/cascade_rcnn_r50_rfp_1x_coco.py + Metadata: + Training Memory (GB): 7.5 + Epochs: 12 + Results: + - Task: Object Detection + Dataset: COCO + Metrics: + box AP: 44.8 + Weights: https://download.openmmlab.com/mmdetection/v2.0/detectors/cascade_rcnn_r50_rfp_1x_coco/cascade_rcnn_r50_rfp_1x_coco-8cf51bfd.pth + + - Name: cascade_rcnn_r50_sac_1x_coco + In Collection: DetectoRS + Config: configs/detectors/cascade_rcnn_r50_sac_1x_coco.py + Metadata: + Training Memory (GB): 5.6 + Epochs: 12 + Results: + - Task: Object Detection + Dataset: COCO + Metrics: + box AP: 45.0 + Weights: https://download.openmmlab.com/mmdetection/v2.0/detectors/cascade_rcnn_r50_sac_1x_coco/cascade_rcnn_r50_sac_1x_coco-24bfda62.pth + + - Name: detectors_cascade_rcnn_r50_1x_coco + In Collection: DetectoRS + Config: configs/detectors/detectors_cascade_rcnn_r50_1x_coco.py + Metadata: + Training Memory (GB): 9.9 + Epochs: 12 + Results: + - Task: Object Detection + Dataset: COCO + Metrics: + box AP: 47.4 + Weights: https://download.openmmlab.com/mmdetection/v2.0/detectors/detectors_cascade_rcnn_r50_1x_coco/detectors_cascade_rcnn_r50_1x_coco-32a10ba0.pth + + - Name: htc_r50_rfp_1x_coco + In Collection: DetectoRS + Config: configs/detectors/htc_r50_rfp_1x_coco.py + Metadata: + Training Memory (GB): 11.2 + Epochs: 12 + Results: + - Task: Object Detection + Dataset: COCO + Metrics: + box AP: 46.6 + - Task: Instance Segmentation + Dataset: COCO + Metrics: + mask AP: 40.9 + Weights: https://download.openmmlab.com/mmdetection/v2.0/detectors/htc_r50_rfp_1x_coco/htc_r50_rfp_1x_coco-8ff87c51.pth + + - Name: htc_r50_sac_1x_coco + In Collection: DetectoRS + Config: configs/detectors/htc_r50_sac_1x_coco.py + Metadata: + Training Memory (GB): 9.3 + Epochs: 12 + Results: + - Task: Object Detection + Dataset: COCO + Metrics: + box AP: 46.4 + - Task: Instance Segmentation + Dataset: COCO + Metrics: + mask AP: 40.9 + Weights: https://download.openmmlab.com/mmdetection/v2.0/detectors/htc_r50_sac_1x_coco/htc_r50_sac_1x_coco-bfa60c54.pth + + - Name: detectors_htc_r50_1x_coco + In Collection: DetectoRS + Config: configs/detectors/detectors_htc_r50_1x_coco.py + Metadata: + Training Memory (GB): 13.6 + Epochs: 12 + Results: + - Task: Object Detection + Dataset: COCO + Metrics: + box AP: 49.1 + - Task: Instance Segmentation + Dataset: COCO + Metrics: + mask AP: 42.6 + Weights: https://download.openmmlab.com/mmdetection/v2.0/detectors/detectors_htc_r50_1x_coco/detectors_htc_r50_1x_coco-329b1453.pth diff --git a/configs/detr/README.md b/configs/detr/README.md index 711a308..617a8fb 100644 --- a/configs/detr/README.md +++ b/configs/detr/README.md @@ -2,7 +2,7 @@ ## Introduction -[ALGORITHM] + We provide the config files for DETR: [End-to-End Object Detection with Transformers](https://arxiv.org/abs/2005.12872). @@ -24,4 +24,4 @@ We provide the config files for DETR: [End-to-End Object Detection with Transfor | Backbone | Model | Lr schd | Mem (GB) | Inf time (fps) | box AP | Config | Download | |:------:|:--------:|:-------:|:--------:|:--------------:|:------:|:------:|:--------:| -| R-50 | DETR |150e |7.9| | 40.1 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/detr/detr_r50_8x2_150e_coco.py) | [model](http://download.openmmlab.com/mmdetection/v2.0/detr/detr_r50_8x2_150e_coco/detr_r50_8x2_150e_coco_20201130_194835-2c4b8974.pth) | [log](http://download.openmmlab.com/mmdetection/v2.0/detr/detr_r50_8x2_150e_coco/detr_r50_8x2_150e_coco_20201130_194835.log.json) | +| R-50 | DETR |150e |7.9| | 40.1 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/detr/detr_r50_8x2_150e_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/detr/detr_r50_8x2_150e_coco/detr_r50_8x2_150e_coco_20201130_194835-2c4b8974.pth) | [log](https://download.openmmlab.com/mmdetection/v2.0/detr/detr_r50_8x2_150e_coco/detr_r50_8x2_150e_coco_20201130_194835.log.json) | diff --git a/configs/detr/detr_r50_8x2_150e_coco.py b/configs/detr/detr_r50_8x2_150e_coco.py index ba276f4..892447d 100644 --- a/configs/detr/detr_r50_8x2_150e_coco.py +++ b/configs/detr/detr_r50_8x2_150e_coco.py @@ -3,7 +3,6 @@ ] model = dict( type='DETR', - pretrained='torchvision://resnet50', backbone=dict( type='ResNet', depth=50, @@ -12,25 +11,45 @@ frozen_stages=1, norm_cfg=dict(type='BN', requires_grad=False), norm_eval=True, - style='pytorch'), + style='pytorch', + init_cfg=dict(type='Pretrained', checkpoint='torchvision://resnet50')), bbox_head=dict( - type='TransformerHead', + type='DETRHead', num_classes=80, in_channels=2048, - num_fcs=2, transformer=dict( type='Transformer', - embed_dims=256, - num_heads=8, - num_encoder_layers=6, - num_decoder_layers=6, - feedforward_channels=2048, - dropout=0.1, - act_cfg=dict(type='ReLU', inplace=True), - norm_cfg=dict(type='LN'), - num_fcs=2, - pre_norm=False, - return_intermediate_dec=True), + encoder=dict( + type='DetrTransformerEncoder', + num_layers=6, + transformerlayers=dict( + type='BaseTransformerLayer', + attn_cfgs=[ + dict( + type='MultiheadAttention', + embed_dims=256, + num_heads=8, + dropout=0.1) + ], + feedforward_channels=2048, + ffn_dropout=0.1, + operation_order=('self_attn', 'norm', 'ffn', 'norm'))), + decoder=dict( + type='DetrTransformerDecoder', + return_intermediate=True, + num_layers=6, + transformerlayers=dict( + type='DetrTransformerDecoderLayer', + attn_cfgs=dict( + type='MultiheadAttention', + embed_dims=256, + num_heads=8, + dropout=0.1), + feedforward_channels=2048, + ffn_dropout=0.1, + operation_order=('self_attn', 'norm', 'cross_attn', 'norm', + 'ffn', 'norm')), + )), positional_encoding=dict( type='SinePositionalEncoding', num_feats=128, normalize=True), loss_cls=dict( @@ -46,7 +65,7 @@ assigner=dict( type='HungarianAssigner', cls_cost=dict(type='ClassificationCost', weight=1.), - reg_cost=dict(type='BBoxL1Cost', weight=5.0), + reg_cost=dict(type='BBoxL1Cost', weight=5.0, box_format='xywh'), iou_cost=dict(type='IoUCost', iou_mode='giou', weight=2.0))), test_cfg=dict(max_per_img=100)) img_norm_cfg = dict( diff --git a/configs/detr/metafile.yml b/configs/detr/metafile.yml new file mode 100644 index 0000000..45622cf --- /dev/null +++ b/configs/detr/metafile.yml @@ -0,0 +1,33 @@ +Collections: + - Name: DETR + Metadata: + Training Data: COCO + Training Techniques: + - AdamW + - Multi Scale Train + - Gradient Clip + Training Resources: 8x V100 GPUs + Architecture: + - ResNet + - Transformer + Paper: + URL: https://arxiv.org/abs/2005.12872 + Title: 'End-to-End Object Detection with Transformers' + README: configs/detr/README.md + Code: + URL: https://github.com/open-mmlab/mmdetection/blob/v2.7.0/mmdet/models/detectors/detr.py#L7 + Version: v2.7.0 + +Models: + - Name: detr_r50_8x2_150e_coco + In Collection: DETR + Config: configs/detr/detr_r50_8x2_150e_coco.py + Metadata: + Training Memory (GB): 7.9 + Epochs: 150 + Results: + - Task: Object Detection + Dataset: COCO + Metrics: + box AP: 40.1 + Weights: https://download.openmmlab.com/mmdetection/v2.0/detr/detr_r50_8x2_150e_coco/detr_r50_8x2_150e_coco_20201130_194835-2c4b8974.pth diff --git a/configs/double_heads/README.md b/configs/double_heads/README.md index 3ad4f49..872211d 100644 --- a/configs/double_heads/README.md +++ b/configs/double_heads/README.md @@ -2,7 +2,7 @@ ## Introduction -[ALGORITHM] + ```latex @article{wu2019rethinking, @@ -19,4 +19,4 @@ | Backbone | Style | Lr schd | Mem (GB) | Inf time (fps) | box AP | Config | Download | | :-------------: | :-----: | :-----: | :------: | :------------: | :----: | :------: | :--------: | -| R-50-FPN | pytorch | 1x | 6.8 | 9.5 | 40.0 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/double_heads/dh_faster_rcnn_r50_fpn_1x_coco.py) | [model](http://download.openmmlab.com/mmdetection/v2.0/double_heads/dh_faster_rcnn_r50_fpn_1x_coco/dh_faster_rcnn_r50_fpn_1x_coco_20200130-586b67df.pth) | [log](http://download.openmmlab.com/mmdetection/v2.0/double_heads/dh_faster_rcnn_r50_fpn_1x_coco/dh_faster_rcnn_r50_fpn_1x_coco_20200130_220238.log.json) | +| R-50-FPN | pytorch | 1x | 6.8 | 9.5 | 40.0 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/double_heads/dh_faster_rcnn_r50_fpn_1x_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/double_heads/dh_faster_rcnn_r50_fpn_1x_coco/dh_faster_rcnn_r50_fpn_1x_coco_20200130-586b67df.pth) | [log](https://download.openmmlab.com/mmdetection/v2.0/double_heads/dh_faster_rcnn_r50_fpn_1x_coco/dh_faster_rcnn_r50_fpn_1x_coco_20200130_220238.log.json) | diff --git a/configs/double_heads/metafile.yml b/configs/double_heads/metafile.yml new file mode 100644 index 0000000..6fe9b7a --- /dev/null +++ b/configs/double_heads/metafile.yml @@ -0,0 +1,41 @@ +Collections: + - Name: Rethinking Classification and Localization for Object Detection + Metadata: + Training Data: COCO + Training Techniques: + - SGD with Momentum + - Weight Decay + Training Resources: 8x V100 GPUs + Architecture: + - FPN + - RPN + - ResNet + - RoIAlign + Paper: + URL: https://arxiv.org/pdf/1904.06493 + Title: 'Rethinking Classification and Localization for Object Detection' + README: configs/double_heads/README.md + Code: + URL: https://github.com/open-mmlab/mmdetection/blob/v2.0.0/mmdet/models/roi_heads/double_roi_head.py#L6 + Version: v2.0.0 + +Models: + - Name: dh_faster_rcnn_r50_fpn_1x_coco + In Collection: Rethinking Classification and Localization for Object Detection + Config: configs/double_heads/dh_faster_rcnn_r50_fpn_1x_coco.py + Metadata: + Training Memory (GB): 6.8 + inference time (ms/im): + - value: 105.26 + hardware: V100 + backend: PyTorch + batch size: 1 + mode: FP32 + resolution: (800, 1333) + Epochs: 12 + Results: + - Task: Object Detection + Dataset: COCO + Metrics: + box AP: 40.0 + Weights: https://download.openmmlab.com/mmdetection/v2.0/double_heads/dh_faster_rcnn_r50_fpn_1x_coco/dh_faster_rcnn_r50_fpn_1x_coco_20200130-586b67df.pth diff --git a/configs/dynamic_rcnn/README.md b/configs/dynamic_rcnn/README.md index ffdc42d..2b087c3 100644 --- a/configs/dynamic_rcnn/README.md +++ b/configs/dynamic_rcnn/README.md @@ -2,7 +2,7 @@ ## Introduction -[ALGORITHM] + ``` @article{DynamicRCNN, @@ -17,4 +17,4 @@ | Backbone | Style | Lr schd | Mem (GB) | Inf time (fps) | box AP | Config | Download | |:---------:|:-------:|:-------:|:--------:|:--------------:|:------:|:------:|:--------:| -| R-50 | pytorch | 1x | 3.8 | | 38.9 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/dynamic_rcnn/dynamic_rcnn_r50_fpn_1x.py) | [model](http://download.openmmlab.com/mmdetection/v2.0/dynamic_rcnn/dynamic_rcnn_r50_fpn_1x/dynamic_rcnn_r50_fpn_1x-62a3f276.pth) | [log](http://download.openmmlab.com/mmdetection/v2.0/dynamic_rcnn/dynamic_rcnn_r50_fpn_1x/dynamic_rcnn_r50_fpn_1x_20200618_095048.log.json) | +| R-50 | pytorch | 1x | 3.8 | | 38.9 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/dynamic_rcnn/dynamic_rcnn_r50_fpn_1x_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/dynamic_rcnn/dynamic_rcnn_r50_fpn_1x/dynamic_rcnn_r50_fpn_1x-62a3f276.pth) | [log](https://download.openmmlab.com/mmdetection/v2.0/dynamic_rcnn/dynamic_rcnn_r50_fpn_1x/dynamic_rcnn_r50_fpn_1x_20200618_095048.log.json) | diff --git a/configs/dynamic_rcnn/dynamic_rcnn_r50_fpn_1x.py b/configs/dynamic_rcnn/dynamic_rcnn_r50_fpn_1x_coco.py similarity index 100% rename from configs/dynamic_rcnn/dynamic_rcnn_r50_fpn_1x.py rename to configs/dynamic_rcnn/dynamic_rcnn_r50_fpn_1x_coco.py diff --git a/configs/dynamic_rcnn/metafile.yml b/configs/dynamic_rcnn/metafile.yml new file mode 100644 index 0000000..fec43db --- /dev/null +++ b/configs/dynamic_rcnn/metafile.yml @@ -0,0 +1,35 @@ +Collections: + - Name: Dynamic R-CNN + Metadata: + Training Data: COCO + Training Techniques: + - SGD with Momentum + - Weight Decay + Training Resources: 8x V100 GPUs + Architecture: + - Dynamic R-CNN + - FPN + - RPN + - ResNet + - RoIAlign + Paper: + URL: https://arxiv.org/pdf/2004.06002 + Title: 'Dynamic R-CNN: Towards High Quality Object Detection via Dynamic Training' + README: configs/dynamic_rcnn/README.md + Code: + URL: https://github.com/open-mmlab/mmdetection/blob/v2.2.0/mmdet/models/roi_heads/dynamic_roi_head.py#L11 + Version: v2.2.0 + +Models: + - Name: dynamic_rcnn_r50_fpn_1x_coco + In Collection: Dynamic R-CNN + Config: configs/dynamic_rcnn/dynamic_rcnn_r50_fpn_1x_coco.py + Metadata: + Training Memory (GB): 3.8 + Epochs: 12 + Results: + - Task: Object Detection + Dataset: COCO + Metrics: + box AP: 38.9 + Weights: https://download.openmmlab.com/mmdetection/v2.0/dynamic_rcnn/dynamic_rcnn_r50_fpn_1x/dynamic_rcnn_r50_fpn_1x-62a3f276.pth diff --git a/configs/empirical_attention/README.md b/configs/empirical_attention/README.md index f9782d7..f8647bd 100644 --- a/configs/empirical_attention/README.md +++ b/configs/empirical_attention/README.md @@ -2,7 +2,7 @@ ## Introduction -[ALGORITHM] + ```latex @article{zhu2019empirical, @@ -17,7 +17,7 @@ | Backbone | Attention Component | DCN | Lr schd | Mem (GB) | Inf time (fps) | box AP | Config | Download | |:---------:|:-------------------:|:----:|:-------:|:--------:|:--------------:|:------:|:------:|:--------:| -| R-50 | 1111 | N | 1x | 8.0 | 13.8 | 40.0 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/empirical_attention/faster_rcnn_r50_fpn_attention_1111_1x_coco.py) | [model](http://download.openmmlab.com/mmdetection/v2.0/empirical_attention/faster_rcnn_r50_fpn_attention_1111_1x_coco/faster_rcnn_r50_fpn_attention_1111_1x_coco_20200130-403cccba.pth) | [log](http://download.openmmlab.com/mmdetection/v2.0/empirical_attention/faster_rcnn_r50_fpn_attention_1111_1x_coco/faster_rcnn_r50_fpn_attention_1111_1x_coco_20200130_210344.log.json) | -| R-50 | 0010 | N | 1x | 4.2 | 18.4 | 39.1 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/empirical_attention/faster_rcnn_r50_fpn_attention_0010_1x_coco.py) | [model](http://download.openmmlab.com/mmdetection/v2.0/empirical_attention/faster_rcnn_r50_fpn_attention_0010_1x_coco/faster_rcnn_r50_fpn_attention_0010_1x_coco_20200130-7cb0c14d.pth) | [log](http://download.openmmlab.com/mmdetection/v2.0/empirical_attention/faster_rcnn_r50_fpn_attention_0010_1x_coco/faster_rcnn_r50_fpn_attention_0010_1x_coco_20200130_210125.log.json) | -| R-50 | 1111 | Y | 1x | 8.0 | 12.7 | 42.1 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/empirical_attention/faster_rcnn_r50_fpn_attention_1111_dcn_1x_coco.py) | [model](http://download.openmmlab.com/mmdetection/v2.0/empirical_attention/faster_rcnn_r50_fpn_attention_1111_dcn_1x_coco/faster_rcnn_r50_fpn_attention_1111_dcn_1x_coco_20200130-8b2523a6.pth) | [log](http://download.openmmlab.com/mmdetection/v2.0/empirical_attention/faster_rcnn_r50_fpn_attention_1111_dcn_1x_coco/faster_rcnn_r50_fpn_attention_1111_dcn_1x_coco_20200130_204442.log.json) | -| R-50 | 0010 | Y | 1x | 4.2 | 17.1 | 42.0 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/empirical_attention/faster_rcnn_r50_fpn_attention_0010_dcn_1x_coco.py) | [model](http://download.openmmlab.com/mmdetection/v2.0/empirical_attention/faster_rcnn_r50_fpn_attention_0010_dcn_1x_coco/faster_rcnn_r50_fpn_attention_0010_dcn_1x_coco_20200130-1a2e831d.pth) | [log](http://download.openmmlab.com/mmdetection/v2.0/empirical_attention/faster_rcnn_r50_fpn_attention_0010_dcn_1x_coco/faster_rcnn_r50_fpn_attention_0010_dcn_1x_coco_20200130_210410.log.json) | +| R-50 | 1111 | N | 1x | 8.0 | 13.8 | 40.0 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/empirical_attention/faster_rcnn_r50_fpn_attention_1111_1x_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/empirical_attention/faster_rcnn_r50_fpn_attention_1111_1x_coco/faster_rcnn_r50_fpn_attention_1111_1x_coco_20200130-403cccba.pth) | [log](https://download.openmmlab.com/mmdetection/v2.0/empirical_attention/faster_rcnn_r50_fpn_attention_1111_1x_coco/faster_rcnn_r50_fpn_attention_1111_1x_coco_20200130_210344.log.json) | +| R-50 | 0010 | N | 1x | 4.2 | 18.4 | 39.1 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/empirical_attention/faster_rcnn_r50_fpn_attention_0010_1x_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/empirical_attention/faster_rcnn_r50_fpn_attention_0010_1x_coco/faster_rcnn_r50_fpn_attention_0010_1x_coco_20200130-7cb0c14d.pth) | [log](https://download.openmmlab.com/mmdetection/v2.0/empirical_attention/faster_rcnn_r50_fpn_attention_0010_1x_coco/faster_rcnn_r50_fpn_attention_0010_1x_coco_20200130_210125.log.json) | +| R-50 | 1111 | Y | 1x | 8.0 | 12.7 | 42.1 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/empirical_attention/faster_rcnn_r50_fpn_attention_1111_dcn_1x_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/empirical_attention/faster_rcnn_r50_fpn_attention_1111_dcn_1x_coco/faster_rcnn_r50_fpn_attention_1111_dcn_1x_coco_20200130-8b2523a6.pth) | [log](https://download.openmmlab.com/mmdetection/v2.0/empirical_attention/faster_rcnn_r50_fpn_attention_1111_dcn_1x_coco/faster_rcnn_r50_fpn_attention_1111_dcn_1x_coco_20200130_204442.log.json) | +| R-50 | 0010 | Y | 1x | 4.2 | 17.1 | 42.0 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/empirical_attention/faster_rcnn_r50_fpn_attention_0010_dcn_1x_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/empirical_attention/faster_rcnn_r50_fpn_attention_0010_dcn_1x_coco/faster_rcnn_r50_fpn_attention_0010_dcn_1x_coco_20200130-1a2e831d.pth) | [log](https://download.openmmlab.com/mmdetection/v2.0/empirical_attention/faster_rcnn_r50_fpn_attention_0010_dcn_1x_coco/faster_rcnn_r50_fpn_attention_0010_dcn_1x_coco_20200130_210410.log.json) | diff --git a/configs/empirical_attention/metafile.yml b/configs/empirical_attention/metafile.yml new file mode 100644 index 0000000..923bcb2 --- /dev/null +++ b/configs/empirical_attention/metafile.yml @@ -0,0 +1,103 @@ +Collections: + - Name: Empirical Attention + Metadata: + Training Data: COCO + Training Techniques: + - SGD with Momentum + - Weight Decay + Training Resources: 8x V100 GPUs + Architecture: + - Deformable Convolution + - FPN + - RPN + - ResNet + - RoIAlign + - Spatial Attention + Paper: + URL: https://arxiv.org/pdf/1904.05873 + Title: 'An Empirical Study of Spatial Attention Mechanisms in Deep Networks' + README: configs/empirical_attention/README.md + Code: + URL: https://github.com/open-mmlab/mmdetection/blob/v2.0.0/mmdet/ops/generalized_attention.py#L10 + Version: v2.0.0 + +Models: + - Name: faster_rcnn_r50_fpn_attention_1111_1x_coco + In Collection: Empirical Attention + Config: configs/empirical_attention/faster_rcnn_r50_fpn_attention_1111_1x_coco.py + Metadata: + Training Memory (GB): 8.0 + inference time (ms/im): + - value: 72.46 + hardware: V100 + backend: PyTorch + batch size: 1 + mode: FP32 + resolution: (800, 1333) + Epochs: 12 + Results: + - Task: Object Detection + Dataset: COCO + Metrics: + box AP: 40.0 + Weights: https://download.openmmlab.com/mmdetection/v2.0/empirical_attention/faster_rcnn_r50_fpn_attention_1111_1x_coco/faster_rcnn_r50_fpn_attention_1111_1x_coco_20200130-403cccba.pth + + - Name: faster_rcnn_r50_fpn_attention_0010_1x_coco + In Collection: Empirical Attention + Config: configs/empirical_attention/faster_rcnn_r50_fpn_attention_0010_1x_coco.py + Metadata: + Training Memory (GB): 4.2 + inference time (ms/im): + - value: 54.35 + hardware: V100 + backend: PyTorch + batch size: 1 + mode: FP32 + resolution: (800, 1333) + Epochs: 12 + Results: + - Task: Object Detection + Dataset: COCO + Metrics: + box AP: 39.1 + Weights: https://download.openmmlab.com/mmdetection/v2.0/empirical_attention/faster_rcnn_r50_fpn_attention_0010_1x_coco/faster_rcnn_r50_fpn_attention_0010_1x_coco_20200130-7cb0c14d.pth + + - Name: faster_rcnn_r50_fpn_attention_1111_dcn_1x_coco + In Collection: Empirical Attention + Config: configs/empirical_attention/faster_rcnn_r50_fpn_attention_1111_dcn_1x_coco.py + Metadata: + Training Memory (GB): 8.0 + inference time (ms/im): + - value: 78.74 + hardware: V100 + backend: PyTorch + batch size: 1 + mode: FP32 + resolution: (800, 1333) + Epochs: 12 + Results: + - Task: Object Detection + Dataset: COCO + Metrics: + box AP: 42.1 + Weights: https://download.openmmlab.com/mmdetection/v2.0/empirical_attention/faster_rcnn_r50_fpn_attention_1111_dcn_1x_coco/faster_rcnn_r50_fpn_attention_1111_dcn_1x_coco_20200130-8b2523a6.pth + + - Name: faster_rcnn_r50_fpn_attention_0010_dcn_1x_coco + In Collection: Empirical Attention + Config: configs/empirical_attention/faster_rcnn_r50_fpn_attention_0010_dcn_1x_coco.py + Metadata: + Training Memory (GB): 4.2 + inference time (ms/im): + - value: 58.48 + hardware: V100 + backend: PyTorch + batch size: 1 + mode: FP32 + resolution: (800, 1333) + Epochs: 12 + Results: + - Task: Object Detection + Dataset: COCO + Metrics: + box AP: 42.0 + Weights: https://download.openmmlab.com/mmdetection/v2.0/empirical_attention/faster_rcnn_r50_fpn_attention_0010_dcn_1x_coco/faster_rcnn_r50_fpn_attention_0010_dcn_1x_coco_20200130-1a2e831d.pth diff --git a/configs/fast_rcnn/README.md b/configs/fast_rcnn/README.md index c756507..3be862a 100644 --- a/configs/fast_rcnn/README.md +++ b/configs/fast_rcnn/README.md @@ -2,7 +2,7 @@ ## Introduction -[ALGORITHM] + ```latex @inproceedings{girshick2015fast, diff --git a/configs/fast_rcnn/fast_rcnn_r101_caffe_fpn_1x_coco.py b/configs/fast_rcnn/fast_rcnn_r101_caffe_fpn_1x_coco.py index 6db24b1..3ab8e98 100644 --- a/configs/fast_rcnn/fast_rcnn_r101_caffe_fpn_1x_coco.py +++ b/configs/fast_rcnn/fast_rcnn_r101_caffe_fpn_1x_coco.py @@ -1,4 +1,7 @@ _base_ = './fast_rcnn_r50_caffe_fpn_1x_coco.py' model = dict( - pretrained='open-mmlab://detectron2/resnet101_caffe', - backbone=dict(depth=101)) + backbone=dict( + depth=101, + init_cfg=dict( + type='Pretrained', + checkpoint='open-mmlab://detectron2/resnet101_caffe'))) diff --git a/configs/fast_rcnn/fast_rcnn_r101_fpn_1x_coco.py b/configs/fast_rcnn/fast_rcnn_r101_fpn_1x_coco.py index 9a76b39..83852b2 100644 --- a/configs/fast_rcnn/fast_rcnn_r101_fpn_1x_coco.py +++ b/configs/fast_rcnn/fast_rcnn_r101_fpn_1x_coco.py @@ -1,2 +1,6 @@ _base_ = './fast_rcnn_r50_fpn_1x_coco.py' -model = dict(pretrained='torchvision://resnet101', backbone=dict(depth=101)) +model = dict( + backbone=dict( + depth=101, + init_cfg=dict(type='Pretrained', + checkpoint='torchvision://resnet101'))) diff --git a/configs/fast_rcnn/fast_rcnn_r101_fpn_2x_coco.py b/configs/fast_rcnn/fast_rcnn_r101_fpn_2x_coco.py index c9d5b4b..c220885 100644 --- a/configs/fast_rcnn/fast_rcnn_r101_fpn_2x_coco.py +++ b/configs/fast_rcnn/fast_rcnn_r101_fpn_2x_coco.py @@ -1,2 +1,6 @@ _base_ = './fast_rcnn_r50_fpn_2x_coco.py' -model = dict(pretrained='torchvision://resnet101', backbone=dict(depth=101)) +model = dict( + backbone=dict( + depth=101, + init_cfg=dict(type='Pretrained', + checkpoint='torchvision://resnet101'))) diff --git a/configs/fast_rcnn/fast_rcnn_r50_caffe_fpn_1x_coco.py b/configs/fast_rcnn/fast_rcnn_r50_caffe_fpn_1x_coco.py index 178deb6..f1b29ef 100644 --- a/configs/fast_rcnn/fast_rcnn_r50_caffe_fpn_1x_coco.py +++ b/configs/fast_rcnn/fast_rcnn_r50_caffe_fpn_1x_coco.py @@ -1,9 +1,12 @@ _base_ = './fast_rcnn_r50_fpn_1x_coco.py' model = dict( - pretrained='open-mmlab://detectron2/resnet50_caffe', backbone=dict( - norm_cfg=dict(type='BN', requires_grad=False), style='caffe')) + norm_cfg=dict(type='BN', requires_grad=False), + style='caffe', + init_cfg=dict( + type='Pretrained', + checkpoint='open-mmlab://detectron2/resnet50_caffe'))) # use caffe img_norm img_norm_cfg = dict( diff --git a/configs/faster_rcnn/README.md b/configs/faster_rcnn/README.md index d43fc6d..772a6c4 100644 --- a/configs/faster_rcnn/README.md +++ b/configs/faster_rcnn/README.md @@ -2,7 +2,7 @@ ## Introduction -[ALGORITHM] + ```latex @article{Ren_2017, @@ -20,16 +20,16 @@ | Backbone | Style | Lr schd | Mem (GB) | Inf time (fps) | box AP | Config | Download | | :-------------: | :-----: | :-----: | :------: | :------------: | :----: | :------: | :--------: | | R-50-DC5 | caffe | 1x | - | - | 37.2 | [config](https://github.com/open-mmlab/mmdetection/blob/master/configs/faster_rcnn/faster_rcnn_r50_caffe_dc5_1x_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/faster_rcnn/faster_rcnn_r50_caffe_dc5_1x_coco/faster_rcnn_r50_caffe_dc5_1x_coco_20201030_151909-531f0f43.pth) | [log](https://download.openmmlab.com/mmdetection/v2.0/faster_rcnn/faster_rcnn_r50_caffe_dc5_1x_coco/faster_rcnn_r50_caffe_dc5_1x_coco_20201030_151909.log.json) | -| R-50-FPN | caffe | 1x | 3.8 | | 37.8 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/faster_rcnn/faster_rcnn_r50_caffe_fpn_1x_coco.py) | [model](http://download.openmmlab.com/mmdetection/v2.0/faster_rcnn/faster_rcnn_r50_caffe_fpn_1x_coco/faster_rcnn_r50_caffe_fpn_1x_coco_bbox_mAP-0.378_20200504_180032-c5925ee5.pth) | [log](http://download.openmmlab.com/mmdetection/v2.0/faster_rcnn/faster_rcnn_r50_caffe_fpn_1x_coco/faster_rcnn_r50_caffe_fpn_1x_coco_20200504_180032.log.json) | -| R-50-FPN | pytorch | 1x | 4.0 | 21.4 | 37.4 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/faster_rcnn/faster_rcnn_r50_fpn_1x_coco.py) | [model](http://download.openmmlab.com/mmdetection/v2.0/faster_rcnn/faster_rcnn_r50_fpn_1x_coco/faster_rcnn_r50_fpn_1x_coco_20200130-047c8118.pth) | [log](http://download.openmmlab.com/mmdetection/v2.0/faster_rcnn/faster_rcnn_r50_fpn_1x_coco/faster_rcnn_r50_fpn_1x_coco_20200130_204655.log.json) | -| R-50-FPN | pytorch | 2x | - | - | 38.4 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/faster_rcnn/faster_rcnn_r50_fpn_2x_coco.py) | [model](http://download.openmmlab.com/mmdetection/v2.0/faster_rcnn/faster_rcnn_r50_fpn_2x_coco/faster_rcnn_r50_fpn_2x_coco_bbox_mAP-0.384_20200504_210434-a5d8aa15.pth) | [log](http://download.openmmlab.com/mmdetection/v2.0/faster_rcnn/faster_rcnn_r50_fpn_2x_coco/faster_rcnn_r50_fpn_2x_coco_20200504_210434.log.json) | -| R-101-FPN | caffe | 1x | 5.7 | | 39.8 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/faster_rcnn/faster_rcnn_r101_caffe_fpn_1x_coco.py) | [model](http://download.openmmlab.com/mmdetection/v2.0/faster_rcnn/faster_rcnn_r101_caffe_fpn_1x_coco/faster_rcnn_r101_caffe_fpn_1x_coco_bbox_mAP-0.398_20200504_180057-b269e9dd.pth) | [log](http://download.openmmlab.com/mmdetection/v2.0/faster_rcnn/faster_rcnn_r101_caffe_fpn_1x_coco/faster_rcnn_r101_caffe_fpn_1x_coco_20200504_180057.log.json) | -| R-101-FPN | pytorch | 1x | 6.0 | 15.6 | 39.4 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/faster_rcnn/faster_rcnn_r101_fpn_1x_coco.py) | [model](http://download.openmmlab.com/mmdetection/v2.0/faster_rcnn/faster_rcnn_r101_fpn_1x_coco/faster_rcnn_r101_fpn_1x_coco_20200130-f513f705.pth) | [log](http://download.openmmlab.com/mmdetection/v2.0/faster_rcnn/faster_rcnn_r101_fpn_1x_coco/faster_rcnn_r101_fpn_1x_coco_20200130_204655.log.json) | -| R-101-FPN | pytorch | 2x | - | - | 39.8 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/faster_rcnn/faster_rcnn_r101_fpn_2x_coco.py) | [model](http://download.openmmlab.com/mmdetection/v2.0/faster_rcnn/faster_rcnn_r101_fpn_2x_coco/faster_rcnn_r101_fpn_2x_coco_bbox_mAP-0.398_20200504_210455-1d2dac9c.pth) | [log](http://download.openmmlab.com/mmdetection/v2.0/faster_rcnn/faster_rcnn_r101_fpn_2x_coco/faster_rcnn_r101_fpn_2x_coco_20200504_210455.log.json) | -| X-101-32x4d-FPN | pytorch | 1x | 7.2 | 13.8 | 41.2 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/faster_rcnn/faster_rcnn_x101_32x4d_fpn_1x_coco.py) | [model](http://download.openmmlab.com/mmdetection/v2.0/faster_rcnn/faster_rcnn_x101_32x4d_fpn_1x_coco/faster_rcnn_x101_32x4d_fpn_1x_coco_20200203-cff10310.pth) | [log](http://download.openmmlab.com/mmdetection/v2.0/faster_rcnn/faster_rcnn_x101_32x4d_fpn_1x_coco/faster_rcnn_x101_32x4d_fpn_1x_coco_20200203_000520.log.json) | -| X-101-32x4d-FPN | pytorch | 2x | - | - | 41.2 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/faster_rcnn/faster_rcnn_x101_32x4d_fpn_2x_coco.py) | [model](http://download.openmmlab.com/mmdetection/v2.0/faster_rcnn/faster_rcnn_x101_32x4d_fpn_2x_coco/faster_rcnn_x101_32x4d_fpn_2x_coco_bbox_mAP-0.412_20200506_041400-64a12c0b.pth) | [log](http://download.openmmlab.com/mmdetection/v2.0/faster_rcnn/faster_rcnn_x101_32x4d_fpn_2x_coco/faster_rcnn_x101_32x4d_fpn_2x_coco_20200506_041400.log.json) | -| X-101-64x4d-FPN | pytorch | 1x | 10.3 | 9.4 | 42.1 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/faster_rcnn/faster_rcnn_x101_64x4d_fpn_1x_coco.py) | [model](http://download.openmmlab.com/mmdetection/v2.0/faster_rcnn/faster_rcnn_x101_64x4d_fpn_1x_coco/faster_rcnn_x101_64x4d_fpn_1x_coco_20200204-833ee192.pth) | [log](http://download.openmmlab.com/mmdetection/v2.0/faster_rcnn/faster_rcnn_x101_64x4d_fpn_1x_coco/faster_rcnn_x101_64x4d_fpn_1x_coco_20200204_134340.log.json) | -| X-101-64x4d-FPN | pytorch | 2x | - | - | 41.6 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/faster_rcnn/faster_rcnn_x101_64x4d_fpn_2x_coco.py) | [model](http://download.openmmlab.com/mmdetection/v2.0/faster_rcnn/faster_rcnn_x101_64x4d_fpn_2x_coco/faster_rcnn_x101_64x4d_fpn_2x_coco_20200512_161033-5961fa95.pth) | [log](http://download.openmmlab.com/mmdetection/v2.0/faster_rcnn/faster_rcnn_x101_64x4d_fpn_2x_coco/faster_rcnn_x101_64x4d_fpn_2x_coco_20200512_161033.log.json) | +| R-50-FPN | caffe | 1x | 3.8 | | 37.8 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/faster_rcnn/faster_rcnn_r50_caffe_fpn_1x_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/faster_rcnn/faster_rcnn_r50_caffe_fpn_1x_coco/faster_rcnn_r50_caffe_fpn_1x_coco_bbox_mAP-0.378_20200504_180032-c5925ee5.pth) | [log](https://download.openmmlab.com/mmdetection/v2.0/faster_rcnn/faster_rcnn_r50_caffe_fpn_1x_coco/faster_rcnn_r50_caffe_fpn_1x_coco_20200504_180032.log.json) | +| R-50-FPN | pytorch | 1x | 4.0 | 21.4 | 37.4 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/faster_rcnn/faster_rcnn_r50_fpn_1x_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/faster_rcnn/faster_rcnn_r50_fpn_1x_coco/faster_rcnn_r50_fpn_1x_coco_20200130-047c8118.pth) | [log](https://download.openmmlab.com/mmdetection/v2.0/faster_rcnn/faster_rcnn_r50_fpn_1x_coco/faster_rcnn_r50_fpn_1x_coco_20200130_204655.log.json) | +| R-50-FPN | pytorch | 2x | - | - | 38.4 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/faster_rcnn/faster_rcnn_r50_fpn_2x_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/faster_rcnn/faster_rcnn_r50_fpn_2x_coco/faster_rcnn_r50_fpn_2x_coco_bbox_mAP-0.384_20200504_210434-a5d8aa15.pth) | [log](https://download.openmmlab.com/mmdetection/v2.0/faster_rcnn/faster_rcnn_r50_fpn_2x_coco/faster_rcnn_r50_fpn_2x_coco_20200504_210434.log.json) | +| R-101-FPN | caffe | 1x | 5.7 | | 39.8 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/faster_rcnn/faster_rcnn_r101_caffe_fpn_1x_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/faster_rcnn/faster_rcnn_r101_caffe_fpn_1x_coco/faster_rcnn_r101_caffe_fpn_1x_coco_bbox_mAP-0.398_20200504_180057-b269e9dd.pth) | [log](https://download.openmmlab.com/mmdetection/v2.0/faster_rcnn/faster_rcnn_r101_caffe_fpn_1x_coco/faster_rcnn_r101_caffe_fpn_1x_coco_20200504_180057.log.json) | +| R-101-FPN | pytorch | 1x | 6.0 | 15.6 | 39.4 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/faster_rcnn/faster_rcnn_r101_fpn_1x_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/faster_rcnn/faster_rcnn_r101_fpn_1x_coco/faster_rcnn_r101_fpn_1x_coco_20200130-f513f705.pth) | [log](https://download.openmmlab.com/mmdetection/v2.0/faster_rcnn/faster_rcnn_r101_fpn_1x_coco/faster_rcnn_r101_fpn_1x_coco_20200130_204655.log.json) | +| R-101-FPN | pytorch | 2x | - | - | 39.8 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/faster_rcnn/faster_rcnn_r101_fpn_2x_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/faster_rcnn/faster_rcnn_r101_fpn_2x_coco/faster_rcnn_r101_fpn_2x_coco_bbox_mAP-0.398_20200504_210455-1d2dac9c.pth) | [log](https://download.openmmlab.com/mmdetection/v2.0/faster_rcnn/faster_rcnn_r101_fpn_2x_coco/faster_rcnn_r101_fpn_2x_coco_20200504_210455.log.json) | +| X-101-32x4d-FPN | pytorch | 1x | 7.2 | 13.8 | 41.2 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/faster_rcnn/faster_rcnn_x101_32x4d_fpn_1x_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/faster_rcnn/faster_rcnn_x101_32x4d_fpn_1x_coco/faster_rcnn_x101_32x4d_fpn_1x_coco_20200203-cff10310.pth) | [log](https://download.openmmlab.com/mmdetection/v2.0/faster_rcnn/faster_rcnn_x101_32x4d_fpn_1x_coco/faster_rcnn_x101_32x4d_fpn_1x_coco_20200203_000520.log.json) | +| X-101-32x4d-FPN | pytorch | 2x | - | - | 41.2 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/faster_rcnn/faster_rcnn_x101_32x4d_fpn_2x_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/faster_rcnn/faster_rcnn_x101_32x4d_fpn_2x_coco/faster_rcnn_x101_32x4d_fpn_2x_coco_bbox_mAP-0.412_20200506_041400-64a12c0b.pth) | [log](https://download.openmmlab.com/mmdetection/v2.0/faster_rcnn/faster_rcnn_x101_32x4d_fpn_2x_coco/faster_rcnn_x101_32x4d_fpn_2x_coco_20200506_041400.log.json) | +| X-101-64x4d-FPN | pytorch | 1x | 10.3 | 9.4 | 42.1 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/faster_rcnn/faster_rcnn_x101_64x4d_fpn_1x_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/faster_rcnn/faster_rcnn_x101_64x4d_fpn_1x_coco/faster_rcnn_x101_64x4d_fpn_1x_coco_20200204-833ee192.pth) | [log](https://download.openmmlab.com/mmdetection/v2.0/faster_rcnn/faster_rcnn_x101_64x4d_fpn_1x_coco/faster_rcnn_x101_64x4d_fpn_1x_coco_20200204_134340.log.json) | +| X-101-64x4d-FPN | pytorch | 2x | - | - | 41.6 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/faster_rcnn/faster_rcnn_x101_64x4d_fpn_2x_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/faster_rcnn/faster_rcnn_x101_64x4d_fpn_2x_coco/faster_rcnn_x101_64x4d_fpn_2x_coco_20200512_161033-5961fa95.pth) | [log](https://download.openmmlab.com/mmdetection/v2.0/faster_rcnn/faster_rcnn_x101_64x4d_fpn_2x_coco/faster_rcnn_x101_64x4d_fpn_2x_coco_20200512_161033.log.json) | ## Different regression loss @@ -37,10 +37,10 @@ We trained with R-50-FPN pytorch style backbone for 1x schedule. | Backbone | Loss type | Mem (GB) | Inf time (fps) | box AP | Config | Download | | :-------------: | :-------: | :------: | :------------: | :----: | :------: | :--------: | -| R-50-FPN | L1Loss | 4.0 | 21.4 | 37.4 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/faster_rcnn/faster_rcnn_r50_fpn_1x_coco.py) | [model](http://download.openmmlab.com/mmdetection/v2.0/faster_rcnn/faster_rcnn_r50_fpn_1x_coco/faster_rcnn_r50_fpn_1x_coco_20200130-047c8118.pth) | [log](http://download.openmmlab.com/mmdetection/v2.0/faster_rcnn/faster_rcnn_r50_fpn_1x_coco/faster_rcnn_r50_fpn_1x_coco_20200130_204655.log.json) | -| R-50-FPN | IoULoss | | | 37.9 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/faster_rcnn/faster_rcnn_r50_fpn_1x_coco.py) | [model](http://download.openmmlab.com/mmdetection/v2.0/faster_rcnn/faster_rcnn_r50_fpn_1x_coco/faster_rcnn_r50_fpn_iou_1x_coco-fdd207f3.pth) | [log](http://download.openmmlab.com/mmdetection/v2.0/faster_rcnn/faster_rcnn_r50_fpn_1x_coco/faster_rcnn_r50_fpn_iou_1x_coco_20200506_095954.log.json) | -| R-50-FPN | GIoULoss | | | 37.6 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/faster_rcnn/faster_rcnn_r50_fpn_1x_coco.py) | [model](http://download.openmmlab.com/mmdetection/v2.0/faster_rcnn/faster_rcnn_r50_fpn_1x_coco/faster_rcnn_r50_fpn_giou_1x_coco-0eada910.pth) | [log](http://download.openmmlab.com/mmdetection/v2.0/faster_rcnn/faster_rcnn_r50_fpn_1x_coco/faster_rcnn_r50_fpn_giou_1x_coco_20200505_161120.log.json) | -| R-50-FPN | BoundedIoULoss | | | 37.4 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/faster_rcnn/faster_rcnn_r50_fpn_1x_coco.py) | [model](http://download.openmmlab.com/mmdetection/v2.0/faster_rcnn/faster_rcnn_r50_fpn_1x_coco/faster_rcnn_r50_fpn_bounded_iou_1x_coco-98ad993b.pth) | [log](http://download.openmmlab.com/mmdetection/v2.0/faster_rcnn/faster_rcnn_r50_fpn_1x_coco/faster_rcnn_r50_fpn_bounded_iou_1x_coco_20200505_160738.log.json) | +| R-50-FPN | L1Loss | 4.0 | 21.4 | 37.4 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/faster_rcnn/faster_rcnn_r50_fpn_1x_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/faster_rcnn/faster_rcnn_r50_fpn_1x_coco/faster_rcnn_r50_fpn_1x_coco_20200130-047c8118.pth) | [log](https://download.openmmlab.com/mmdetection/v2.0/faster_rcnn/faster_rcnn_r50_fpn_1x_coco/faster_rcnn_r50_fpn_1x_coco_20200130_204655.log.json) | +| R-50-FPN | IoULoss | | | 37.9 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/faster_rcnn/faster_rcnn_r50_fpn_1x_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/faster_rcnn/faster_rcnn_r50_fpn_1x_coco/faster_rcnn_r50_fpn_iou_1x_coco-fdd207f3.pth) | [log](https://download.openmmlab.com/mmdetection/v2.0/faster_rcnn/faster_rcnn_r50_fpn_1x_coco/faster_rcnn_r50_fpn_iou_1x_coco_20200506_095954.log.json) | +| R-50-FPN | GIoULoss | | | 37.6 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/faster_rcnn/faster_rcnn_r50_fpn_1x_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/faster_rcnn/faster_rcnn_r50_fpn_1x_coco/faster_rcnn_r50_fpn_giou_1x_coco-0eada910.pth) | [log](https://download.openmmlab.com/mmdetection/v2.0/faster_rcnn/faster_rcnn_r50_fpn_1x_coco/faster_rcnn_r50_fpn_giou_1x_coco_20200505_161120.log.json) | +| R-50-FPN | BoundedIoULoss | | | 37.4 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/faster_rcnn/faster_rcnn_r50_fpn_1x_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/faster_rcnn/faster_rcnn_r50_fpn_1x_coco/faster_rcnn_r50_fpn_bounded_iou_1x_coco-98ad993b.pth) | [log](https://download.openmmlab.com/mmdetection/v2.0/faster_rcnn/faster_rcnn_r50_fpn_1x_coco/faster_rcnn_r50_fpn_bounded_iou_1x_coco_20200505_160738.log.json) | ## Pre-trained Models @@ -48,10 +48,16 @@ We also train some models with longer schedules and multi-scale training. The us | Backbone | Style | Lr schd | Mem (GB) | Inf time (fps) | box AP | Config | Download | | :-------------: | :-----: | :-----: | :------: | :------------: | :----: | :------: | :--------: | -| [R-50-DC5](./faster_rcnn_r50_caffe_dc5_mstrain_1x_coco.py) | caffe | 1x | - | | 37.4 | [config](https://github.com/open-mmlab/mmdetection/blob/master/configs/faster_rcnn/faster_rcnn_r50_caffe_dc5_mstrain_1x_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/faster_rcnn/faster_rcnn_r50_caffe_dc5_mstrain_1x_coco/faster_rcnn_r50_caffe_dc5_mstrain_1x_coco_20201028_233851-b33d21b9.pth) | [log](https://download.openmmlab.com/mmdetection/v2.0/faster_rcnn/faster_rcnn_r50_caffe_dc5_mstrain_1x_coco/faster_rcnn_r50_caffe_dc5_mstrain_1x_coco_20201028_233851.log.json) -| [R-50-DC5](./faster_rcnn_r50_caffe_dc5_mstrain_3x_coco.py) | caffe | 3x | - | | 38.7 | [config](https://github.com/open-mmlab/mmdetection/blob/master/configs/faster_rcnn/faster_rcnn_r50_caffe_dc5_mstrain_3x_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/faster_rcnn/faster_rcnn_r50_caffe_dc5_mstrain_3x_coco/faster_rcnn_r50_caffe_dc5_mstrain_3x_coco_20201028_002107-34a53b2c.pth) | [log](https://download.openmmlab.com/mmdetection/v2.0/faster_rcnn/faster_rcnn_r50_caffe_dc5_mstrain_3x_coco/faster_rcnn_r50_caffe_dc5_mstrain_3x_coco_20201028_002107.log.json) -| [R-50-FPN](./faster_rcnn_r50_caffe_fpn_mstrain_2x_coco.py) | caffe | 2x | 4.3 | | 39.7 |[config](https://github.com/open-mmlab/mmdetection/tree/master/configs/faster_rcnn/faster_rcnn_r50_caffe_fpn_mstrain_2x_coco.py) | [model](http://download.openmmlab.com/mmdetection/v2.0/faster_rcnn/faster_rcnn_r50_caffe_fpn_mstrain_2x_coco/faster_rcnn_r50_caffe_fpn_mstrain_2x_coco_bbox_mAP-0.397_20200504_231813-10b2de58.pth) | [log](http://download.openmmlab.com/mmdetection/v2.0/faster_rcnn/faster_rcnn_r50_caffe_fpn_mstrain_2x_coco/faster_rcnn_r50_caffe_fpn_mstrain_2x_coco_20200504_231813.log.json) -| [R-50-FPN](./faster_rcnn_r50_caffe_fpn_mstrain_3x_coco.py) | caffe | 3x | 4.3 | | 40.2 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/faster_rcnn/faster_rcnn_r50_caffe_fpn_mstrain_3x_coco.py) | [model](http://download.openmmlab.com/mmdetection/v2.0/faster_rcnn/faster_rcnn_r50_caffe_fpn_mstrain_3x_coco/faster_rcnn_r50_caffe_fpn_mstrain_3x_coco_bbox_mAP-0.398_20200504_163323-30042637.pth) | [log](http://download.openmmlab.com/mmdetection/v2.0/faster_rcnn/faster_rcnn_r50_caffe_fpn_mstrain_3x_coco/faster_rcnn_r50_caffe_fpn_mstrain_3x_coco_20200504_163323.log.json) +| [R-50-DC5](./faster_rcnn_r50_caffe_dc5_mstrain_1x_coco.py) | caffe | 1x | - | | 37.4 | [config](https://github.com/open-mmlab/mmdetection/blob/master/configs/faster_rcnn/faster_rcnn_r50_caffe_dc5_mstrain_1x_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/faster_rcnn/faster_rcnn_r50_caffe_dc5_mstrain_1x_coco/faster_rcnn_r50_caffe_dc5_mstrain_1x_coco_20201028_233851-b33d21b9.pth) | [log](https://download.openmmlab.com/mmdetection/v2.0/faster_rcnn/faster_rcnn_r50_caffe_dc5_mstrain_1x_coco/faster_rcnn_r50_caffe_dc5_mstrain_1x_coco_20201028_233851.log.json) | +| [R-50-DC5](./faster_rcnn_r50_caffe_dc5_mstrain_3x_coco.py) | caffe | 3x | - | | 38.7 | [config](https://github.com/open-mmlab/mmdetection/blob/master/configs/faster_rcnn/faster_rcnn_r50_caffe_dc5_mstrain_3x_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/faster_rcnn/faster_rcnn_r50_caffe_dc5_mstrain_3x_coco/faster_rcnn_r50_caffe_dc5_mstrain_3x_coco_20201028_002107-34a53b2c.pth) | [log](https://download.openmmlab.com/mmdetection/v2.0/faster_rcnn/faster_rcnn_r50_caffe_dc5_mstrain_3x_coco/faster_rcnn_r50_caffe_dc5_mstrain_3x_coco_20201028_002107.log.json) | +| [R-50-FPN](./faster_rcnn_r50_caffe_fpn_mstrain_2x_coco.py) | caffe | 2x | 3.7 | | 39.7 |[config](https://github.com/open-mmlab/mmdetection/tree/master/configs/faster_rcnn/faster_rcnn_r50_caffe_fpn_mstrain_2x_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/faster_rcnn/faster_rcnn_r50_caffe_fpn_mstrain_2x_coco/faster_rcnn_r50_caffe_fpn_mstrain_2x_coco_bbox_mAP-0.397_20200504_231813-10b2de58.pth) | [log](https://download.openmmlab.com/mmdetection/v2.0/faster_rcnn/faster_rcnn_r50_caffe_fpn_mstrain_2x_coco/faster_rcnn_r50_caffe_fpn_mstrain_2x_coco_20200504_231813.log.json) | +| [R-50-FPN](./faster_rcnn_r50_caffe_fpn_mstrain_3x_coco.py) | caffe | 3x | 3.7 | | 39.9 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/faster_rcnn/faster_rcnn_r50_caffe_fpn_mstrain_3x_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/faster_rcnn/faster_rcnn_r50_caffe_fpn_mstrain_3x_coco/faster_rcnn_r50_caffe_fpn_mstrain_3x_coco_20210526_095054-1f77628b.pth) | [log](https://download.openmmlab.com/mmdetection/v2.0/faster_rcnn/faster_rcnn_r50_caffe_fpn_mstrain_3x_coco/faster_rcnn_r50_caffe_fpn_mstrain_3x_coco_20210526_095054.log.json) | +| [R-50-FPN](./faster_rcnn_r50_fpn_mstrain_3x_coco.py) | pytorch | 3x | 3.9 | | 40.3 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/faster_rcnn/faster_rcnn_r50_fpn_mstrain_3x_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/faster_rcnn/faster_rcnn_r50_fpn_mstrain_3x_coco/faster_rcnn_r50_fpn_mstrain_3x_coco_20210524_110822-e10bd31c.pth) | [log](https://download.openmmlab.com/mmdetection/v2.0/faster_rcnn/faster_rcnn_r50_fpn_mstrain_3x_coco/faster_rcnn_r50_fpn_mstrain_3x_coco_20210524_110822.log.json) | +| [R-101-FPN](./faster_rcnn_r101_caffe_fpn_mstrain_3x_coco.py) | caffe | 3x | 5.6 | | 42.0 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/faster_rcnn/faster_rcnn_r101_caffe_fpn_mstrain_3x_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/faster_rcnn/faster_rcnn_r101_caffe_fpn_mstrain_3x_coco/faster_rcnn_r101_caffe_fpn_mstrain_3x_coco_20210526_095742-a7ae426d.pth) | [log](https://download.openmmlab.com/mmdetection/v2.0/faster_rcnn/faster_rcnn_r101_caffe_fpn_mstrain_3x_coco/faster_rcnn_r101_caffe_fpn_mstrain_3x_coco_20210526_095742.log.json) | +| [R-101-FPN](./faster_rcnn_r101_fpn_mstrain_3x_coco.py) | pytorch | 3x | 5.8 | | 41.8 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/faster_rcnn/faster_rcnn_r101_fpn_mstrain_3x_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/faster_rcnn/faster_rcnn_r101_fpn_mstrain_3x_coco/faster_rcnn_r101_fpn_mstrain_3x_coco_20210524_110822-4d4d2ca8.pth) | [log](https://download.openmmlab.com/mmdetection/v2.0/faster_rcnn/faster_rcnn_r101_fpn_mstrain_3x_coco/faster_rcnn_r101_fpn_mstrain_3x_coco_20210524_110822.log.json) | +| [X-101-32x4d-FPN](./faster_rcnn_x101_32x4d_fpn_mstrain_3x_coco.py) | pytorch | 3x | 7.0 | | 42.5 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/faster_rcnn/faster_rcnn_x101_32x4d_fpn_mstrain_3x_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/faster_rcnn/faster_rcnn_x101_32x4d_fpn_mstrain_3x_coco/faster_rcnn_x101_32x4d_fpn_mstrain_3x_coco_20210524_124151-16b9b260.pth) | [log](https://download.openmmlab.com/mmdetection/v2.0/faster_rcnn/faster_rcnn_x101_32x4d_fpn_mstrain_3x_coco/faster_rcnn_x101_32x4d_fpn_mstrain_3x_coco_20210524_124151.log.json) | +| [X-101-32x8d-FPN](./faster_rcnn_x101_32x8d_fpn_mstrain_3x_coco.py) | pytorch | 3x | 10.1 | | 42.4 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/faster_rcnn/faster_rcnn_x101_32x8d_fpn_mstrain_3x_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/faster_rcnn/faster_rcnn_x101_32x8d_fpn_mstrain_3x_coco/faster_rcnn_x101_32x8d_fpn_mstrain_3x_coco_20210604_182954-002e082a.pth) | [log](https://download.openmmlab.com/mmdetection/v2.0/faster_rcnn/faster_rcnn_x101_32x8d_fpn_mstrain_3x_coco/faster_rcnn_x101_32x8d_fpn_mstrain_3x_coco_20210604_182954.log.json) | +| [X-101-64x4d-FPN](./faster_rcnn_x101_64x4d_fpn_mstrain_3x_coco.py) | pytorch | 3x | 10.0 | | 43.1 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/faster_rcnn/faster_rcnn_x101_64x4d_fpn_mstrain_3x_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/faster_rcnn/faster_rcnn_x101_64x4d_fpn_mstrain_3x_coco/faster_rcnn_x101_64x4d_fpn_mstrain_3x_coco_20210524_124528-26c63de6.pth) | [log](https://download.openmmlab.com/mmdetection/v2.0/faster_rcnn/faster_rcnn_x101_64x4d_fpn_mstrain_3x_coco/faster_rcnn_x101_64x4d_fpn_mstrain_3x_coco_20210524_124528.log.json) | We further finetune some pre-trained models on the COCO subsets, which only contain only a few of the 80 categories. diff --git a/configs/faster_rcnn/faster_rcnn_r101_caffe_fpn_1x_coco.py b/configs/faster_rcnn/faster_rcnn_r101_caffe_fpn_1x_coco.py index 95c7238..c6f078c 100644 --- a/configs/faster_rcnn/faster_rcnn_r101_caffe_fpn_1x_coco.py +++ b/configs/faster_rcnn/faster_rcnn_r101_caffe_fpn_1x_coco.py @@ -1,4 +1,7 @@ _base_ = './faster_rcnn_r50_caffe_fpn_1x_coco.py' model = dict( - pretrained='open-mmlab://detectron2/resnet101_caffe', - backbone=dict(depth=101)) + backbone=dict( + depth=101, + init_cfg=dict( + type='Pretrained', + checkpoint='open-mmlab://detectron2/resnet101_caffe'))) diff --git a/configs/faster_rcnn/faster_rcnn_r101_caffe_fpn_mstrain_3x_coco.py b/configs/faster_rcnn/faster_rcnn_r101_caffe_fpn_mstrain_3x_coco.py new file mode 100644 index 0000000..6a13fe9 --- /dev/null +++ b/configs/faster_rcnn/faster_rcnn_r101_caffe_fpn_mstrain_3x_coco.py @@ -0,0 +1,49 @@ +_base_ = 'faster_rcnn_r50_fpn_mstrain_3x_coco.py' + +model = dict( + backbone=dict( + depth=101, + norm_cfg=dict(requires_grad=False), + norm_eval=True, + style='caffe', + init_cfg=dict( + type='Pretrained', + checkpoint='open-mmlab://detectron2/resnet101_caffe'))) + +# use caffe img_norm +img_norm_cfg = dict( + mean=[103.530, 116.280, 123.675], std=[1.0, 1.0, 1.0], to_rgb=False) +train_pipeline = [ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations', with_bbox=True), + dict( + type='Resize', + img_scale=[(1333, 640), (1333, 800)], + multiscale_mode='range', + keep_ratio=True), + dict(type='RandomFlip', flip_ratio=0.5), + dict(type='Normalize', **img_norm_cfg), + dict(type='Pad', size_divisor=32), + dict(type='DefaultFormatBundle'), + dict(type='Collect', keys=['img', 'gt_bboxes', 'gt_labels']), +] +test_pipeline = [ + dict(type='LoadImageFromFile'), + dict( + type='MultiScaleFlipAug', + img_scale=(1333, 800), + flip=False, + transforms=[ + dict(type='Resize', keep_ratio=True), + dict(type='RandomFlip'), + dict(type='Normalize', **img_norm_cfg), + dict(type='Pad', size_divisor=32), + dict(type='ImageToTensor', keys=['img']), + dict(type='Collect', keys=['img']), + ]) +] + +data = dict( + train=dict(dataset=dict(pipeline=train_pipeline)), + val=dict(pipeline=test_pipeline), + test=dict(pipeline=test_pipeline)) diff --git a/configs/faster_rcnn/faster_rcnn_r101_fpn_1x_coco.py b/configs/faster_rcnn/faster_rcnn_r101_fpn_1x_coco.py index d2edab1..1de53a6 100644 --- a/configs/faster_rcnn/faster_rcnn_r101_fpn_1x_coco.py +++ b/configs/faster_rcnn/faster_rcnn_r101_fpn_1x_coco.py @@ -1,2 +1,6 @@ _base_ = './faster_rcnn_r50_fpn_1x_coco.py' -model = dict(pretrained='torchvision://resnet101', backbone=dict(depth=101)) +model = dict( + backbone=dict( + depth=101, + init_cfg=dict(type='Pretrained', + checkpoint='torchvision://resnet101'))) diff --git a/configs/faster_rcnn/faster_rcnn_r101_fpn_2x_coco.py b/configs/faster_rcnn/faster_rcnn_r101_fpn_2x_coco.py index 9367a3c..0d41599 100644 --- a/configs/faster_rcnn/faster_rcnn_r101_fpn_2x_coco.py +++ b/configs/faster_rcnn/faster_rcnn_r101_fpn_2x_coco.py @@ -1,2 +1,6 @@ _base_ = './faster_rcnn_r50_fpn_2x_coco.py' -model = dict(pretrained='torchvision://resnet101', backbone=dict(depth=101)) +model = dict( + backbone=dict( + depth=101, + init_cfg=dict(type='Pretrained', + checkpoint='torchvision://resnet101'))) diff --git a/configs/faster_rcnn/faster_rcnn_r101_fpn_mstrain_3x_coco.py b/configs/faster_rcnn/faster_rcnn_r101_fpn_mstrain_3x_coco.py new file mode 100644 index 0000000..0b498bb --- /dev/null +++ b/configs/faster_rcnn/faster_rcnn_r101_fpn_mstrain_3x_coco.py @@ -0,0 +1,7 @@ +_base_ = 'faster_rcnn_r50_fpn_mstrain_3x_coco.py' + +model = dict( + backbone=dict( + depth=101, + init_cfg=dict(type='Pretrained', + checkpoint='torchvision://resnet101'))) diff --git a/configs/faster_rcnn/faster_rcnn_r50_caffe_fpn_1x_coco.py b/configs/faster_rcnn/faster_rcnn_r50_caffe_fpn_1x_coco.py index 762c72b..56c01bd 100644 --- a/configs/faster_rcnn/faster_rcnn_r50_caffe_fpn_1x_coco.py +++ b/configs/faster_rcnn/faster_rcnn_r50_caffe_fpn_1x_coco.py @@ -1,8 +1,12 @@ _base_ = './faster_rcnn_r50_fpn_1x_coco.py' model = dict( - pretrained='open-mmlab://detectron2/resnet50_caffe', backbone=dict( - norm_cfg=dict(requires_grad=False), norm_eval=True, style='caffe')) + norm_cfg=dict(requires_grad=False), + norm_eval=True, + style='caffe', + init_cfg=dict( + type='Pretrained', + checkpoint='open-mmlab://detectron2/resnet50_caffe'))) # use caffe img_norm img_norm_cfg = dict( mean=[103.530, 116.280, 123.675], std=[1.0, 1.0, 1.0], to_rgb=False) diff --git a/configs/faster_rcnn/faster_rcnn_r50_caffe_fpn_mstrain_1x_coco-person-bicycle-car.py b/configs/faster_rcnn/faster_rcnn_r50_caffe_fpn_mstrain_1x_coco-person-bicycle-car.py index 23d7285..4f1f376 100644 --- a/configs/faster_rcnn/faster_rcnn_r50_caffe_fpn_mstrain_1x_coco-person-bicycle-car.py +++ b/configs/faster_rcnn/faster_rcnn_r50_caffe_fpn_mstrain_1x_coco-person-bicycle-car.py @@ -6,4 +6,4 @@ val=dict(classes=classes), test=dict(classes=classes)) -load_from = 'http://download.openmmlab.com/mmdetection/v2.0/faster_rcnn/faster_rcnn_r50_caffe_fpn_mstrain_3x_coco/faster_rcnn_r50_caffe_fpn_mstrain_3x_coco_bbox_mAP-0.398_20200504_163323-30042637.pth' # noqa +load_from = 'https://download.openmmlab.com/mmdetection/v2.0/faster_rcnn/faster_rcnn_r50_caffe_fpn_mstrain_3x_coco/faster_rcnn_r50_caffe_fpn_mstrain_3x_coco_bbox_mAP-0.398_20200504_163323-30042637.pth' # noqa diff --git a/configs/faster_rcnn/faster_rcnn_r50_caffe_fpn_mstrain_1x_coco-person.py b/configs/faster_rcnn/faster_rcnn_r50_caffe_fpn_mstrain_1x_coco-person.py index b0164c7..b5dfb4f 100644 --- a/configs/faster_rcnn/faster_rcnn_r50_caffe_fpn_mstrain_1x_coco-person.py +++ b/configs/faster_rcnn/faster_rcnn_r50_caffe_fpn_mstrain_1x_coco-person.py @@ -6,4 +6,4 @@ val=dict(classes=classes), test=dict(classes=classes)) -load_from = 'http://download.openmmlab.com/mmdetection/v2.0/faster_rcnn/faster_rcnn_r50_caffe_fpn_mstrain_3x_coco/faster_rcnn_r50_caffe_fpn_mstrain_3x_coco_bbox_mAP-0.398_20200504_163323-30042637.pth' # noqa +load_from = 'https://download.openmmlab.com/mmdetection/v2.0/faster_rcnn/faster_rcnn_r50_caffe_fpn_mstrain_3x_coco/faster_rcnn_r50_caffe_fpn_mstrain_3x_coco_bbox_mAP-0.398_20200504_163323-30042637.pth' # noqa diff --git a/configs/faster_rcnn/faster_rcnn_r50_caffe_fpn_mstrain_1x_coco.py b/configs/faster_rcnn/faster_rcnn_r50_caffe_fpn_mstrain_1x_coco.py index 4b87b2c..f807a19 100644 --- a/configs/faster_rcnn/faster_rcnn_r50_caffe_fpn_mstrain_1x_coco.py +++ b/configs/faster_rcnn/faster_rcnn_r50_caffe_fpn_mstrain_1x_coco.py @@ -1,8 +1,12 @@ _base_ = './faster_rcnn_r50_fpn_1x_coco.py' model = dict( - pretrained='open-mmlab://detectron2/resnet50_caffe', backbone=dict( - norm_cfg=dict(requires_grad=False), norm_eval=True, style='caffe')) + norm_cfg=dict(requires_grad=False), + norm_eval=True, + style='caffe', + init_cfg=dict( + type='Pretrained', + checkpoint='open-mmlab://detectron2/resnet50_caffe'))) # use caffe img_norm img_norm_cfg = dict( mean=[103.530, 116.280, 123.675], std=[1.0, 1.0, 1.0], to_rgb=False) diff --git a/configs/faster_rcnn/faster_rcnn_r50_caffe_fpn_mstrain_3x_coco.py b/configs/faster_rcnn/faster_rcnn_r50_caffe_fpn_mstrain_3x_coco.py index a0ba54d..9eeaace 100644 --- a/configs/faster_rcnn/faster_rcnn_r50_caffe_fpn_mstrain_3x_coco.py +++ b/configs/faster_rcnn/faster_rcnn_r50_caffe_fpn_mstrain_3x_coco.py @@ -1,4 +1,47 @@ -_base_ = './faster_rcnn_r50_caffe_fpn_mstrain_1x_coco.py' -# learning policy -lr_config = dict(step=[28, 34]) -runner = dict(type='EpochBasedRunner', max_epochs=36) +_base_ = 'faster_rcnn_r50_fpn_mstrain_3x_coco.py' +model = dict( + backbone=dict( + norm_cfg=dict(requires_grad=False), + norm_eval=True, + style='caffe', + init_cfg=dict( + type='Pretrained', + checkpoint='open-mmlab://detectron2/resnet50_caffe'))) + +# use caffe img_norm +img_norm_cfg = dict( + mean=[103.530, 116.280, 123.675], std=[1.0, 1.0, 1.0], to_rgb=False) +train_pipeline = [ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations', with_bbox=True), + dict( + type='Resize', + img_scale=[(1333, 640), (1333, 800)], + multiscale_mode='range', + keep_ratio=True), + dict(type='RandomFlip', flip_ratio=0.5), + dict(type='Normalize', **img_norm_cfg), + dict(type='Pad', size_divisor=32), + dict(type='DefaultFormatBundle'), + dict(type='Collect', keys=['img', 'gt_bboxes', 'gt_labels']), +] +test_pipeline = [ + dict(type='LoadImageFromFile'), + dict( + type='MultiScaleFlipAug', + img_scale=(1333, 800), + flip=False, + transforms=[ + dict(type='Resize', keep_ratio=True), + dict(type='RandomFlip'), + dict(type='Normalize', **img_norm_cfg), + dict(type='Pad', size_divisor=32), + dict(type='ImageToTensor', keys=['img']), + dict(type='Collect', keys=['img']), + ]) +] + +data = dict( + train=dict(dataset=dict(pipeline=train_pipeline)), + val=dict(pipeline=test_pipeline), + test=dict(pipeline=test_pipeline)) diff --git a/configs/faster_rcnn/faster_rcnn_r50_fpn_ciou_1x_coco.py b/configs/faster_rcnn/faster_rcnn_r50_fpn_ciou_1x_coco.py new file mode 100644 index 0000000..886d566 --- /dev/null +++ b/configs/faster_rcnn/faster_rcnn_r50_fpn_ciou_1x_coco.py @@ -0,0 +1,6 @@ +_base_ = './faster_rcnn_r50_fpn_1x_coco.py' +model = dict( + roi_head=dict( + bbox_head=dict( + reg_decoded_bbox=True, + loss_bbox=dict(type='CIoULoss', loss_weight=12.0)))) diff --git a/configs/faster_rcnn/faster_rcnn_r50_fpn_mstrain_3x_coco.py b/configs/faster_rcnn/faster_rcnn_r50_fpn_mstrain_3x_coco.py new file mode 100644 index 0000000..faf8f92 --- /dev/null +++ b/configs/faster_rcnn/faster_rcnn_r50_fpn_mstrain_3x_coco.py @@ -0,0 +1,3 @@ +_base_ = [ + '../common/mstrain_3x_coco.py', '../_base_/models/faster_rcnn_r50_fpn.py' +] diff --git a/configs/faster_rcnn/faster_rcnn_x101_32x4d_fpn_1x_coco.py b/configs/faster_rcnn/faster_rcnn_x101_32x4d_fpn_1x_coco.py index c536fcc..3808c9f 100644 --- a/configs/faster_rcnn/faster_rcnn_x101_32x4d_fpn_1x_coco.py +++ b/configs/faster_rcnn/faster_rcnn_x101_32x4d_fpn_1x_coco.py @@ -1,6 +1,5 @@ _base_ = './faster_rcnn_r50_fpn_1x_coco.py' model = dict( - pretrained='open-mmlab://resnext101_32x4d', backbone=dict( type='ResNeXt', depth=101, @@ -10,4 +9,6 @@ out_indices=(0, 1, 2, 3), frozen_stages=1, norm_cfg=dict(type='BN', requires_grad=True), - style='pytorch')) + style='pytorch', + init_cfg=dict( + type='Pretrained', checkpoint='open-mmlab://resnext101_32x4d'))) diff --git a/configs/faster_rcnn/faster_rcnn_x101_32x4d_fpn_2x_coco.py b/configs/faster_rcnn/faster_rcnn_x101_32x4d_fpn_2x_coco.py index 9276092..e93f5d8 100644 --- a/configs/faster_rcnn/faster_rcnn_x101_32x4d_fpn_2x_coco.py +++ b/configs/faster_rcnn/faster_rcnn_x101_32x4d_fpn_2x_coco.py @@ -1,6 +1,5 @@ _base_ = './faster_rcnn_r50_fpn_2x_coco.py' model = dict( - pretrained='open-mmlab://resnext101_32x4d', backbone=dict( type='ResNeXt', depth=101, @@ -10,4 +9,6 @@ out_indices=(0, 1, 2, 3), frozen_stages=1, norm_cfg=dict(type='BN', requires_grad=True), - style='pytorch')) + style='pytorch', + init_cfg=dict( + type='Pretrained', checkpoint='open-mmlab://resnext101_32x4d'))) diff --git a/configs/faster_rcnn/faster_rcnn_x101_32x4d_fpn_mstrain_3x_coco.py b/configs/faster_rcnn/faster_rcnn_x101_32x4d_fpn_mstrain_3x_coco.py new file mode 100644 index 0000000..f55985d --- /dev/null +++ b/configs/faster_rcnn/faster_rcnn_x101_32x4d_fpn_mstrain_3x_coco.py @@ -0,0 +1,16 @@ +_base_ = [ + '../common/mstrain_3x_coco.py', '../_base_/models/faster_rcnn_r50_fpn.py' +] +model = dict( + backbone=dict( + type='ResNeXt', + depth=101, + groups=32, + base_width=4, + num_stages=4, + out_indices=(0, 1, 2, 3), + frozen_stages=1, + norm_cfg=dict(type='BN', requires_grad=True), + style='pytorch', + init_cfg=dict( + type='Pretrained', checkpoint='open-mmlab://resnext101_32x4d'))) diff --git a/configs/faster_rcnn/faster_rcnn_x101_32x8d_fpn_mstrain_3x_coco.py b/configs/faster_rcnn/faster_rcnn_x101_32x8d_fpn_mstrain_3x_coco.py new file mode 100644 index 0000000..a5d5aeb --- /dev/null +++ b/configs/faster_rcnn/faster_rcnn_x101_32x8d_fpn_mstrain_3x_coco.py @@ -0,0 +1,62 @@ +_base_ = [ + '../common/mstrain_3x_coco.py', '../_base_/models/faster_rcnn_r50_fpn.py' +] +model = dict( + backbone=dict( + type='ResNeXt', + depth=101, + groups=32, + base_width=8, + num_stages=4, + out_indices=(0, 1, 2, 3), + frozen_stages=1, + norm_cfg=dict(type='BN', requires_grad=False), + style='pytorch', + init_cfg=dict( + type='Pretrained', + checkpoint='open-mmlab://detectron2/resnext101_32x8d'))) + +# ResNeXt-101-32x8d model trained with Caffe2 at FB, +# so the mean and std need to be changed. +img_norm_cfg = dict( + mean=[103.530, 116.280, 123.675], + std=[57.375, 57.120, 58.395], + to_rgb=False) + +# In mstrain 3x config, img_scale=[(1333, 640), (1333, 800)], +# multiscale_mode='range' +train_pipeline = [ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations', with_bbox=True), + dict( + type='Resize', + img_scale=[(1333, 640), (1333, 800)], + multiscale_mode='range', + keep_ratio=True), + dict(type='RandomFlip', flip_ratio=0.5), + dict(type='Normalize', **img_norm_cfg), + dict(type='Pad', size_divisor=32), + dict(type='DefaultFormatBundle'), + dict(type='Collect', keys=['img', 'gt_bboxes', 'gt_labels']), +] +test_pipeline = [ + dict(type='LoadImageFromFile'), + dict( + type='MultiScaleFlipAug', + img_scale=(1333, 800), + flip=False, + transforms=[ + dict(type='Resize', keep_ratio=True), + dict(type='RandomFlip'), + dict(type='Normalize', **img_norm_cfg), + dict(type='Pad', size_divisor=32), + dict(type='ImageToTensor', keys=['img']), + dict(type='Collect', keys=['img']), + ]) +] + +# Use RepeatDataset to speed up training +data = dict( + train=dict(dataset=dict(pipeline=train_pipeline)), + val=dict(pipeline=test_pipeline), + test=dict(pipeline=test_pipeline)) diff --git a/configs/faster_rcnn/faster_rcnn_x101_64x4d_fpn_1x_coco.py b/configs/faster_rcnn/faster_rcnn_x101_64x4d_fpn_1x_coco.py index b588b4e..8bf2b65 100644 --- a/configs/faster_rcnn/faster_rcnn_x101_64x4d_fpn_1x_coco.py +++ b/configs/faster_rcnn/faster_rcnn_x101_64x4d_fpn_1x_coco.py @@ -1,6 +1,5 @@ _base_ = './faster_rcnn_r50_fpn_1x_coco.py' model = dict( - pretrained='open-mmlab://resnext101_64x4d', backbone=dict( type='ResNeXt', depth=101, @@ -10,4 +9,6 @@ out_indices=(0, 1, 2, 3), frozen_stages=1, norm_cfg=dict(type='BN', requires_grad=True), - style='pytorch')) + style='pytorch', + init_cfg=dict( + type='Pretrained', checkpoint='open-mmlab://resnext101_64x4d'))) diff --git a/configs/faster_rcnn/faster_rcnn_x101_64x4d_fpn_2x_coco.py b/configs/faster_rcnn/faster_rcnn_x101_64x4d_fpn_2x_coco.py index e87d21a..7ea9b2d 100644 --- a/configs/faster_rcnn/faster_rcnn_x101_64x4d_fpn_2x_coco.py +++ b/configs/faster_rcnn/faster_rcnn_x101_64x4d_fpn_2x_coco.py @@ -1,6 +1,5 @@ _base_ = './faster_rcnn_r50_fpn_2x_coco.py' model = dict( - pretrained='open-mmlab://resnext101_64x4d', backbone=dict( type='ResNeXt', depth=101, @@ -10,4 +9,6 @@ out_indices=(0, 1, 2, 3), frozen_stages=1, norm_cfg=dict(type='BN', requires_grad=True), - style='pytorch')) + style='pytorch', + init_cfg=dict( + type='Pretrained', checkpoint='open-mmlab://resnext101_64x4d'))) diff --git a/configs/faster_rcnn/faster_rcnn_x101_64x4d_fpn_mstrain_3x_coco.py b/configs/faster_rcnn/faster_rcnn_x101_64x4d_fpn_mstrain_3x_coco.py new file mode 100644 index 0000000..80397f4 --- /dev/null +++ b/configs/faster_rcnn/faster_rcnn_x101_64x4d_fpn_mstrain_3x_coco.py @@ -0,0 +1,16 @@ +_base_ = [ + '../common/mstrain_3x_coco.py', '../_base_/models/faster_rcnn_r50_fpn.py' +] +model = dict( + backbone=dict( + type='ResNeXt', + depth=101, + groups=64, + base_width=4, + num_stages=4, + out_indices=(0, 1, 2, 3), + frozen_stages=1, + norm_cfg=dict(type='BN', requires_grad=True), + style='pytorch', + init_cfg=dict( + type='Pretrained', checkpoint='open-mmlab://resnext101_64x4d'))) diff --git a/configs/faster_rcnn/metafile.yml b/configs/faster_rcnn/metafile.yml new file mode 100644 index 0000000..97faf53 --- /dev/null +++ b/configs/faster_rcnn/metafile.yml @@ -0,0 +1,383 @@ +Collections: + - Name: Faster R-CNN + Metadata: + Training Data: COCO + Training Techniques: + - SGD with Momentum + - Weight Decay + Training Resources: 8x V100 GPUs + Architecture: + - FPN + - RPN + - ResNet + - RoIPool + Paper: + URL: https://arxiv.org/abs/1506.01497 + Title: 'Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks' + README: configs/faster_rcnn/README.md + Code: + URL: https://github.com/open-mmlab/mmdetection/blob/v2.0.0/mmdet/models/detectors/faster_rcnn.py#L6 + Version: v2.0.0 + +Models: + - Name: faster_rcnn_r50_caffe_dc5_1x_coco + In Collection: Faster R-CNN + Config: configs/faster_rcnn/faster_rcnn_r50_caffe_dc5_1x_coco.py + Metadata: + Epochs: 12 + Results: + - Task: Object Detection + Dataset: COCO + Metrics: + box AP: 37.2 + Weights: https://download.openmmlab.com/mmdetection/v2.0/faster_rcnn/faster_rcnn_r50_caffe_dc5_1x_coco/faster_rcnn_r50_caffe_dc5_1x_coco_20201030_151909-531f0f43.pth + + - Name: faster_rcnn_r50_caffe_fpn_1x_coco + In Collection: Faster R-CNN + Config: configs/faster_rcnn/faster_rcnn_r50_caffe_fpn_1x_coco.py + Metadata: + Training Memory (GB): 3.8 + Epochs: 12 + Results: + - Task: Object Detection + Dataset: COCO + Metrics: + box AP: 37.8 + Weights: https://download.openmmlab.com/mmdetection/v2.0/faster_rcnn/faster_rcnn_r50_caffe_fpn_1x_coco/faster_rcnn_r50_caffe_fpn_1x_coco_bbox_mAP-0.378_20200504_180032-c5925ee5.pth + + - Name: faster_rcnn_r50_fpn_1x_coco + In Collection: Faster R-CNN + Config: configs/faster_rcnn/faster_rcnn_r50_fpn_1x_coco.py + Metadata: + Training Memory (GB): 4.0 + inference time (ms/im): + - value: 46.73 + hardware: V100 + backend: PyTorch + batch size: 1 + mode: FP32 + resolution: (800, 1333) + Epochs: 12 + Results: + - Task: Object Detection + Dataset: COCO + Metrics: + box AP: 37.4 + Weights: https://download.openmmlab.com/mmdetection/v2.0/faster_rcnn/faster_rcnn_r50_fpn_1x_coco/faster_rcnn_r50_fpn_1x_coco_20200130-047c8118.pth + + - Name: faster_rcnn_r50_fpn_2x_coco + In Collection: Faster R-CNN + Config: configs/faster_rcnn/faster_rcnn_r50_fpn_2x_coco.py + Metadata: + Training Memory (GB): 4.0 + inference time (ms/im): + - value: 46.73 + hardware: V100 + backend: PyTorch + batch size: 1 + mode: FP32 + resolution: (800, 1333) + Epochs: 24 + Results: + - Task: Object Detection + Dataset: COCO + Metrics: + box AP: 38.4 + Weights: https://download.openmmlab.com/mmdetection/v2.0/faster_rcnn/faster_rcnn_r50_fpn_2x_coco/faster_rcnn_r50_fpn_2x_coco_bbox_mAP-0.384_20200504_210434-a5d8aa15.pth + + - Name: faster_rcnn_r101_caffe_fpn_1x_coco + In Collection: Faster R-CNN + Config: configs/faster_rcnn/faster_rcnn_r101_caffe_fpn_1x_coco.py + Metadata: + Training Memory (GB): 5.7 + Epochs: 12 + Results: + - Task: Object Detection + Dataset: COCO + Metrics: + box AP: 39.8 + Weights: https://download.openmmlab.com/mmdetection/v2.0/faster_rcnn/faster_rcnn_r101_caffe_fpn_1x_coco/faster_rcnn_r101_caffe_fpn_1x_coco_bbox_mAP-0.398_20200504_180057-b269e9dd.pth + + - Name: faster_rcnn_r101_fpn_1x_coco + In Collection: Faster R-CNN + Config: configs/faster_rcnn/faster_rcnn_r101_fpn_1x_coco.py + Metadata: + Training Memory (GB): 6.0 + inference time (ms/im): + - value: 64.1 + hardware: V100 + backend: PyTorch + batch size: 1 + mode: FP32 + resolution: (800, 1333) + Epochs: 12 + Results: + - Task: Object Detection + Dataset: COCO + Metrics: + box AP: 39.4 + Weights: https://download.openmmlab.com/mmdetection/v2.0/faster_rcnn/faster_rcnn_r101_fpn_1x_coco/faster_rcnn_r101_fpn_1x_coco_20200130-f513f705.pth + + - Name: faster_rcnn_r101_fpn_2x_coco + In Collection: Faster R-CNN + Config: configs/faster_rcnn/faster_rcnn_r101_fpn_2x_coco.py + Metadata: + Training Memory (GB): 6.0 + inference time (ms/im): + - value: 64.1 + hardware: V100 + backend: PyTorch + batch size: 1 + mode: FP32 + resolution: (800, 1333) + Epochs: 24 + Results: + - Task: Object Detection + Dataset: COCO + Metrics: + box AP: 39.8 + Weights: https://download.openmmlab.com/mmdetection/v2.0/faster_rcnn/faster_rcnn_r101_fpn_2x_coco/faster_rcnn_r101_fpn_2x_coco_bbox_mAP-0.398_20200504_210455-1d2dac9c.pth + + - Name: faster_rcnn_x101_32x4d_fpn_1x_coco + In Collection: Faster R-CNN + Config: configs/faster_rcnn/faster_rcnn_x101_32x4d_fpn_1x_coco.py + Metadata: + Training Memory (GB): 7.2 + inference time (ms/im): + - value: 72.46 + hardware: V100 + backend: PyTorch + batch size: 1 + mode: FP32 + resolution: (800, 1333) + Epochs: 12 + Results: + - Task: Object Detection + Dataset: COCO + Metrics: + box AP: 41.2 + Weights: https://download.openmmlab.com/mmdetection/v2.0/faster_rcnn/faster_rcnn_x101_32x4d_fpn_1x_coco/faster_rcnn_x101_32x4d_fpn_1x_coco_20200203-cff10310.pth + + - Name: faster_rcnn_x101_32x4d_fpn_2x_coco + In Collection: Faster R-CNN + Config: configs/faster_rcnn/faster_rcnn_x101_32x4d_fpn_2x_coco.py + Metadata: + Training Memory (GB): 7.2 + inference time (ms/im): + - value: 72.46 + hardware: V100 + backend: PyTorch + batch size: 1 + mode: FP32 + resolution: (800, 1333) + Epochs: 24 + Results: + - Task: Object Detection + Dataset: COCO + Metrics: + box AP: 41.2 + Weights: https://download.openmmlab.com/mmdetection/v2.0/faster_rcnn/faster_rcnn_x101_32x4d_fpn_2x_coco/faster_rcnn_x101_32x4d_fpn_2x_coco_bbox_mAP-0.412_20200506_041400-64a12c0b.pth + + - Name: faster_rcnn_x101_64x4d_fpn_1x_coco + In Collection: Faster R-CNN + Config: configs/faster_rcnn/faster_rcnn_x101_64x4d_fpn_1x_coco.py + Metadata: + Training Memory (GB): 10.3 + inference time (ms/im): + - value: 106.38 + hardware: V100 + backend: PyTorch + batch size: 1 + mode: FP32 + resolution: (800, 1333) + Epochs: 12 + Results: + - Task: Object Detection + Dataset: COCO + Metrics: + box AP: 42.1 + Weights: https://download.openmmlab.com/mmdetection/v2.0/faster_rcnn/faster_rcnn_x101_64x4d_fpn_1x_coco/faster_rcnn_x101_64x4d_fpn_1x_coco_20200204-833ee192.pth + + - Name: faster_rcnn_x101_64x4d_fpn_2x_coco + In Collection: Faster R-CNN + Config: configs/faster_rcnn/faster_rcnn_x101_64x4d_fpn_2x_coco.py + Metadata: + Training Memory (GB): 10.3 + inference time (ms/im): + - value: 106.38 + hardware: V100 + backend: PyTorch + batch size: 1 + mode: FP32 + resolution: (800, 1333) + Epochs: 24 + Results: + - Task: Object Detection + Dataset: COCO + Metrics: + box AP: 41.6 + Weights: https://download.openmmlab.com/mmdetection/v2.0/faster_rcnn/faster_rcnn_x101_64x4d_fpn_2x_coco/faster_rcnn_x101_64x4d_fpn_2x_coco_20200512_161033-5961fa95.pth + + - Name: faster_rcnn_r50_fpn_iou_1x_coco + In Collection: Faster R-CNN + Config: configs/faster_rcnn/faster_rcnn_r50_fpn_iou_1x_coco.py + Metadata: + Epochs: 12 + Results: + - Task: Object Detection + Dataset: COCO + Metrics: + box AP: 37.9 + Weights: https://download.openmmlab.com/mmdetection/v2.0/faster_rcnn/faster_rcnn_r50_fpn_1x_coco/faster_rcnn_r50_fpn_iou_1x_coco-fdd207f3.pth + + - Name: faster_rcnn_r50_fpn_giou_1x_coco + In Collection: Faster R-CNN + Config: configs/faster_rcnn/faster_rcnn_r50_fpn_giou_1x_coco.py + Metadata: + Epochs: 12 + Results: + - Task: Object Detection + Dataset: COCO + Metrics: + box AP: 37.6 + Weights: https://download.openmmlab.com/mmdetection/v2.0/faster_rcnn/faster_rcnn_r50_fpn_1x_coco/faster_rcnn_r50_fpn_giou_1x_coco-0eada910.pth + + - Name: faster_rcnn_r50_fpn_bounded_iou_1x_coco + In Collection: Faster R-CNN + Config: configs/faster_rcnn/faster_rcnn_r50_fpn_bounded_iou_1x_coco.py + Metadata: + Epochs: 12 + Results: + - Task: Object Detection + Dataset: COCO + Metrics: + box AP: 37.4 + Weights: https://download.openmmlab.com/mmdetection/v2.0/faster_rcnn/faster_rcnn_r50_fpn_1x_coco/faster_rcnn_r50_fpn_bounded_iou_1x_coco-98ad993b.pth + + - Name: faster_rcnn_r50_caffe_dc5_mstrain_1x_coco + In Collection: Faster R-CNN + Config: configs/faster_rcnn/faster_rcnn_r50_caffe_dc5_mstrain_1x_coco.py + Metadata: + Epochs: 12 + Results: + - Task: Object Detection + Dataset: COCO + Metrics: + box AP: 37.4 + Weights: https://download.openmmlab.com/mmdetection/v2.0/faster_rcnn/faster_rcnn_r50_caffe_dc5_mstrain_1x_coco/faster_rcnn_r50_caffe_dc5_mstrain_1x_coco_20201028_233851-b33d21b9.pth + + - Name: faster_rcnn_r50_caffe_dc5_mstrain_3x_coco + In Collection: Faster R-CNN + Config: configs/faster_rcnn/faster_rcnn_r50_caffe_dc5_mstrain_3x_coco.py + Metadata: + Epochs: 36 + Results: + - Task: Object Detection + Dataset: COCO + Metrics: + box AP: 38.7 + Weights: https://download.openmmlab.com/mmdetection/v2.0/faster_rcnn/faster_rcnn_r50_caffe_dc5_mstrain_3x_coco/faster_rcnn_r50_caffe_dc5_mstrain_3x_coco_20201028_002107-34a53b2c.pth + + - Name: faster_rcnn_r50_caffe_fpn_mstrain_2x_coco + In Collection: Faster R-CNN + Config: configs/faster_rcnn/faster_rcnn_r50_caffe_fpn_mstrain_2x_coco.py + Metadata: + Training Memory (GB): 4.3 + Epochs: 24 + Results: + - Task: Object Detection + Dataset: COCO + Metrics: + box AP: 39.7 + Weights: https://download.openmmlab.com/mmdetection/v2.0/faster_rcnn/faster_rcnn_r50_caffe_fpn_mstrain_2x_coco/faster_rcnn_r50_caffe_fpn_mstrain_2x_coco_bbox_mAP-0.397_20200504_231813-10b2de58.pth + + - Name: faster_rcnn_r50_caffe_fpn_mstrain_3x_coco + In Collection: Faster R-CNN + Config: configs/faster_rcnn/faster_rcnn_r50_caffe_fpn_mstrain_3x_coco.py + Metadata: + Training Memory (GB): 3.7 + Epochs: 36 + Results: + - Task: Object Detection + Dataset: COCO + Metrics: + box AP: 39.9 + Weights: https://download.openmmlab.com/mmdetection/v2.0/faster_rcnn/faster_rcnn_r50_caffe_fpn_mstrain_3x_coco/faster_rcnn_r50_caffe_fpn_mstrain_3x_coco_20210526_095054-1f77628b.pth + + - Name: faster_rcnn_r50_fpn_mstrain_3x_coco + In Collection: Faster R-CNN + Config: configs/faster_rcnn/faster_rcnn_r50_fpn_mstrain_3x_coco.py + Metadata: + Training Memory (GB): 3.9 + Epochs: 36 + Results: + - Task: Object Detection + Dataset: COCO + Metrics: + box AP: 40.3 + Weights: https://download.openmmlab.com/mmdetection/v2.0/faster_rcnn/faster_rcnn_r50_fpn_mstrain_3x_coco/faster_rcnn_r50_fpn_mstrain_3x_coco_20210524_110822-e10bd31c.pth + + - Name: faster_rcnn_r101_caffe_fpn_mstrain_3x_coco + In Collection: Faster R-CNN + Config: configs/faster_rcnn/faster_rcnn_r101_caffe_fpn_mstrain_3x_coco.py + Metadata: + Training Memory (GB): 5.6 + Epochs: 36 + Results: + - Task: Object Detection + Dataset: COCO + Metrics: + box AP: 42.0 + Weights: https://download.openmmlab.com/mmdetection/v2.0/faster_rcnn/faster_rcnn_r101_caffe_fpn_mstrain_3x_coco/faster_rcnn_r101_caffe_fpn_mstrain_3x_coco_20210526_095742-a7ae426d.pth + + - Name: faster_rcnn_r101_fpn_mstrain_3x_coco + In Collection: Faster R-CNN + Config: configs/faster_rcnn/faster_rcnn_r101_fpn_mstrain_3x_coco.py + Metadata: + Training Memory (GB): 5.8 + Epochs: 36 + Results: + - Task: Object Detection + Dataset: COCO + Metrics: + box AP: 41.8 + Weights: https://download.openmmlab.com/mmdetection/v2.0/faster_rcnn/faster_rcnn_r101_fpn_mstrain_3x_coco/faster_rcnn_r101_fpn_mstrain_3x_coco_20210524_110822-4d4d2ca8.pth + + - Name: faster_rcnn_x101_32x4d_fpn_mstrain_3x_coco + In Collection: Faster R-CNN + Config: configs/faster_rcnn/faster_rcnn_x101_32x4d_fpn_mstrain_3x_coco.py + Metadata: + Training Memory (GB): 7.0 + Epochs: 36 + Results: + - Task: Object Detection + Dataset: COCO + Metrics: + box AP: 42.5 + Weights: https://download.openmmlab.com/mmdetection/v2.0/faster_rcnn/faster_rcnn_x101_32x4d_fpn_mstrain_3x_coco/faster_rcnn_x101_32x4d_fpn_mstrain_3x_coco_20210524_124151-16b9b260.pth + + - Name: faster_rcnn_x101_32x8d_fpn_mstrain_3x_coco + In Collection: Faster R-CNN + Config: configs/faster_rcnn/faster_rcnn_x101_32x8d_fpn_mstrain_3x_coco.py + Metadata: + Training Memory (GB): 10.1 + Epochs: 36 + Results: + - Task: Object Detection + Dataset: COCO + Metrics: + box AP: 42.4 + Weights: https://download.openmmlab.com/mmdetection/v2.0/faster_rcnn/faster_rcnn_x101_32x8d_fpn_mstrain_3x_coco/faster_rcnn_x101_32x8d_fpn_mstrain_3x_coco_20210604_182954-002e082a.pth + + - Name: faster_rcnn_x101_64x4d_fpn_mstrain_3x_coco + In Collection: Faster R-CNN + Config: configs/faster_rcnn/faster_rcnn_x101_64x4d_fpn_mstrain_3x_coco.py + Metadata: + Training Memory (GB): 10.0 + Epochs: 36 + Results: + - Task: Object Detection + Dataset: COCO + Metrics: + box AP: 43.1 + Weights: https://download.openmmlab.com/mmdetection/v2.0/faster_rcnn/faster_rcnn_x101_64x4d_fpn_mstrain_3x_coco/faster_rcnn_x101_64x4d_fpn_mstrain_3x_coco_20210524_124528-26c63de6.pth diff --git a/configs/fcos/README.md b/configs/fcos/README.md index c6209d2..ae5470a 100644 --- a/configs/fcos/README.md +++ b/configs/fcos/README.md @@ -2,7 +2,7 @@ ## Introduction -[ALGORITHM] + ```latex @article{tian2019fcos, diff --git a/configs/fcos/fcos_center-normbbox-centeronreg-giou_r50_caffe_fpn_gn-head_1x_coco.py b/configs/fcos/fcos_center-normbbox-centeronreg-giou_r50_caffe_fpn_gn-head_1x_coco.py index c25561e..2699bdb 100644 --- a/configs/fcos/fcos_center-normbbox-centeronreg-giou_r50_caffe_fpn_gn-head_1x_coco.py +++ b/configs/fcos/fcos_center-normbbox-centeronreg-giou_r50_caffe_fpn_gn-head_1x_coco.py @@ -1,7 +1,10 @@ _base_ = 'fcos_r50_caffe_fpn_gn-head_1x_coco.py' model = dict( - pretrained='open-mmlab://detectron2/resnet50_caffe', + backbone=dict( + init_cfg=dict( + type='Pretrained', + checkpoint='open-mmlab://detectron2/resnet50_caffe')), bbox_head=dict( norm_on_bbox=True, centerness_on_reg=True, diff --git a/configs/fcos/fcos_center-normbbox-centeronreg-giou_r50_caffe_fpn_gn-head_dcn_1x_coco.py b/configs/fcos/fcos_center-normbbox-centeronreg-giou_r50_caffe_fpn_gn-head_dcn_1x_coco.py index 72b90f8..cf93c91 100644 --- a/configs/fcos/fcos_center-normbbox-centeronreg-giou_r50_caffe_fpn_gn-head_dcn_1x_coco.py +++ b/configs/fcos/fcos_center-normbbox-centeronreg-giou_r50_caffe_fpn_gn-head_dcn_1x_coco.py @@ -1,10 +1,12 @@ _base_ = 'fcos_r50_caffe_fpn_gn-head_1x_coco.py' model = dict( - pretrained='open-mmlab://detectron2/resnet50_caffe', backbone=dict( dcn=dict(type='DCNv2', deform_groups=1, fallback_on_stride=False), - stage_with_dcn=(False, True, True, True)), + stage_with_dcn=(False, True, True, True), + init_cfg=dict( + type='Pretrained', + checkpoint='open-mmlab://detectron2/resnet50_caffe')), bbox_head=dict( norm_on_bbox=True, centerness_on_reg=True, diff --git a/configs/fcos/fcos_r101_caffe_fpn_gn-head_1x_coco.py b/configs/fcos/fcos_r101_caffe_fpn_gn-head_1x_coco.py index 6c38266..45bea48 100644 --- a/configs/fcos/fcos_r101_caffe_fpn_gn-head_1x_coco.py +++ b/configs/fcos/fcos_r101_caffe_fpn_gn-head_1x_coco.py @@ -1,4 +1,7 @@ _base_ = './fcos_r50_caffe_fpn_gn-head_1x_coco.py' model = dict( - pretrained='open-mmlab://detectron/resnet101_caffe', - backbone=dict(depth=101)) + backbone=dict( + depth=101, + init_cfg=dict( + type='Pretrained', + checkpoint='open-mmlab://detectron/resnet101_caffe'))) diff --git a/configs/fcos/fcos_r101_caffe_fpn_gn-head_mstrain_640-800_2x_coco.py b/configs/fcos/fcos_r101_caffe_fpn_gn-head_mstrain_640-800_2x_coco.py index 81f61c6..f4d36f1 100644 --- a/configs/fcos/fcos_r101_caffe_fpn_gn-head_mstrain_640-800_2x_coco.py +++ b/configs/fcos/fcos_r101_caffe_fpn_gn-head_mstrain_640-800_2x_coco.py @@ -1,7 +1,10 @@ _base_ = './fcos_r50_caffe_fpn_gn-head_1x_coco.py' model = dict( - pretrained='open-mmlab://detectron/resnet101_caffe', - backbone=dict(depth=101)) + backbone=dict( + depth=101, + init_cfg=dict( + type='Pretrained', + checkpoint='open-mmlab://detectron/resnet101_caffe'))) img_norm_cfg = dict( mean=[102.9801, 115.9465, 122.7717], std=[1.0, 1.0, 1.0], to_rgb=False) train_pipeline = [ diff --git a/configs/fcos/fcos_r50_caffe_fpn_gn-head_1x_coco.py b/configs/fcos/fcos_r50_caffe_fpn_gn-head_1x_coco.py index 6e12411..955787b 100644 --- a/configs/fcos/fcos_r50_caffe_fpn_gn-head_1x_coco.py +++ b/configs/fcos/fcos_r50_caffe_fpn_gn-head_1x_coco.py @@ -5,7 +5,6 @@ # model settings model = dict( type='FCOS', - pretrained='open-mmlab://detectron/resnet50_caffe', backbone=dict( type='ResNet', depth=50, @@ -14,14 +13,16 @@ frozen_stages=1, norm_cfg=dict(type='BN', requires_grad=False), norm_eval=True, - style='caffe'), + style='caffe', + init_cfg=dict( + type='Pretrained', + checkpoint='open-mmlab://detectron/resnet50_caffe')), neck=dict( type='FPN', in_channels=[256, 512, 1024, 2048], out_channels=256, start_level=1, - add_extra_convs=True, - extra_convs_on_inputs=False, # use P5 + add_extra_convs='on_output', # use P5 num_outs=5, relu_before_extra_convs=True), bbox_head=dict( diff --git a/configs/fcos/fcos_x101_64x4d_fpn_gn-head_mstrain_640-800_2x_coco.py b/configs/fcos/fcos_x101_64x4d_fpn_gn-head_mstrain_640-800_2x_coco.py index fc576f6..e70e465 100644 --- a/configs/fcos/fcos_x101_64x4d_fpn_gn-head_mstrain_640-800_2x_coco.py +++ b/configs/fcos/fcos_x101_64x4d_fpn_gn-head_mstrain_640-800_2x_coco.py @@ -1,6 +1,5 @@ _base_ = './fcos_r50_caffe_fpn_gn-head_1x_coco.py' model = dict( - pretrained='open-mmlab://resnext101_64x4d', backbone=dict( type='ResNeXt', depth=101, @@ -11,7 +10,9 @@ frozen_stages=1, norm_cfg=dict(type='BN', requires_grad=True), norm_eval=True, - style='pytorch')) + style='pytorch', + init_cfg=dict( + type='Pretrained', checkpoint='open-mmlab://resnext101_64x4d'))) img_norm_cfg = dict( mean=[123.675, 116.28, 103.53], std=[58.395, 57.12, 57.375], to_rgb=True) train_pipeline = [ diff --git a/configs/fcos/metafile.yml b/configs/fcos/metafile.yml new file mode 100644 index 0000000..c0d6f93 --- /dev/null +++ b/configs/fcos/metafile.yml @@ -0,0 +1,146 @@ +Collections: + - Name: FCOS + Metadata: + Training Data: COCO + Training Techniques: + - SGD with Momentum + - Weight Decay + Training Resources: 8x V100 GPUs + Architecture: + - FPN + - Group Normalization + - ResNet + Paper: + URL: https://arxiv.org/abs/1904.01355 + Title: 'FCOS: Fully Convolutional One-Stage Object Detection' + README: configs/fcos/README.md + Code: + URL: https://github.com/open-mmlab/mmdetection/blob/v2.0.0/mmdet/models/detectors/fcos.py#L6 + Version: v2.0.0 + +Models: + - Name: fcos_r50_caffe_fpn_gn-head_1x_coco + In Collection: FCOS + Config: configs/fcos/fcos_r50_caffe_fpn_gn-head_1x_coco.py + Metadata: + Training Memory (GB): 3.6 + inference time (ms/im): + - value: 44.05 + hardware: V100 + backend: PyTorch + batch size: 1 + mode: FP32 + resolution: (800, 1333) + Epochs: 12 + Results: + - Task: Object Detection + Dataset: COCO + Metrics: + box AP: 36.6 + Weights: https://openmmlab.oss-cn-hangzhou.aliyuncs.com/mmdetection/v2.0/fcos/fcos_r50_caffe_fpn_gn-head_1x_coco/fcos_r50_caffe_fpn_gn-head_1x_coco-821213aa.pth + + - Name: fcos_center-normbbox-centeronreg-giou_r50_caffe_fpn_gn-head_1x_coco + In Collection: FCOS + Config: configs/fcos/fcos_center-normbbox-centeronreg-giou_r50_caffe_fpn_gn-head_1x_coco.py + Metadata: + Training Memory (GB): 3.7 + Epochs: 12 + Results: + - Task: Object Detection + Dataset: COCO + Metrics: + box AP: 38.7 + Weights: https://openmmlab.oss-cn-hangzhou.aliyuncs.com/mmdetection/v2.0/fcos/fcos_center-normbbox-centeronreg-giou_r50_caffe_fpn_gn-head_1x_coco/fcos_center-normbbox-centeronreg-giou_r50_caffe_fpn_gn-head_1x_coco-0a0d75a8.pth + + - Name: fcos_center-normbbox-centeronreg-giou_r50_caffe_fpn_gn-head_dcn_1x_coco + In Collection: FCOS + Config: configs/fcos/fcos_center-normbbox-centeronreg-giou_r50_caffe_fpn_gn-head_dcn_1x_coco.py + Metadata: + Training Memory (GB): 3.8 + Epochs: 12 + Results: + - Task: Object Detection + Dataset: COCO + Metrics: + box AP: 42.3 + Weights: https://openmmlab.oss-cn-hangzhou.aliyuncs.com/mmdetection/v2.0/fcos/fcos_center-normbbox-centeronreg-giou_r50_caffe_fpn_gn-head_dcn_1x_coco/fcos_center-normbbox-centeronreg-giou_r50_caffe_fpn_gn-head_dcn_1x_coco-ae4d8b3d.pth + + - Name: fcos_r101_caffe_fpn_gn-head_1x_coco + In Collection: FCOS + Config: configs/fcos/fcos_r101_caffe_fpn_gn-head_1x_coco.py + Metadata: + Training Memory (GB): 5.5 + inference time (ms/im): + - value: 57.8 + hardware: V100 + backend: PyTorch + batch size: 1 + mode: FP32 + resolution: (800, 1333) + Epochs: 12 + Results: + - Task: Object Detection + Dataset: COCO + Metrics: + box AP: 39.1 + Weights: https://openmmlab.oss-cn-hangzhou.aliyuncs.com/mmdetection/v2.0/fcos/fcos_r101_caffe_fpn_gn-head_1x_coco/fcos_r101_caffe_fpn_gn-head_1x_coco-0e37b982.pth + + - Name: fcos_r50_caffe_fpn_gn-head_mstrain_640-800_2x_coco + In Collection: FCOS + Config: configs/fcos/fcos_r50_caffe_fpn_gn-head_mstrain_640-800_2x_coco.py + Metadata: + Training Memory (GB): 2.6 + inference time (ms/im): + - value: 43.67 + hardware: V100 + backend: PyTorch + batch size: 1 + mode: FP32 + resolution: (800, 1333) + Epochs: 24 + Results: + - Task: Object Detection + Dataset: COCO + Metrics: + box AP: 38.5 + Weights: https://openmmlab.oss-cn-hangzhou.aliyuncs.com/mmdetection/v2.0/fcos/fcos_r50_caffe_fpn_gn-head_mstrain_640-800_2x_coco/fcos_r50_caffe_fpn_gn-head_mstrain_640-800_2x_coco-d92ceeea.pth + + - Name: fcos_r101_caffe_fpn_gn-head_mstrain_640-800_2x_coco + In Collection: FCOS + Config: configs/fcos/fcos_r101_caffe_fpn_gn-head_mstrain_640-800_2x_coco.py + Metadata: + Training Memory (GB): 5.5 + inference time (ms/im): + - value: 57.8 + hardware: V100 + backend: PyTorch + batch size: 1 + mode: FP32 + resolution: (800, 1333) + Epochs: 24 + Results: + - Task: Object Detection + Dataset: COCO + Metrics: + box AP: 40.8 + Weights: https://openmmlab.oss-cn-hangzhou.aliyuncs.com/mmdetection/v2.0/fcos/fcos_r101_caffe_fpn_gn-head_mstrain_640-800_2x_coco/fcos_r101_caffe_fpn_gn-head_mstrain_640-800_2x_coco-511424d6.pth + + - Name: fcos_x101_64x4d_fpn_gn-head_mstrain_640-800_2x_coco + In Collection: FCOS + Config: configs/fcos/fcos_x101_64x4d_fpn_gn-head_mstrain_640-800_2x_coco.py + Metadata: + Training Memory (GB): 10.0 + inference time (ms/im): + - value: 103.09 + hardware: V100 + backend: PyTorch + batch size: 1 + mode: FP32 + resolution: (800, 1333) + Epochs: 24 + Results: + - Task: Object Detection + Dataset: COCO + Metrics: + box AP: 42.6 + Weights: https://openmmlab.oss-cn-hangzhou.aliyuncs.com/mmdetection/v2.0/fcos/fcos_x101_64x4d_fpn_gn-head_mstrain_640-800_2x_coco/fcos_x101_64x4d_fpn_gn-head_mstrain_640-800_2x_coco-ede514a8.pth diff --git a/configs/foveabox/README.md b/configs/foveabox/README.md index 91a43c9..b9b5684 100644 --- a/configs/foveabox/README.md +++ b/configs/foveabox/README.md @@ -1,6 +1,6 @@ # FoveaBox: Beyond Anchor-based Object Detector -[ALGORITHM] + FoveaBox is an accurate, flexible and completely anchor-free object detection system for object detection framework, as presented in our paper [https://arxiv.org/abs/1904.03797](https://arxiv.org/abs/1904.03797): Different from previous anchor-based methods, FoveaBox directly learns the object existing possibility and the bounding box coordinates without anchor reference. This is achieved by: (a) predicting category-sensitive semantic maps for the object existing possibility, and (b) producing category-agnostic bounding box for each position that potentially contains an object. @@ -11,14 +11,14 @@ Different from previous anchor-based methods, FoveaBox directly learns the objec | Backbone | Style | align | ms-train| Lr schd | Mem (GB) | Inf time (fps) | box AP | Config | Download | |:---------:|:-------:|:-------:|:-------:|:-------:|:--------:|:--------------:|:------:|:------:|:--------:| -| R-50 | pytorch | N | N | 1x | 5.6 | 24.1 | 36.5 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/foveabox/fovea_r50_fpn_4x4_1x_coco.py) | [model](http://download.openmmlab.com/mmdetection/v2.0/foveabox/fovea_r50_fpn_4x4_1x_coco/fovea_r50_fpn_4x4_1x_coco_20200219-ee4d5303.pth) | [log](http://download.openmmlab.com/mmdetection/v2.0/foveabox/fovea_r50_fpn_4x4_1x_coco/fovea_r50_fpn_4x4_1x_coco_20200219_223025.log.json) | -| R-50 | pytorch | N | N | 2x | 5.6 | - | 37.2 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/foveabox/fovea_r50_fpn_4x4_2x_coco.py) | [model](http://download.openmmlab.com/mmdetection/v2.0/foveabox/fovea_r50_fpn_4x4_2x_coco/fovea_r50_fpn_4x4_2x_coco_20200203-2df792b1.pth) | [log](http://download.openmmlab.com/mmdetection/v2.0/foveabox/fovea_r50_fpn_4x4_2x_coco/fovea_r50_fpn_4x4_2x_coco_20200203_112043.log.json) | -| R-50 | pytorch | Y | N | 2x | 8.1 | 19.4 | 37.9 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/foveabox/fovea_align_r50_fpn_gn-head_4x4_2x_coco.py) | [model](http://download.openmmlab.com/mmdetection/v2.0/foveabox/fovea_align_r50_fpn_gn-head_4x4_2x_coco/fovea_align_r50_fpn_gn-head_4x4_2x_coco_20200203-8987880d.pth) | [log](http://download.openmmlab.com/mmdetection/v2.0/foveabox/fovea_align_r50_fpn_gn-head_4x4_2x_coco/fovea_align_r50_fpn_gn-head_4x4_2x_coco_20200203_134252.log.json) | -| R-50 | pytorch | Y | Y | 2x | 8.1 | 18.3 | 40.4 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/foveabox/fovea_align_r50_fpn_gn-head_mstrain_640-800_4x4_2x_coco.py) | [model](http://download.openmmlab.com/mmdetection/v2.0/foveabox/fovea_align_r50_fpn_gn-head_mstrain_640-800_4x4_2x_coco/fovea_align_r50_fpn_gn-head_mstrain_640-800_4x4_2x_coco_20200205-85ce26cb.pth) | [log](http://download.openmmlab.com/mmdetection/v2.0/foveabox/fovea_align_r50_fpn_gn-head_mstrain_640-800_4x4_2x_coco/fovea_align_r50_fpn_gn-head_mstrain_640-800_4x4_2x_coco_20200205_112557.log.json) | -| R-101 | pytorch | N | N | 1x | 9.2 | 17.4 | 38.6 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/foveabox/fovea_r101_fpn_4x4_1x_coco.py) | [model](http://download.openmmlab.com/mmdetection/v2.0/foveabox/fovea_r101_fpn_4x4_1x_coco/fovea_r101_fpn_4x4_1x_coco_20200219-05e38f1c.pth) | [log](http://download.openmmlab.com/mmdetection/v2.0/foveabox/fovea_r101_fpn_4x4_1x_coco/fovea_r101_fpn_4x4_1x_coco_20200219_011740.log.json) | -| R-101 | pytorch | N | N | 2x | 11.7 | - | 40.0 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/foveabox/fovea_r101_fpn_4x4_2x_coco.py) | [model](http://download.openmmlab.com/mmdetection/v2.0/foveabox/fovea_r101_fpn_4x4_2x_coco/fovea_r101_fpn_4x4_2x_coco_20200208-02320ea4.pth) | [log](http://download.openmmlab.com/mmdetection/v2.0/foveabox/fovea_r101_fpn_4x4_2x_coco/fovea_r101_fpn_4x4_2x_coco_20200208_202059.log.json) | -| R-101 | pytorch | Y | N | 2x | 11.7 | 14.7 | 40.0 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/foveabox/fovea_align_r101_fpn_gn-head_4x4_2x_coco.py) | [model](http://download.openmmlab.com/mmdetection/v2.0/foveabox/fovea_align_r101_fpn_gn-head_4x4_2x_coco/fovea_align_r101_fpn_gn-head_4x4_2x_coco_20200208-c39a027a.pth) | [log](http://download.openmmlab.com/mmdetection/v2.0/foveabox/fovea_align_r101_fpn_gn-head_4x4_2x_coco/fovea_align_r101_fpn_gn-head_4x4_2x_coco_20200208_203337.log.json) | -| R-101 | pytorch | Y | Y | 2x | 11.7 | 14.7 | 42.0 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/foveabox/fovea_align_r101_fpn_gn-head_mstrain_640-800_4x4_2x_coco.py) | [model](http://download.openmmlab.com/mmdetection/v2.0/foveabox/fovea_align_r101_fpn_gn-head_mstrain_640-800_4x4_2x_coco/fovea_align_r101_fpn_gn-head_mstrain_640-800_4x4_2x_coco_20200208-649c5eb6.pth) | [log](http://download.openmmlab.com/mmdetection/v2.0/foveabox/fovea_align_r101_fpn_gn-head_mstrain_640-800_4x4_2x_coco/fovea_align_r101_fpn_gn-head_mstrain_640-800_4x4_2x_coco_20200208_202124.log.json) | +| R-50 | pytorch | N | N | 1x | 5.6 | 24.1 | 36.5 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/foveabox/fovea_r50_fpn_4x4_1x_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/foveabox/fovea_r50_fpn_4x4_1x_coco/fovea_r50_fpn_4x4_1x_coco_20200219-ee4d5303.pth) | [log](https://download.openmmlab.com/mmdetection/v2.0/foveabox/fovea_r50_fpn_4x4_1x_coco/fovea_r50_fpn_4x4_1x_coco_20200219_223025.log.json) | +| R-50 | pytorch | N | N | 2x | 5.6 | - | 37.2 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/foveabox/fovea_r50_fpn_4x4_2x_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/foveabox/fovea_r50_fpn_4x4_2x_coco/fovea_r50_fpn_4x4_2x_coco_20200203-2df792b1.pth) | [log](https://download.openmmlab.com/mmdetection/v2.0/foveabox/fovea_r50_fpn_4x4_2x_coco/fovea_r50_fpn_4x4_2x_coco_20200203_112043.log.json) | +| R-50 | pytorch | Y | N | 2x | 8.1 | 19.4 | 37.9 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/foveabox/fovea_align_r50_fpn_gn-head_4x4_2x_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/foveabox/fovea_align_r50_fpn_gn-head_4x4_2x_coco/fovea_align_r50_fpn_gn-head_4x4_2x_coco_20200203-8987880d.pth) | [log](https://download.openmmlab.com/mmdetection/v2.0/foveabox/fovea_align_r50_fpn_gn-head_4x4_2x_coco/fovea_align_r50_fpn_gn-head_4x4_2x_coco_20200203_134252.log.json) | +| R-50 | pytorch | Y | Y | 2x | 8.1 | 18.3 | 40.4 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/foveabox/fovea_align_r50_fpn_gn-head_mstrain_640-800_4x4_2x_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/foveabox/fovea_align_r50_fpn_gn-head_mstrain_640-800_4x4_2x_coco/fovea_align_r50_fpn_gn-head_mstrain_640-800_4x4_2x_coco_20200205-85ce26cb.pth) | [log](https://download.openmmlab.com/mmdetection/v2.0/foveabox/fovea_align_r50_fpn_gn-head_mstrain_640-800_4x4_2x_coco/fovea_align_r50_fpn_gn-head_mstrain_640-800_4x4_2x_coco_20200205_112557.log.json) | +| R-101 | pytorch | N | N | 1x | 9.2 | 17.4 | 38.6 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/foveabox/fovea_r101_fpn_4x4_1x_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/foveabox/fovea_r101_fpn_4x4_1x_coco/fovea_r101_fpn_4x4_1x_coco_20200219-05e38f1c.pth) | [log](https://download.openmmlab.com/mmdetection/v2.0/foveabox/fovea_r101_fpn_4x4_1x_coco/fovea_r101_fpn_4x4_1x_coco_20200219_011740.log.json) | +| R-101 | pytorch | N | N | 2x | 11.7 | - | 40.0 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/foveabox/fovea_r101_fpn_4x4_2x_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/foveabox/fovea_r101_fpn_4x4_2x_coco/fovea_r101_fpn_4x4_2x_coco_20200208-02320ea4.pth) | [log](https://download.openmmlab.com/mmdetection/v2.0/foveabox/fovea_r101_fpn_4x4_2x_coco/fovea_r101_fpn_4x4_2x_coco_20200208_202059.log.json) | +| R-101 | pytorch | Y | N | 2x | 11.7 | 14.7 | 40.0 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/foveabox/fovea_align_r101_fpn_gn-head_4x4_2x_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/foveabox/fovea_align_r101_fpn_gn-head_4x4_2x_coco/fovea_align_r101_fpn_gn-head_4x4_2x_coco_20200208-c39a027a.pth) | [log](https://download.openmmlab.com/mmdetection/v2.0/foveabox/fovea_align_r101_fpn_gn-head_4x4_2x_coco/fovea_align_r101_fpn_gn-head_4x4_2x_coco_20200208_203337.log.json) | +| R-101 | pytorch | Y | Y | 2x | 11.7 | 14.7 | 42.0 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/foveabox/fovea_align_r101_fpn_gn-head_mstrain_640-800_4x4_2x_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/foveabox/fovea_align_r101_fpn_gn-head_mstrain_640-800_4x4_2x_coco/fovea_align_r101_fpn_gn-head_mstrain_640-800_4x4_2x_coco_20200208-649c5eb6.pth) | [log](https://download.openmmlab.com/mmdetection/v2.0/foveabox/fovea_align_r101_fpn_gn-head_mstrain_640-800_4x4_2x_coco/fovea_align_r101_fpn_gn-head_mstrain_640-800_4x4_2x_coco_20200208_202124.log.json) | [1] *1x and 2x mean the model is trained for 12 and 24 epochs, respectively.* \ [2] *Align means utilizing deformable convolution to align the cls branch.* \ diff --git a/configs/foveabox/fovea_align_r101_fpn_gn-head_4x4_2x_coco.py b/configs/foveabox/fovea_align_r101_fpn_gn-head_4x4_2x_coco.py index 30dca04..c5d1784 100644 --- a/configs/foveabox/fovea_align_r101_fpn_gn-head_4x4_2x_coco.py +++ b/configs/foveabox/fovea_align_r101_fpn_gn-head_4x4_2x_coco.py @@ -1,7 +1,9 @@ _base_ = './fovea_r50_fpn_4x4_1x_coco.py' model = dict( - pretrained='torchvision://resnet101', - backbone=dict(depth=101), + backbone=dict( + depth=101, + init_cfg=dict(type='Pretrained', + checkpoint='torchvision://resnet101')), bbox_head=dict( with_deform=True, norm_cfg=dict(type='GN', num_groups=32, requires_grad=True))) diff --git a/configs/foveabox/fovea_align_r101_fpn_gn-head_mstrain_640-800_4x4_2x_coco.py b/configs/foveabox/fovea_align_r101_fpn_gn-head_mstrain_640-800_4x4_2x_coco.py index a02a814..cc5affe 100644 --- a/configs/foveabox/fovea_align_r101_fpn_gn-head_mstrain_640-800_4x4_2x_coco.py +++ b/configs/foveabox/fovea_align_r101_fpn_gn-head_mstrain_640-800_4x4_2x_coco.py @@ -1,7 +1,9 @@ _base_ = './fovea_r50_fpn_4x4_1x_coco.py' model = dict( - pretrained='torchvision://resnet101', - backbone=dict(depth=101), + backbone=dict( + depth=101, + init_cfg=dict(type='Pretrained', + checkpoint='torchvision://resnet101')), bbox_head=dict( with_deform=True, norm_cfg=dict(type='GN', num_groups=32, requires_grad=True))) diff --git a/configs/foveabox/fovea_r101_fpn_4x4_1x_coco.py b/configs/foveabox/fovea_r101_fpn_4x4_1x_coco.py index 907bede..9201af1 100644 --- a/configs/foveabox/fovea_r101_fpn_4x4_1x_coco.py +++ b/configs/foveabox/fovea_r101_fpn_4x4_1x_coco.py @@ -1,2 +1,6 @@ _base_ = './fovea_r50_fpn_4x4_1x_coco.py' -model = dict(pretrained='torchvision://resnet101', backbone=dict(depth=101)) +model = dict( + backbone=dict( + depth=101, + init_cfg=dict(type='Pretrained', + checkpoint='torchvision://resnet101'))) diff --git a/configs/foveabox/fovea_r101_fpn_4x4_2x_coco.py b/configs/foveabox/fovea_r101_fpn_4x4_2x_coco.py index 9296393..1ef5243 100644 --- a/configs/foveabox/fovea_r101_fpn_4x4_2x_coco.py +++ b/configs/foveabox/fovea_r101_fpn_4x4_2x_coco.py @@ -1,2 +1,6 @@ _base_ = './fovea_r50_fpn_4x4_2x_coco.py' -model = dict(pretrained='torchvision://resnet101', backbone=dict(depth=101)) +model = dict( + backbone=dict( + depth=101, + init_cfg=dict(type='Pretrained', + checkpoint='torchvision://resnet101'))) diff --git a/configs/foveabox/fovea_r50_fpn_4x4_1x_coco.py b/configs/foveabox/fovea_r50_fpn_4x4_1x_coco.py index fd39257..7e986eb 100644 --- a/configs/foveabox/fovea_r50_fpn_4x4_1x_coco.py +++ b/configs/foveabox/fovea_r50_fpn_4x4_1x_coco.py @@ -5,7 +5,6 @@ # model settings model = dict( type='FOVEA', - pretrained='torchvision://resnet50', backbone=dict( type='ResNet', depth=50, @@ -14,7 +13,8 @@ frozen_stages=1, norm_cfg=dict(type='BN', requires_grad=True), norm_eval=True, - style='pytorch'), + style='pytorch', + init_cfg=dict(type='Pretrained', checkpoint='torchvision://resnet50')), neck=dict( type='FPN', in_channels=[256, 512, 1024, 2048], diff --git a/configs/foveabox/metafile.yml b/configs/foveabox/metafile.yml new file mode 100644 index 0000000..fe9a283 --- /dev/null +++ b/configs/foveabox/metafile.yml @@ -0,0 +1,172 @@ +Collections: + - Name: FoveaBox + Metadata: + Training Data: COCO + Training Techniques: + - SGD with Momentum + - Weight Decay + Training Resources: 4x V100 GPUs + Architecture: + - FPN + - ResNet + Paper: + URL: https://arxiv.org/abs/1904.03797 + Title: 'FoveaBox: Beyond Anchor-based Object Detector' + README: configs/foveabox/README.md + Code: + URL: https://github.com/open-mmlab/mmdetection/blob/v2.0.0/mmdet/models/detectors/fovea.py#L6 + Version: v2.0.0 + +Models: + - Name: fovea_r50_fpn_4x4_1x_coco + In Collection: FoveaBox + Config: configs/foveabox/fovea_r50_fpn_4x4_1x_coco.py + Metadata: + Training Memory (GB): 5.6 + inference time (ms/im): + - value: 41.49 + hardware: V100 + backend: PyTorch + batch size: 1 + mode: FP32 + resolution: (800, 1333) + Epochs: 12 + Results: + - Task: Object Detection + Dataset: COCO + Metrics: + box AP: 36.5 + Weights: https://download.openmmlab.com/mmdetection/v2.0/foveabox/fovea_r50_fpn_4x4_1x_coco/fovea_r50_fpn_4x4_1x_coco_20200219-ee4d5303.pth + + - Name: fovea_r50_fpn_4x4_2x_coco + In Collection: FoveaBox + Config: configs/foveabox/fovea_r50_fpn_4x4_2x_coco.py + Metadata: + Training Memory (GB): 5.6 + inference time (ms/im): + - value: 41.49 + hardware: V100 + backend: PyTorch + batch size: 1 + mode: FP32 + resolution: (800, 1333) + Epochs: 24 + Results: + - Task: Object Detection + Dataset: COCO + Metrics: + box AP: 37.2 + Weights: https://download.openmmlab.com/mmdetection/v2.0/foveabox/fovea_r50_fpn_4x4_2x_coco/fovea_r50_fpn_4x4_2x_coco_20200203-2df792b1.pth + + - Name: fovea_align_r50_fpn_gn-head_4x4_2x_coco + In Collection: FoveaBox + Config: configs/foveabox/fovea_align_r50_fpn_gn-head_4x4_2x_coco.py + Metadata: + Training Memory (GB): 8.1 + inference time (ms/im): + - value: 51.55 + hardware: V100 + backend: PyTorch + batch size: 1 + mode: FP32 + resolution: (800, 1333) + Epochs: 24 + Results: + - Task: Object Detection + Dataset: COCO + Metrics: + box AP: 37.9 + Weights: https://download.openmmlab.com/mmdetection/v2.0/foveabox/fovea_align_r50_fpn_gn-head_4x4_2x_coco/fovea_align_r50_fpn_gn-head_4x4_2x_coco_20200203-8987880d.pth + + - Name: fovea_align_r50_fpn_gn-head_mstrain_640-800_4x4_2x_coco + In Collection: FoveaBox + Config: configs/foveabox/fovea_align_r50_fpn_gn-head_mstrain_640-800_4x4_2x_coco.py + Metadata: + Training Memory (GB): 8.1 + inference time (ms/im): + - value: 54.64 + hardware: V100 + backend: PyTorch + batch size: 1 + mode: FP32 + resolution: (800, 1333) + Epochs: 24 + Results: + - Task: Object Detection + Dataset: COCO + Metrics: + box AP: 40.4 + Weights: https://download.openmmlab.com/mmdetection/v2.0/foveabox/fovea_align_r50_fpn_gn-head_mstrain_640-800_4x4_2x_coco/fovea_align_r50_fpn_gn-head_mstrain_640-800_4x4_2x_coco_20200205-85ce26cb.pth + + - Name: fovea_r101_fpn_4x4_1x_coco + In Collection: FoveaBox + Config: configs/foveabox/fovea_r101_fpn_4x4_1x_coco.py + Metadata: + Training Memory (GB): 9.2 + inference time (ms/im): + - value: 57.47 + hardware: V100 + backend: PyTorch + batch size: 1 + mode: FP32 + resolution: (800, 1333) + Epochs: 12 + Results: + - Task: Object Detection + Dataset: COCO + Metrics: + box AP: 38.6 + Weights: https://download.openmmlab.com/mmdetection/v2.0/foveabox/fovea_r101_fpn_4x4_1x_coco/fovea_r101_fpn_4x4_1x_coco_20200219-05e38f1c.pth + + - Name: fovea_r101_fpn_4x4_2x_coco + In Collection: FoveaBox + Config: configs/foveabox/fovea_r101_fpn_4x4_2x_coco.py + Metadata: + Training Memory (GB): 11.7 + Epochs: 24 + Results: + - Task: Object Detection + Dataset: COCO + Metrics: + box AP: 40.0 + Weights: https://download.openmmlab.com/mmdetection/v2.0/foveabox/fovea_r101_fpn_4x4_2x_coco/fovea_r101_fpn_4x4_2x_coco_20200208-02320ea4.pth + + - Name: fovea_align_r101_fpn_gn-head_4x4_2x_coco + In Collection: FoveaBox + Config: configs/foveabox/fovea_align_r101_fpn_gn-head_4x4_2x_coco.py + Metadata: + Training Memory (GB): 11.7 + inference time (ms/im): + - value: 68.03 + hardware: V100 + backend: PyTorch + batch size: 1 + mode: FP32 + resolution: (800, 1333) + Epochs: 24 + Results: + - Task: Object Detection + Dataset: COCO + Metrics: + box AP: 40.0 + Weights: https://download.openmmlab.com/mmdetection/v2.0/foveabox/fovea_align_r101_fpn_gn-head_4x4_2x_coco/fovea_align_r101_fpn_gn-head_4x4_2x_coco_20200208-c39a027a.pth + + - Name: fovea_align_r101_fpn_gn-head_mstrain_640-800_4x4_2x_coco + In Collection: FoveaBox + Config: configs/foveabox/fovea_align_r101_fpn_gn-head_mstrain_640-800_4x4_2x_coco.py + Metadata: + Training Memory (GB): 11.7 + inference time (ms/im): + - value: 68.03 + hardware: V100 + backend: PyTorch + batch size: 1 + mode: FP32 + resolution: (800, 1333) + Epochs: 24 + Results: + - Task: Object Detection + Dataset: COCO + Metrics: + box AP: 42.0 + Weights: https://download.openmmlab.com/mmdetection/v2.0/foveabox/fovea_align_r101_fpn_gn-head_mstrain_640-800_4x4_2x_coco/fovea_align_r101_fpn_gn-head_mstrain_640-800_4x4_2x_coco_20200208-649c5eb6.pth diff --git a/configs/fp16/README.md b/configs/fp16/README.md index 17eaa7d..171aa1b 100644 --- a/configs/fp16/README.md +++ b/configs/fp16/README.md @@ -2,7 +2,7 @@ ## Introduction -[OTHERS] + ```latex @article{micikevicius2017mixed, @@ -15,8 +15,10 @@ ## Results and Models -| Architecture | Backbone | Style | Lr schd | Mem (GB) | Inf time (fps) | box AP | mask AP | Config | Download | -|:------------:|:---------:|:-------:|:-------:|:--------:|:--------------:|:------:|:-------:|:------:|:--------:| -| Faster R-CNN | R-50 | pytorch | 1x | 3.4 | 28.8 | 37.5 | - |[config](https://github.com/open-mmlab/mmdetection/tree/master/configs/fp16/faster_rcnn_r50_fpn_fp16_1x_coco.py) | [model](http://download.openmmlab.com/mmdetection/v2.0/fp16/faster_rcnn_r50_fpn_fp16_1x_coco/faster_rcnn_r50_fpn_fp16_1x_coco_20200204-d4dc1471.pth) | [log](http://download.openmmlab.com/mmdetection/v2.0/fp16/faster_rcnn_r50_fpn_fp16_1x_coco/faster_rcnn_r50_fpn_fp16_1x_coco_20200204_143530.log.json) | -| Mask R-CNN | R-50 | pytorch | 1x | 3.6 | 24.1 | 38.1 | 34.7 |[config](https://github.com/open-mmlab/mmdetection/tree/master/configs/fp16/mask_rcnn_r50_fpn_fp16_1x_coco.py) | [model](http://download.openmmlab.com/mmdetection/v2.0/fp16/mask_rcnn_r50_fpn_fp16_1x_coco/mask_rcnn_r50_fpn_fp16_1x_coco_20200205-59faf7e4.pth) | [log](http://download.openmmlab.com/mmdetection/v2.0/fp16/mask_rcnn_r50_fpn_fp16_1x_coco/mask_rcnn_r50_fpn_fp16_1x_coco_20200205_130539.log.json) | -| Retinanet | R-50 | pytorch | 1x | 2.8 | 31.6 | 36.4 | |[config](https://github.com/open-mmlab/mmdetection/tree/master/configs/fp16/retinanet_r50_fpn_fp16_1x_coco.py) | [model](http://download.openmmlab.com/mmdetection/v2.0/fp16/retinanet_r50_fpn_fp16_1x_coco/retinanet_r50_fpn_fp16_1x_coco_20200702-0dbfb212.pth) | [log](http://download.openmmlab.com/mmdetection/v2.0/fp16/retinanet_r50_fpn_fp16_1x_coco/retinanet_r50_fpn_fp16_1x_coco_20200702_020127.log.json) | +| Architecture | Backbone | Style | Conv | Lr schd | Mem (GB) | Inf time (fps) | box AP | mask AP | Config | Download | +|:------------:|:---------:|:-------:|:------------:|:-------:|:--------:|:--------------:|:------:|:-------:|:------:|:--------:| +| Faster R-CNN | R-50 | pytorch | - | 1x | 3.4 | 28.8 | 37.5 | - |[config](https://github.com/open-mmlab/mmdetection/tree/master/configs/fp16/faster_rcnn_r50_fpn_fp16_1x_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/fp16/faster_rcnn_r50_fpn_fp16_1x_coco/faster_rcnn_r50_fpn_fp16_1x_coco_20200204-d4dc1471.pth) | [log](https://download.openmmlab.com/mmdetection/v2.0/fp16/faster_rcnn_r50_fpn_fp16_1x_coco/faster_rcnn_r50_fpn_fp16_1x_coco_20200204_143530.log.json) | +| Mask R-CNN | R-50 | pytorch | - | 1x | 3.6 | 24.1 | 38.1 | 34.7 |[config](https://github.com/open-mmlab/mmdetection/tree/master/configs/fp16/mask_rcnn_r50_fpn_fp16_1x_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/fp16/mask_rcnn_r50_fpn_fp16_1x_coco/mask_rcnn_r50_fpn_fp16_1x_coco_20200205-59faf7e4.pth) | [log](https://download.openmmlab.com/mmdetection/v2.0/fp16/mask_rcnn_r50_fpn_fp16_1x_coco/mask_rcnn_r50_fpn_fp16_1x_coco_20200205_130539.log.json) | +| Mask R-CNN | R-50 | pytorch | dconv(c3-c5) | 1x | 3.0 | | 41.9 | 37.5 |[config](https://github.com/open-mmlab/mmdetection/tree/master/configs/fp16/mask_rcnn_r50_fpn_fp16_dconv_c3-c5_1x_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/fp16/mask_rcnn_r50_fpn_fp16_dconv_c3-c5_1x_coco/mask_rcnn_r50_fpn_fp16_dconv_c3-c5_1x_coco_20210520_180247-c06429d2.pth) | [log](https://download.openmmlab.com/mmdetection/v2.0/fp16/mask_rcnn_r50_fpn_fp16_dconv_c3-c5_1x_coco/mask_rcnn_r50_fpn_fp16_dconv_c3-c5_1x_coco_20210520_180247.log.json) | +| Mask R-CNN | R-50 | pytorch | mdconv(c3-c5)| 1x | 3.1 | | 42.0 | 37.6 |[config](https://github.com/open-mmlab/mmdetection/tree/master/configs/fp16/mask_rcnn_r50_fpn_fp16_mdconv_c3-c5_1x_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/fp16/mask_rcnn_r50_fpn_fp16_mdconv_c3-c5_1x_coco/mask_rcnn_r50_fpn_fp16_mdconv_c3-c5_1x_coco_20210520_180434-cf8fefa5.pth) | [log](https://download.openmmlab.com/mmdetection/v2.0/fp16/mask_rcnn_r50_fpn_fp16_mdconv_c3-c5_1x_coco/mask_rcnn_r50_fpn_fp16_mdconv_c3-c5_1x_coco_20210520_180434.log.json) | +| Retinanet | R-50 | pytorch | - | 1x | 2.8 | 31.6 | 36.4 | |[config](https://github.com/open-mmlab/mmdetection/tree/master/configs/fp16/retinanet_r50_fpn_fp16_1x_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/fp16/retinanet_r50_fpn_fp16_1x_coco/retinanet_r50_fpn_fp16_1x_coco_20200702-0dbfb212.pth) | [log](https://download.openmmlab.com/mmdetection/v2.0/fp16/retinanet_r50_fpn_fp16_1x_coco/retinanet_r50_fpn_fp16_1x_coco_20200702_020127.log.json) | diff --git a/configs/fp16/mask_rcnn_r50_fpn_fp16_dconv_c3-c5_1x_coco.py b/configs/fp16/mask_rcnn_r50_fpn_fp16_dconv_c3-c5_1x_coco.py new file mode 100644 index 0000000..ee5cca7 --- /dev/null +++ b/configs/fp16/mask_rcnn_r50_fpn_fp16_dconv_c3-c5_1x_coco.py @@ -0,0 +1,7 @@ +_base_ = '../mask_rcnn/mask_rcnn_r50_fpn_1x_coco.py' +model = dict( + backbone=dict( + dcn=dict(type='DCN', deform_groups=1, fallback_on_stride=False), + stage_with_dcn=(False, True, True, True))) + +fp16 = dict(loss_scale=512.) diff --git a/configs/fp16/mask_rcnn_r50_fpn_fp16_mdconv_c3-c5_1x_coco.py b/configs/fp16/mask_rcnn_r50_fpn_fp16_mdconv_c3-c5_1x_coco.py new file mode 100644 index 0000000..7e21454 --- /dev/null +++ b/configs/fp16/mask_rcnn_r50_fpn_fp16_mdconv_c3-c5_1x_coco.py @@ -0,0 +1,7 @@ +_base_ = '../mask_rcnn/mask_rcnn_r50_fpn_1x_coco.py' +model = dict( + backbone=dict( + dcn=dict(type='DCNv2', deform_groups=1, fallback_on_stride=False), + stage_with_dcn=(False, True, True, True))) + +fp16 = dict(loss_scale=512.) diff --git a/configs/fp16/metafile.yml b/configs/fp16/metafile.yml new file mode 100644 index 0000000..70d6649 --- /dev/null +++ b/configs/fp16/metafile.yml @@ -0,0 +1,113 @@ +Collections: + - Name: FP16 + Metadata: + Training Data: COCO + Training Techniques: + - Mixed Precision Training + Training Resources: 8x V100 GPUs + Paper: + URL: https://arxiv.org/abs/1710.03740 + Title: 'Mixed Precision Training' + README: configs/fp16/README.md + Code: + URL: https://github.com/open-mmlab/mmdetection/blob/v2.0.0/mmdet/core/fp16/hooks.py#L11 + Version: v2.0.0 + +Models: + - Name: faster_rcnn_r50_fpn_fp16_1x_coco + In Collection: FP16 + Config: configs/fp16/faster_rcnn_r50_fpn_fp16_1x_coco.py + Metadata: + Training Memory (GB): 3.4 + inference time (ms/im): + - value: 34.72 + hardware: V100 + backend: PyTorch + batch size: 1 + mode: FP16 + resolution: (800, 1333) + Epochs: 12 + Results: + - Task: Object Detection + Dataset: COCO + Metrics: + box AP: 37.5 + Weights: https://download.openmmlab.com/mmdetection/v2.0/fp16/faster_rcnn_r50_fpn_fp16_1x_coco/faster_rcnn_r50_fpn_fp16_1x_coco_20200204-d4dc1471.pth + + - Name: mask_rcnn_r50_fpn_fp16_1x_coco + In Collection: FP16 + Config: configs/fp16/mask_rcnn_r50_fpn_fp16_1x_coco.py + Metadata: + Training Memory (GB): 3.6 + inference time (ms/im): + - value: 41.49 + hardware: V100 + backend: PyTorch + batch size: 1 + mode: FP16 + resolution: (800, 1333) + Epochs: 12 + Results: + - Task: Object Detection + Dataset: COCO + Metrics: + box AP: 38.1 + - Task: Instance Segmentation + Dataset: COCO + Metrics: + mask AP: 34.7 + Weights: https://download.openmmlab.com/mmdetection/v2.0/fp16/mask_rcnn_r50_fpn_fp16_1x_coco/mask_rcnn_r50_fpn_fp16_1x_coco_20200205-59faf7e4.pth + + - Name: mask_rcnn_r50_fpn_fp16_dconv_c3-c5_1x_coco + In Collection: FP16 + Config: configs/fp16/mask_rcnn_r50_fpn_fp16_dconv_c3-c5_1x_coco.py + Metadata: + Training Memory (GB): 3.0 + Epochs: 12 + Results: + - Task: Object Detection + Dataset: COCO + Metrics: + box AP: 41.9 + - Task: Instance Segmentation + Dataset: COCO + Metrics: + mask AP: 37.5 + Weights: https://download.openmmlab.com/mmdetection/v2.0/fp16/mask_rcnn_r50_fpn_fp16_dconv_c3-c5_1x_coco/mask_rcnn_r50_fpn_fp16_dconv_c3-c5_1x_coco_20210520_180247-c06429d2.pth + + - Name: mask_rcnn_r50_fpn_fp16_mdconv_c3-c5_1x_coco + In Collection: FP16 + Config: configs/fp16/mask_rcnn_r50_fpn_fp16_mdconv_c3-c5_1x_coco.py + Metadata: + Training Memory (GB): 3.1 + Epochs: 12 + Results: + - Task: Object Detection + Dataset: COCO + Metrics: + box AP: 42.0 + - Task: Instance Segmentation + Dataset: COCO + Metrics: + mask AP: 37.6 + Weights: https://download.openmmlab.com/mmdetection/v2.0/fp16/mask_rcnn_r50_fpn_fp16_mdconv_c3-c5_1x_coco/mask_rcnn_r50_fpn_fp16_mdconv_c3-c5_1x_coco_20210520_180434-cf8fefa5.pth + + - Name: retinanet_r50_fpn_fp16_1x_coco + In Collection: FP16 + Config: configs/fp16/retinanet_r50_fpn_fp16_1x_coco.py + Metadata: + Training Memory (GB): 2.8 + inference time (ms/im): + - value: 31.65 + hardware: V100 + backend: PyTorch + batch size: 1 + mode: FP16 + resolution: (800, 1333) + Epochs: 12 + Results: + - Task: Object Detection + Dataset: COCO + Metrics: + box AP: 36.4 + Weights: https://download.openmmlab.com/mmdetection/v2.0/fp16/retinanet_r50_fpn_fp16_1x_coco/retinanet_r50_fpn_fp16_1x_coco_20200702-0dbfb212.pth diff --git a/configs/fpg/README.md b/configs/fpg/README.md index 89f5adb..c0545da 100644 --- a/configs/fpg/README.md +++ b/configs/fpg/README.md @@ -1,4 +1,5 @@ # Feature Pyramid Grids + ## Introduction @@ -18,12 +19,12 @@ All backbones are Resnet-50 in pytorch style. | Method | Neck | Lr schd | Mem (GB) | Inf time (fps) | box AP | mask AP | Config | Download | |:------------:|:-----------:|:-------:|:--------:|:--------------:|:------:|:-------:|:-------:|:--------:| -| Faster R-CNN | FPG | 50e | 20.0 | - | 42.2 | - |[config](https://github.com/open-mmlab/mmdetection/tree/master/configs/fpg/faster_rcnn_r50_fpg_crop640_50e_coco.py) | -| Faster R-CNN | FPG-chn128 | 50e | 11.9 | - | 41.2 | - |[config](https://github.com/open-mmlab/mmdetection/tree/master/configs/fpg/faster_rcnn_r50_fpg-chn128_crop640_50e_coco.py) | -| Mask R-CNN | FPG | 50e | 23.2 | - | 42.7 | 37.8 |[config](https://github.com/open-mmlab/mmdetection/tree/master/configs/fpg/mask_rcnn_r50_fpg_crop640_50e_coco.py) | -| Mask R-CNN | FPG-chn128 | 50e | 15.3 | - | 41.7 | 36.9 |[config](https://github.com/open-mmlab/mmdetection/tree/master/configs/fpg/mask_rcnn_r50_fpg-chn128_crop640_50e_coco.py) | -| RetinaNet | FPG | 50e | 20.8 | - | 40.5 | - |[config](https://github.com/open-mmlab/mmdetection/tree/master/configs/fpg/retinanet_r50_fpg_crop640_50e_coco.py) | -| RetinaNet | FPG-chn128 | 50e | | - | | - |[config](https://github.com/open-mmlab/mmdetection/tree/master/configs/fpg/retinanet_r50_fpg-chn128_crop640_50e_coco.py) | +| Faster R-CNN | FPG | 50e | 20.0 | - | 42.2 | - |[config](https://github.com/open-mmlab/mmdetection/tree/master/configs/fpg/faster_rcnn_r50_fpg_crop640_50e_coco.py) |[model](https://download.openmmlab.com/mmdetection/v2.0/fpg/faster_rcnn_r50_fpg_crop640_50e_coco/faster_rcnn_r50_fpg_crop640_50e_coco-76220505.pth) | [log](https://download.openmmlab.com/mmdetection/v2.0/fpg/faster_rcnn_r50_fpg_crop640_50e_coco/20210218_223520.log.json) | +| Faster R-CNN | FPG-chn128 | 50e | 11.9 | - | 41.2 | - |[config](https://github.com/open-mmlab/mmdetection/tree/master/configs/fpg/faster_rcnn_r50_fpg-chn128_crop640_50e_coco.py) |[model](https://download.openmmlab.com/mmdetection/v2.0/fpg/faster_rcnn_r50_fpg-chn128_crop640_50e_coco/faster_rcnn_r50_fpg-chn128_crop640_50e_coco-24257de9.pth) | [log](https://download.openmmlab.com/mmdetection/v2.0/fpg/faster_rcnn_r50_fpg-chn128_crop640_50e_coco/20210218_221412.log.json) | +| Mask R-CNN | FPG | 50e | 23.2 | - | 42.7 | 37.8 |[config](https://github.com/open-mmlab/mmdetection/tree/master/configs/fpg/mask_rcnn_r50_fpg_crop640_50e_coco.py) |[model](https://download.openmmlab.com/mmdetection/v2.0/fpg/mask_rcnn_r50_fpg_crop640_50e_coco/mask_rcnn_r50_fpg_crop640_50e_coco-c5860453.pth) | [log](https://download.openmmlab.com/mmdetection/v2.0/fpg/mask_rcnn_r50_fpg_crop640_50e_coco/20210222_205447.log.json) | +| Mask R-CNN | FPG-chn128 | 50e | 15.3 | - | 41.7 | 36.9 |[config](https://github.com/open-mmlab/mmdetection/tree/master/configs/fpg/mask_rcnn_r50_fpg-chn128_crop640_50e_coco.py) |[model](https://download.openmmlab.com/mmdetection/v2.0/fpg/mask_rcnn_r50_fpg-chn128_crop640_50e_coco/mask_rcnn_r50_fpg-chn128_crop640_50e_coco-5c6ea10d.pth) | [log](https://download.openmmlab.com/mmdetection/v2.0/fpg/mask_rcnn_r50_fpg-chn128_crop640_50e_coco/20210223_025039.log.json) | +| RetinaNet | FPG | 50e | 20.8 | - | 40.5 | - |[config](https://github.com/open-mmlab/mmdetection/tree/master/configs/fpg/retinanet_r50_fpg_crop640_50e_coco.py) |[model](https://download.openmmlab.com/mmdetection/v2.0/fpg/retinanet_r50_fpg_crop640_50e_coco/retinanet_r50_fpg_crop640_50e_coco-46fdd1c6.pth) | [log](https://download.openmmlab.com/mmdetection/v2.0/fpg/retinanet_r50_fpg_crop640_50e_coco/20210225_143957.log.json) | +| RetinaNet | FPG-chn128 | 50e | 19.9 | - | 40.3 | - |[config](https://github.com/open-mmlab/mmdetection/tree/master/configs/fpg/retinanet_r50_fpg-chn128_crop640_50e_coco.py) |[model](https://download.openmmlab.com/mmdetection/v2.0/fpg/retinanet_r50_fpg-chn128_crop640_50e_coco/retinanet_r50_fpg-chn128_crop640_50e_coco-5cf33c76.pth) | [log](https://download.openmmlab.com/mmdetection/v2.0/fpg/retinanet_r50_fpg-chn128_crop640_50e_coco/20210225_184328.log.json) | **Note**: Chn128 means to decrease the number of channels of features and convs from 256 (default) to 128 in Neck and BBox Head, which can greatly decrease memory consumption without sacrificing much precision. diff --git a/configs/fpg/metafile.yml b/configs/fpg/metafile.yml new file mode 100644 index 0000000..885d857 --- /dev/null +++ b/configs/fpg/metafile.yml @@ -0,0 +1,104 @@ +Collections: + - Name: Feature Pyramid Grids + Metadata: + Training Data: COCO + Training Techniques: + - SGD with Momentum + - Weight Decay + Training Resources: 8x V100 GPUs + Architecture: + - Feature Pyramid Grids + Paper: + URL: https://arxiv.org/abs/2004.03580 + Title: 'Feature Pyramid Grids' + README: configs/fpg/README.md + Code: + URL: https://github.com/open-mmlab/mmdetection/blob/v2.10.0/mmdet/models/necks/fpg.py#L101 + Version: v2.10.0 + +Models: + - Name: faster_rcnn_r50_fpg_crop640_50e_coco + In Collection: Feature Pyramid Grids + Config: configs/fpg/faster_rcnn_r50_fpg_crop640_50e_coco.py + Metadata: + Training Memory (GB): 20.0 + Epochs: 50 + Results: + - Task: Object Detection + Dataset: COCO + Metrics: + box AP: 42.2 + Weights: https://download.openmmlab.com/mmdetection/v2.0/fpg/faster_rcnn_r50_fpg_crop640_50e_coco/faster_rcnn_r50_fpg_crop640_50e_coco-76220505.pth + + - Name: faster_rcnn_r50_fpg-chn128_crop640_50e_coco + In Collection: Feature Pyramid Grids + Config: configs/fpg/faster_rcnn_r50_fpg-chn128_crop640_50e_coco.py + Metadata: + Training Memory (GB): 11.9 + Epochs: 50 + Results: + - Task: Object Detection + Dataset: COCO + Metrics: + box AP: 41.2 + Weights: https://download.openmmlab.com/mmdetection/v2.0/fpg/faster_rcnn_r50_fpg-chn128_crop640_50e_coco/faster_rcnn_r50_fpg-chn128_crop640_50e_coco-24257de9.pth + + - Name: mask_rcnn_r50_fpg_crop640_50e_coco + In Collection: Feature Pyramid Grids + Config: configs/fpg/mask_rcnn_r50_fpg_crop640_50e_coco.py + Metadata: + Training Memory (GB): 23.2 + Epochs: 50 + Results: + - Task: Object Detection + Dataset: COCO + Metrics: + box AP: 42.7 + - Task: Instance Segmentation + Dataset: COCO + Metrics: + mask AP: 37.8 + Weights: https://download.openmmlab.com/mmdetection/v2.0/fpg/mask_rcnn_r50_fpg_crop640_50e_coco/mask_rcnn_r50_fpg_crop640_50e_coco-c5860453.pth + + - Name: mask_rcnn_r50_fpg-chn128_crop640_50e_coco + In Collection: Feature Pyramid Grids + Config: configs/fpg/mask_rcnn_r50_fpg-chn128_crop640_50e_coco.py + Metadata: + Training Memory (GB): 15.3 + Epochs: 50 + Results: + - Task: Object Detection + Dataset: COCO + Metrics: + box AP: 41.7 + - Task: Instance Segmentation + Dataset: COCO + Metrics: + mask AP: 36.9 + Weights: https://download.openmmlab.com/mmdetection/v2.0/fpg/mask_rcnn_r50_fpg-chn128_crop640_50e_coco/mask_rcnn_r50_fpg-chn128_crop640_50e_coco-5c6ea10d.pth + + - Name: retinanet_r50_fpg_crop640_50e_coco + In Collection: Feature Pyramid Grids + Config: configs/fpg/retinanet_r50_fpg_crop640_50e_coco.py + Metadata: + Training Memory (GB): 20.8 + Epochs: 50 + Results: + - Task: Object Detection + Dataset: COCO + Metrics: + box AP: 40.5 + Weights: https://download.openmmlab.com/mmdetection/v2.0/fpg/retinanet_r50_fpg_crop640_50e_coco/retinanet_r50_fpg_crop640_50e_coco-46fdd1c6.pth + + - Name: retinanet_r50_fpg-chn128_crop640_50e_coco + In Collection: Feature Pyramid Grids + Config: configs/fpg/retinanet_r50_fpg-chn128_crop640_50e_coco.py + Metadata: + Training Memory (GB): 19.9 + Epochs: 50 + Results: + - Task: Object Detection + Dataset: COCO + Metrics: + box AP: 40.3 + Weights: https://download.openmmlab.com/mmdetection/v2.0/fpg/retinanet_r50_fpg-chn128_crop640_50e_coco/retinanet_r50_fpg-chn128_crop640_50e_coco-5cf33c76.pth diff --git a/configs/free_anchor/README.md b/configs/free_anchor/README.md index 6d6474c..5014bc0 100644 --- a/configs/free_anchor/README.md +++ b/configs/free_anchor/README.md @@ -2,7 +2,7 @@ ## Introduction -[ALGORITHM] + ```latex @inproceedings{zhang2019freeanchor, @@ -17,9 +17,9 @@ | Backbone | Style | Lr schd | Mem (GB) | Inf time (fps) | box AP | Config | Download | |:--------:|:-------:|:-------:|:--------:|:--------------:|:------:|:------:|:--------:| -| R-50 | pytorch | 1x | 4.9 | 18.4 | 38.7 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/free_anchor/retinanet_free_anchor_r50_fpn_1x_coco.py) | [model](http://download.openmmlab.com/mmdetection/v2.0/free_anchor/retinanet_free_anchor_r50_fpn_1x_coco/retinanet_free_anchor_r50_fpn_1x_coco_20200130-0f67375f.pth) | [log](http://download.openmmlab.com/mmdetection/v2.0/free_anchor/retinanet_free_anchor_r50_fpn_1x_coco/retinanet_free_anchor_r50_fpn_1x_coco_20200130_095625.log.json) | -| R-101 | pytorch | 1x | 6.8 | 14.9 | 40.3 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/free_anchor/retinanet_free_anchor_r101_fpn_1x_coco.py) | [model](http://download.openmmlab.com/mmdetection/v2.0/free_anchor/retinanet_free_anchor_r101_fpn_1x_coco/retinanet_free_anchor_r101_fpn_1x_coco_20200130-358324e6.pth) | [log](http://download.openmmlab.com/mmdetection/v2.0/free_anchor/retinanet_free_anchor_r101_fpn_1x_coco/retinanet_free_anchor_r101_fpn_1x_coco_20200130_100723.log.json) | -| X-101-32x4d | pytorch | 1x | 8.1 | 11.1 | 41.9 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/free_anchor/retinanet_free_anchor_x101_32x4d_fpn_1x_coco.py) | [model](http://download.openmmlab.com/mmdetection/v2.0/free_anchor/retinanet_free_anchor_x101_32x4d_fpn_1x_coco/retinanet_free_anchor_x101_32x4d_fpn_1x_coco_20200130-d4846968.pth) | [log](http://download.openmmlab.com/mmdetection/v2.0/free_anchor/retinanet_free_anchor_x101_32x4d_fpn_1x_coco/retinanet_free_anchor_x101_32x4d_fpn_1x_coco_20200130_095627.log.json) | +| R-50 | pytorch | 1x | 4.9 | 18.4 | 38.7 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/free_anchor/retinanet_free_anchor_r50_fpn_1x_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/free_anchor/retinanet_free_anchor_r50_fpn_1x_coco/retinanet_free_anchor_r50_fpn_1x_coco_20200130-0f67375f.pth) | [log](https://download.openmmlab.com/mmdetection/v2.0/free_anchor/retinanet_free_anchor_r50_fpn_1x_coco/retinanet_free_anchor_r50_fpn_1x_coco_20200130_095625.log.json) | +| R-101 | pytorch | 1x | 6.8 | 14.9 | 40.3 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/free_anchor/retinanet_free_anchor_r101_fpn_1x_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/free_anchor/retinanet_free_anchor_r101_fpn_1x_coco/retinanet_free_anchor_r101_fpn_1x_coco_20200130-358324e6.pth) | [log](https://download.openmmlab.com/mmdetection/v2.0/free_anchor/retinanet_free_anchor_r101_fpn_1x_coco/retinanet_free_anchor_r101_fpn_1x_coco_20200130_100723.log.json) | +| X-101-32x4d | pytorch | 1x | 8.1 | 11.1 | 41.9 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/free_anchor/retinanet_free_anchor_x101_32x4d_fpn_1x_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/free_anchor/retinanet_free_anchor_x101_32x4d_fpn_1x_coco/retinanet_free_anchor_x101_32x4d_fpn_1x_coco_20200130-d4846968.pth) | [log](https://download.openmmlab.com/mmdetection/v2.0/free_anchor/retinanet_free_anchor_x101_32x4d_fpn_1x_coco/retinanet_free_anchor_x101_32x4d_fpn_1x_coco_20200130_095627.log.json) | **Notes:** diff --git a/configs/free_anchor/metafile.yml b/configs/free_anchor/metafile.yml new file mode 100644 index 0000000..170fb5c --- /dev/null +++ b/configs/free_anchor/metafile.yml @@ -0,0 +1,79 @@ +Collections: + - Name: FreeAnchor + Metadata: + Training Data: COCO + Training Techniques: + - SGD with Momentum + - Weight Decay + Training Resources: 8x V100 GPUs + Architecture: + - FreeAnchor + - ResNet + Paper: + URL: https://arxiv.org/abs/1909.02466 + Title: 'FreeAnchor: Learning to Match Anchors for Visual Object Detection' + README: configs/free_anchor/README.md + Code: + URL: https://github.com/open-mmlab/mmdetection/blob/v2.0.0/mmdet/models/dense_heads/free_anchor_retina_head.py#L10 + Version: v2.0.0 + +Models: + - Name: retinanet_free_anchor_r50_fpn_1x_coco + In Collection: FreeAnchor + Config: configs/free_anchor/retinanet_free_anchor_r50_fpn_1x_coco.py + Metadata: + Training Memory (GB): 4.9 + inference time (ms/im): + - value: 54.35 + hardware: V100 + backend: PyTorch + batch size: 1 + mode: FP32 + resolution: (800, 1333) + Epochs: 12 + Results: + - Task: Object Detection + Dataset: COCO + Metrics: + box AP: 38.7 + Weights: https://download.openmmlab.com/mmdetection/v2.0/free_anchor/retinanet_free_anchor_r50_fpn_1x_coco/retinanet_free_anchor_r50_fpn_1x_coco_20200130-0f67375f.pth + + - Name: retinanet_free_anchor_r101_fpn_1x_coco + In Collection: FreeAnchor + Config: configs/free_anchor/retinanet_free_anchor_r101_fpn_1x_coco.py + Metadata: + Training Memory (GB): 6.8 + inference time (ms/im): + - value: 67.11 + hardware: V100 + backend: PyTorch + batch size: 1 + mode: FP32 + resolution: (800, 1333) + Epochs: 12 + Results: + - Task: Object Detection + Dataset: COCO + Metrics: + box AP: 40.3 + Weights: https://download.openmmlab.com/mmdetection/v2.0/free_anchor/retinanet_free_anchor_r101_fpn_1x_coco/retinanet_free_anchor_r101_fpn_1x_coco_20200130-358324e6.pth + + - Name: retinanet_free_anchor_x101_32x4d_fpn_1x_coco + In Collection: FreeAnchor + Config: configs/free_anchor/retinanet_free_anchor_x101_32x4d_fpn_1x_coco.py + Metadata: + Training Memory (GB): 8.1 + inference time (ms/im): + - value: 90.09 + hardware: V100 + backend: PyTorch + batch size: 1 + mode: FP32 + resolution: (800, 1333) + Epochs: 12 + Results: + - Task: Object Detection + Dataset: COCO + Metrics: + box AP: 41.9 + Weights: https://download.openmmlab.com/mmdetection/v2.0/free_anchor/retinanet_free_anchor_x101_32x4d_fpn_1x_coco/retinanet_free_anchor_x101_32x4d_fpn_1x_coco_20200130-d4846968.pth diff --git a/configs/free_anchor/retinanet_free_anchor_r101_fpn_1x_coco.py b/configs/free_anchor/retinanet_free_anchor_r101_fpn_1x_coco.py index 9917d5c..f4aea53 100644 --- a/configs/free_anchor/retinanet_free_anchor_r101_fpn_1x_coco.py +++ b/configs/free_anchor/retinanet_free_anchor_r101_fpn_1x_coco.py @@ -1,2 +1,6 @@ _base_ = './retinanet_free_anchor_r50_fpn_1x_coco.py' -model = dict(pretrained='torchvision://resnet101', backbone=dict(depth=101)) +model = dict( + backbone=dict( + depth=101, + init_cfg=dict(type='Pretrained', + checkpoint='torchvision://resnet101'))) diff --git a/configs/free_anchor/retinanet_free_anchor_x101_32x4d_fpn_1x_coco.py b/configs/free_anchor/retinanet_free_anchor_x101_32x4d_fpn_1x_coco.py index e2640c0..65f8a9e 100644 --- a/configs/free_anchor/retinanet_free_anchor_x101_32x4d_fpn_1x_coco.py +++ b/configs/free_anchor/retinanet_free_anchor_x101_32x4d_fpn_1x_coco.py @@ -1,6 +1,5 @@ _base_ = './retinanet_free_anchor_r50_fpn_1x_coco.py' model = dict( - pretrained='open-mmlab://resnext101_32x4d', backbone=dict( type='ResNeXt', depth=101, @@ -9,4 +8,6 @@ num_stages=4, out_indices=(0, 1, 2, 3), frozen_stages=1, - style='pytorch')) + style='pytorch', + init_cfg=dict( + type='Pretrained', checkpoint='open-mmlab://resnext101_32x4d'))) diff --git a/configs/fsaf/README.md b/configs/fsaf/README.md index 42468c8..4d64392 100644 --- a/configs/fsaf/README.md +++ b/configs/fsaf/README.md @@ -1,6 +1,6 @@ # Feature Selective Anchor-Free Module for Single-Shot Object Detection -[ALGORITHM] + FSAF is an anchor-free method published in CVPR2019 ([https://arxiv.org/pdf/1903.00621.pdf](https://arxiv.org/pdf/1903.00621.pdf)). Actually it is equivalent to the anchor-based method with only one anchor at each feature map position in each FPN level. @@ -16,10 +16,10 @@ it is empirically found that a hard threshold (0.2-0.2) gives a further gain on | Backbone | ignore range | ms-train| Lr schd |Train Mem (GB)| Train time (s/iter) | Inf time (fps) | box AP | Config | Download | |:----------:| :-------: |:-------:|:-------:|:------------:|:---------------:|:--------------:|:-------------:|:------:|:--------:| -| R-50 | 0.2-0.5 | N | 1x | 3.15 | 0.43 | 12.3 | 36.0 (35.9) | | [model](http://download.openmmlab.com/mmdetection/v2.0/fsaf/fsaf_pscale0.2_nscale0.5_r50_fpn_1x_coco/fsaf_pscale0.2_nscale0.5_r50_fpn_1x_coco_20200715-b555b0e0.pth) | [log](http://download.openmmlab.com/mmdetection/v2.0/fsaf/fsaf_pscale0.2_nscale0.5_r50_fpn_1x_coco/fsaf_pscale0.2_nscale0.5_r50_fpn_1x_coco_20200715_094657.log.json) | -| R-50 | 0.2-0.2 | N | 1x | 3.15 | 0.43 | 13.0 | 37.4 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/fsaf/fsaf_r50_fpn_1x_coco.py) | [model](http://download.openmmlab.com/mmdetection/v2.0/fsaf/fsaf_r50_fpn_1x_coco/fsaf_r50_fpn_1x_coco-94ccc51f.pth) | [log](http://download.openmmlab.com/mmdetection/v2.0/fsaf/fsaf_r50_fpn_1x_coco/fsaf_r50_fpn_1x_coco_20200428_072327.log.json)| -| R-101 | 0.2-0.2 | N | 1x | 5.08 | 0.58 | 10.8 | 39.3 (37.9) | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/fsaf/fsaf_r101_fpn_1x_coco.py) | [model](http://download.openmmlab.com/mmdetection/v2.0/fsaf/fsaf_r101_fpn_1x_coco/fsaf_r101_fpn_1x_coco-9e71098f.pth) | [log](http://download.openmmlab.com/mmdetection/v2.0/fsaf/fsaf_r101_fpn_1x_coco/fsaf_r101_fpn_1x_coco_20200428_160348.log.json)| -| X-101 | 0.2-0.2 | N | 1x | 9.38 | 1.23 | 5.6 | 42.4 (41.0) | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/fsaf/fsaf_x101_64x4d_fpn_1x_coco.py) | [model](http://download.openmmlab.com/mmdetection/v2.0/fsaf/fsaf_x101_64x4d_fpn_1x_coco/fsaf_x101_64x4d_fpn_1x_coco-e3f6e6fd.pth) | [log](http://download.openmmlab.com/mmdetection/v2.0/fsaf/fsaf_x101_64x4d_fpn_1x_coco/fsaf_x101_64x4d_fpn_1x_coco_20200428_160424.log.json)| +| R-50 | 0.2-0.5 | N | 1x | 3.15 | 0.43 | 12.3 | 36.0 (35.9) | | [model](https://download.openmmlab.com/mmdetection/v2.0/fsaf/fsaf_pscale0.2_nscale0.5_r50_fpn_1x_coco/fsaf_pscale0.2_nscale0.5_r50_fpn_1x_coco_20200715-b555b0e0.pth) | [log](https://download.openmmlab.com/mmdetection/v2.0/fsaf/fsaf_pscale0.2_nscale0.5_r50_fpn_1x_coco/fsaf_pscale0.2_nscale0.5_r50_fpn_1x_coco_20200715_094657.log.json) | +| R-50 | 0.2-0.2 | N | 1x | 3.15 | 0.43 | 13.0 | 37.4 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/fsaf/fsaf_r50_fpn_1x_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/fsaf/fsaf_r50_fpn_1x_coco/fsaf_r50_fpn_1x_coco-94ccc51f.pth) | [log](https://download.openmmlab.com/mmdetection/v2.0/fsaf/fsaf_r50_fpn_1x_coco/fsaf_r50_fpn_1x_coco_20200428_072327.log.json)| +| R-101 | 0.2-0.2 | N | 1x | 5.08 | 0.58 | 10.8 | 39.3 (37.9) | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/fsaf/fsaf_r101_fpn_1x_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/fsaf/fsaf_r101_fpn_1x_coco/fsaf_r101_fpn_1x_coco-9e71098f.pth) | [log](https://download.openmmlab.com/mmdetection/v2.0/fsaf/fsaf_r101_fpn_1x_coco/fsaf_r101_fpn_1x_coco_20200428_160348.log.json)| +| X-101 | 0.2-0.2 | N | 1x | 9.38 | 1.23 | 5.6 | 42.4 (41.0) | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/fsaf/fsaf_x101_64x4d_fpn_1x_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/fsaf/fsaf_x101_64x4d_fpn_1x_coco/fsaf_x101_64x4d_fpn_1x_coco-e3f6e6fd.pth) | [log](https://download.openmmlab.com/mmdetection/v2.0/fsaf/fsaf_x101_64x4d_fpn_1x_coco/fsaf_x101_64x4d_fpn_1x_coco_20200428_160424.log.json)| **Notes:** diff --git a/configs/fsaf/fsaf_r101_fpn_1x_coco.py b/configs/fsaf/fsaf_r101_fpn_1x_coco.py index 95a7ae2..12b49fe 100644 --- a/configs/fsaf/fsaf_r101_fpn_1x_coco.py +++ b/configs/fsaf/fsaf_r101_fpn_1x_coco.py @@ -1,2 +1,6 @@ _base_ = './fsaf_r50_fpn_1x_coco.py' -model = dict(pretrained='torchvision://resnet101', backbone=dict(depth=101)) +model = dict( + backbone=dict( + depth=101, + init_cfg=dict(type='Pretrained', + checkpoint='torchvision://resnet101'))) diff --git a/configs/fsaf/fsaf_x101_64x4d_fpn_1x_coco.py b/configs/fsaf/fsaf_x101_64x4d_fpn_1x_coco.py index b966f24..89c0c63 100644 --- a/configs/fsaf/fsaf_x101_64x4d_fpn_1x_coco.py +++ b/configs/fsaf/fsaf_x101_64x4d_fpn_1x_coco.py @@ -1,6 +1,5 @@ _base_ = './fsaf_r50_fpn_1x_coco.py' model = dict( - pretrained='open-mmlab://resnext101_64x4d', backbone=dict( type='ResNeXt', depth=101, @@ -10,4 +9,6 @@ out_indices=(0, 1, 2, 3), frozen_stages=1, norm_cfg=dict(type='BN', requires_grad=True), - style='pytorch')) + style='pytorch', + init_cfg=dict( + type='Pretrained', checkpoint='open-mmlab://resnext101_64x4d'))) diff --git a/configs/fsaf/metafile.yml b/configs/fsaf/metafile.yml new file mode 100644 index 0000000..5434e9a --- /dev/null +++ b/configs/fsaf/metafile.yml @@ -0,0 +1,80 @@ +Collections: + - Name: FSAF + Metadata: + Training Data: COCO + Training Techniques: + - SGD with Momentum + - Weight Decay + Training Resources: 8x Titan-XP GPUs + Architecture: + - FPN + - FSAF + - ResNet + Paper: + URL: https://arxiv.org/abs/1903.00621 + Title: 'Feature Selective Anchor-Free Module for Single-Shot Object Detection' + README: configs/fsaf/README.md + Code: + URL: https://github.com/open-mmlab/mmdetection/blob/v2.1.0/mmdet/models/detectors/fsaf.py#L6 + Version: v2.1.0 + +Models: + - Name: fsaf_r50_fpn_1x_coco + In Collection: FSAF + Config: configs/fsaf/fsaf_r50_fpn_1x_coco.py + Metadata: + Training Memory (GB): 3.15 + inference time (ms/im): + - value: 76.92 + hardware: V100 + backend: PyTorch + batch size: 1 + mode: FP32 + resolution: (800, 1333) + Epochs: 12 + Results: + - Task: Object Detection + Dataset: COCO + Metrics: + box AP: 37.4 + Weights: https://download.openmmlab.com/mmdetection/v2.0/fsaf/fsaf_r50_fpn_1x_coco/fsaf_r50_fpn_1x_coco-94ccc51f.pth + + - Name: fsaf_r101_fpn_1x_coco + In Collection: FSAF + Config: configs/fsaf/fsaf_r101_fpn_1x_coco.py + Metadata: + Training Memory (GB): 5.08 + inference time (ms/im): + - value: 92.59 + hardware: V100 + backend: PyTorch + batch size: 1 + mode: FP32 + resolution: (800, 1333) + Epochs: 12 + Results: + - Task: Object Detection + Dataset: COCO + Metrics: + box AP: 39.3 (37.9) + Weights: https://download.openmmlab.com/mmdetection/v2.0/fsaf/fsaf_r101_fpn_1x_coco/fsaf_r101_fpn_1x_coco-9e71098f.pth + + - Name: fsaf_x101_64x4d_fpn_1x_coco + In Collection: FSAF + Config: configs/fsaf/fsaf_x101_64x4d_fpn_1x_coco.py + Metadata: + Training Memory (GB): 9.38 + inference time (ms/im): + - value: 178.57 + hardware: V100 + backend: PyTorch + batch size: 1 + mode: FP32 + resolution: (800, 1333) + Epochs: 12 + Results: + - Task: Object Detection + Dataset: COCO + Metrics: + box AP: 42.4 (41.0) + Weights: https://download.openmmlab.com/mmdetection/v2.0/fsaf/fsaf_x101_64x4d_fpn_1x_coco/fsaf_x101_64x4d_fpn_1x_coco-e3f6e6fd.pth diff --git a/configs/gcnet/README.md b/configs/gcnet/README.md index 0ef8db7..0ee6a94 100644 --- a/configs/gcnet/README.md +++ b/configs/gcnet/README.md @@ -7,7 +7,7 @@ We provide config files to reproduce the results in the paper for ## Introduction -[ALGORITHM] + **GCNet** is initially described in [arxiv](https://arxiv.org/abs/1904.11492). Via absorbing advantages of Non-Local Networks (NLNet) and Squeeze-Excitation Networks (SENet), GCNet provides a simple, fast and effective approach for global context modeling, which generally outperforms both NLNet and SENet on major benchmarks for various recognition tasks. @@ -28,28 +28,28 @@ The results on COCO 2017val are shown in the below table. | Backbone | Model | Context | Lr schd | Mem (GB) | Inf time (fps) | box AP | mask AP | Config | Download | | :-------: | :--------------: | :------------: | :-----: | :------: | :------------: | :----: | :-----: | :------: | :--------: | -| R-50-FPN | Mask | GC(c3-c5, r16) | 1x | 5.0 | | 39.7 | 35.9 |[config](https://github.com/open-mmlab/mmdetection/tree/master/configs/gcnet/mask_rcnn_r50_fpn_r16_gcb_c3-c5_1x_coco.py) | [model](http://download.openmmlab.com/mmdetection/v2.0/gcnet/mask_rcnn_r50_fpn_r16_gcb_c3-c5_1x_coco/mask_rcnn_r50_fpn_r16_gcb_c3-c5_1x_coco_20200515_211915-187da160.pth) | [log](http://download.openmmlab.com/mmdetection/v2.0/gcnet/mask_rcnn_r50_fpn_r16_gcb_c3-c5_1x_coco/mask_rcnn_r50_fpn_r16_gcb_c3-c5_1x_coco_20200515_211915.log.json) | -| R-50-FPN | Mask | GC(c3-c5, r4) | 1x | 5.1 | 15.0 | 39.9 | 36.0 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/gcnet/mask_rcnn_r50_fpn_r4_gcb_c3-c5_1x_coco.py) | [model](http://download.openmmlab.com/mmdetection/v2.0/gcnet/mask_rcnn_r50_fpn_r4_gcb_c3-c5_1x_coco/mask_rcnn_r50_fpn_r4_gcb_c3-c5_1x_coco_20200204-17235656.pth) | [log](http://download.openmmlab.com/mmdetection/v2.0/gcnet/mask_rcnn_r50_fpn_r4_gcb_c3-c5_1x_coco/mask_rcnn_r50_fpn_r4_gcb_c3-c5_1x_coco_20200204_024626.log.json) | -| R-101-FPN | Mask | GC(c3-c5, r16) | 1x | 7.6 | 11.4 | 41.3 | 37.2 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/gcnet/mask_rcnn_r101_fpn_r16_gcb_c3-c5_1x_coco.py) | [model](http://download.openmmlab.com/mmdetection/v2.0/gcnet/mask_rcnn_r101_fpn_r16_gcb_c3-c5_1x_coco/mask_rcnn_r101_fpn_r16_gcb_c3-c5_1x_coco_20200205-e58ae947.pth) | [log](http://download.openmmlab.com/mmdetection/v2.0/gcnet/mask_rcnn_r101_fpn_r16_gcb_c3-c5_1x_coco/mask_rcnn_r101_fpn_r16_gcb_c3-c5_1x_coco_20200205_192835.log.json) | -| R-101-FPN | Mask | GC(c3-c5, r4) | 1x | 7.8 | 11.6 | 42.2 | 37.8 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/gcnet/mask_rcnn_r101_fpn_r4_gcb_c3-c5_1x_coco.py) | [model](http://download.openmmlab.com/mmdetection/v2.0/gcnet/mask_rcnn_r101_fpn_r4_gcb_c3-c5_1x_coco/mask_rcnn_r101_fpn_r4_gcb_c3-c5_1x_coco_20200206-af22dc9d.pth) | [log](http://download.openmmlab.com/mmdetection/v2.0/gcnet/mask_rcnn_r101_fpn_r4_gcb_c3-c5_1x_coco/mask_rcnn_r101_fpn_r4_gcb_c3-c5_1x_coco_20200206_112128.log.json) | +| R-50-FPN | Mask | GC(c3-c5, r16) | 1x | 5.0 | | 39.7 | 35.9 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/gcnet/mask_rcnn_r50_fpn_r16_gcb_c3-c5_1x_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/gcnet/mask_rcnn_r50_fpn_r16_gcb_c3-c5_1x_coco/mask_rcnn_r50_fpn_r16_gcb_c3-c5_1x_coco_20200515_211915-187da160.pth) | [log](https://download.openmmlab.com/mmdetection/v2.0/gcnet/mask_rcnn_r50_fpn_r16_gcb_c3-c5_1x_coco/mask_rcnn_r50_fpn_r16_gcb_c3-c5_1x_coco_20200515_211915.log.json) | +| R-50-FPN | Mask | GC(c3-c5, r4) | 1x | 5.1 | 15.0 | 39.9 | 36.0 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/gcnet/mask_rcnn_r50_fpn_r4_gcb_c3-c5_1x_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/gcnet/mask_rcnn_r50_fpn_r4_gcb_c3-c5_1x_coco/mask_rcnn_r50_fpn_r4_gcb_c3-c5_1x_coco_20200204-17235656.pth) | [log](https://download.openmmlab.com/mmdetection/v2.0/gcnet/mask_rcnn_r50_fpn_r4_gcb_c3-c5_1x_coco/mask_rcnn_r50_fpn_r4_gcb_c3-c5_1x_coco_20200204_024626.log.json) | +| R-101-FPN | Mask | GC(c3-c5, r16) | 1x | 7.6 | 11.4 | 41.3 | 37.2 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/gcnet/mask_rcnn_r101_fpn_r16_gcb_c3-c5_1x_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/gcnet/mask_rcnn_r101_fpn_r16_gcb_c3-c5_1x_coco/mask_rcnn_r101_fpn_r16_gcb_c3-c5_1x_coco_20200205-e58ae947.pth) | [log](https://download.openmmlab.com/mmdetection/v2.0/gcnet/mask_rcnn_r101_fpn_r16_gcb_c3-c5_1x_coco/mask_rcnn_r101_fpn_r16_gcb_c3-c5_1x_coco_20200205_192835.log.json) | +| R-101-FPN | Mask | GC(c3-c5, r4) | 1x | 7.8 | 11.6 | 42.2 | 37.8 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/gcnet/mask_rcnn_r101_fpn_r4_gcb_c3-c5_1x_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/gcnet/mask_rcnn_r101_fpn_r4_gcb_c3-c5_1x_coco/mask_rcnn_r101_fpn_r4_gcb_c3-c5_1x_coco_20200206-af22dc9d.pth) | [log](https://download.openmmlab.com/mmdetection/v2.0/gcnet/mask_rcnn_r101_fpn_r4_gcb_c3-c5_1x_coco/mask_rcnn_r101_fpn_r4_gcb_c3-c5_1x_coco_20200206_112128.log.json) | | Backbone | Model | Context | Lr schd | Mem (GB) | Inf time (fps) | box AP | mask AP | Config | Download | | :-------: | :--------------: | :------------: | :-----: | :------: | :------------: | :----: | :-----: | :------: | :-------: | -| R-50-FPN | Mask | - | 1x | 4.4 | 16.6 | 38.4 | 34.6 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/gcnet/mask_rcnn_r50_fpn_syncbn-backbone_1x_coco.py) | [model](http://download.openmmlab.com/mmdetection/v2.0/gcnet/mask_rcnn_r50_fpn_syncbn-backbone_1x_coco/mask_rcnn_r50_fpn_syncbn-backbone_1x_coco_20200202-bb3eb55c.pth) | [log](http://download.openmmlab.com/mmdetection/v2.0/gcnet/mask_rcnn_r50_fpn_syncbn-backbone_1x_coco/mask_rcnn_r50_fpn_syncbn-backbone_1x_coco_20200202_214122.log.json) | -| R-50-FPN | Mask | GC(c3-c5, r16) | 1x | 5.0 | 15.5 | 40.4 | 36.2 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/gcnet/mask_rcnn_r50_fpn_syncbn-backbone_r16_gcb_c3-c5_1x_coco.py) | [model](http://download.openmmlab.com/mmdetection/v2.0/gcnet/mask_rcnn_r50_fpn_syncbn-backbone_r16_gcb_c3-c5_1x_coco/mask_rcnn_r50_fpn_syncbn-backbone_r16_gcb_c3-c5_1x_coco_20200202-587b99aa.pth) | [log](http://download.openmmlab.com/mmdetection/v2.0/gcnet/mask_rcnn_r50_fpn_syncbn-backbone_r16_gcb_c3-c5_1x_coco/mask_rcnn_r50_fpn_syncbn-backbone_r16_gcb_c3-c5_1x_coco_20200202_174907.log.json) | -| R-50-FPN | Mask | GC(c3-c5, r4) | 1x | 5.1 | 15.1 | 40.7 | 36.5 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/gcnet/mask_rcnn_r50_fpn_syncbn-backbone_r4_gcb_c3-c5_1x_coco.py) | [model](http://download.openmmlab.com/mmdetection/v2.0/gcnet/mask_rcnn_r50_fpn_syncbn-backbone_r4_gcb_c3-c5_1x_coco/mask_rcnn_r50_fpn_syncbn-backbone_r4_gcb_c3-c5_1x_coco_20200202-50b90e5c.pth) | [log](http://download.openmmlab.com/mmdetection/v2.0/gcnet/mask_rcnn_r50_fpn_syncbn-backbone_r4_gcb_c3-c5_1x_coco/mask_rcnn_r50_fpn_syncbn-backbone_r4_gcb_c3-c5_1x_coco_20200202_085547.log.json) | -| R-101-FPN | Mask | - | 1x | 6.4 | 13.3 | 40.5 | 36.3 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/gcnet/mask_rcnn_r101_fpn_syncbn-backbone_1x_coco.py) | [model](http://download.openmmlab.com/mmdetection/v2.0/gcnet/mask_rcnn_r101_fpn_syncbn-backbone_1x_coco/mask_rcnn_r101_fpn_syncbn-backbone_1x_coco_20200210-81658c8a.pth) | [log](http://download.openmmlab.com/mmdetection/v2.0/gcnet/mask_rcnn_r101_fpn_syncbn-backbone_1x_coco/mask_rcnn_r101_fpn_syncbn-backbone_1x_coco_20200210_220422.log.json) | -| R-101-FPN | Mask | GC(c3-c5, r16) | 1x | 7.6 | 12.0 | 42.2 | 37.8 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/gcnet/mask_rcnn_r101_fpn_syncbn-backbone_r16_gcb_c3-c5_1x_coco.py) | [model](http://download.openmmlab.com/mmdetection/v2.0/gcnet/mask_rcnn_r101_fpn_syncbn-backbone_r16_gcb_c3-c5_1x_coco/mask_rcnn_r101_fpn_syncbn-backbone_r16_gcb_c3-c5_1x_coco_20200207-945e77ca.pth) | [log](http://download.openmmlab.com/mmdetection/v2.0/gcnet/mask_rcnn_r101_fpn_syncbn-backbone_r16_gcb_c3-c5_1x_coco/mask_rcnn_r101_fpn_syncbn-backbone_r16_gcb_c3-c5_1x_coco_20200207_015330.log.json) | -| R-101-FPN | Mask | GC(c3-c5, r4) | 1x | 7.8 | 11.8 | 42.2 | 37.8 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/gcnet/mask_rcnn_r101_fpn_syncbn-backbone_r4_gcb_c3-c5_1x_coco.py) | [model](http://download.openmmlab.com/mmdetection/v2.0/gcnet/mask_rcnn_r101_fpn_syncbn-backbone_r4_gcb_c3-c5_1x_coco/mask_rcnn_r101_fpn_syncbn-backbone_r4_gcb_c3-c5_1x_coco_20200206-8407a3f0.pth) | [log](http://download.openmmlab.com/mmdetection/v2.0/gcnet/mask_rcnn_r101_fpn_syncbn-backbone_r4_gcb_c3-c5_1x_coco/mask_rcnn_r101_fpn_syncbn-backbone_r4_gcb_c3-c5_1x_coco_20200206_142508.log.json) | -| X-101-FPN | Mask | - | 1x | 7.6 | 11.3 | 42.4 | 37.7 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/gcnet/mask_rcnn_x101_32x4d_fpn_syncbn-backbone_1x_coco.py) | [model](http://download.openmmlab.com/mmdetection/v2.0/gcnet/mask_rcnn_x101_32x4d_fpn_syncbn-backbone_1x_coco/mask_rcnn_x101_32x4d_fpn_syncbn-backbone_1x_coco_20200211-7584841c.pth) | [log](http://download.openmmlab.com/mmdetection/v2.0/gcnet/mask_rcnn_x101_32x4d_fpn_syncbn-backbone_1x_coco/mask_rcnn_x101_32x4d_fpn_syncbn-backbone_1x_coco_20200211_054326.log.json) | -| X-101-FPN | Mask | GC(c3-c5, r16) | 1x | 8.8 | 9.8 | 43.5 | 38.6 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/gcnet/mask_rcnn_x101_32x4d_fpn_syncbn-backbone_r16_gcb_c3-c5_1x_coco.py) | [model](http://download.openmmlab.com/mmdetection/v2.0/gcnet/mask_rcnn_x101_32x4d_fpn_syncbn-backbone_r16_gcb_c3-c5_1x_coco/mask_rcnn_x101_32x4d_fpn_syncbn-backbone_r16_gcb_c3-c5_1x_coco_20200211-cbed3d2c.pth) | [log](http://download.openmmlab.com/mmdetection/v2.0/gcnet/mask_rcnn_x101_32x4d_fpn_syncbn-backbone_r16_gcb_c3-c5_1x_coco/mask_rcnn_x101_32x4d_fpn_syncbn-backbone_r16_gcb_c3-c5_1x_coco_20200211_164715.log.json) | -| X-101-FPN | Mask | GC(c3-c5, r4) | 1x | 9.0 | 9.7 | 43.9 | 39.0 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/gcnet/mask_rcnn_x101_32x4d_fpn_syncbn-backbone_r4_gcb_c3-c5_1x_coco.py) | [model](http://download.openmmlab.com/mmdetection/v2.0/gcnet/mask_rcnn_x101_32x4d_fpn_syncbn-backbone_r4_gcb_c3-c5_1x_coco/mask_rcnn_x101_32x4d_fpn_syncbn-backbone_r4_gcb_c3-c5_1x_coco_20200212-68164964.pth) | [log](http://download.openmmlab.com/mmdetection/v2.0/gcnet/mask_rcnn_x101_32x4d_fpn_syncbn-backbone_r4_gcb_c3-c5_1x_coco/mask_rcnn_x101_32x4d_fpn_syncbn-backbone_r4_gcb_c3-c5_1x_coco_20200212_070942.log.json) | -| X-101-FPN | Cascade Mask | - | 1x | 9.2 | 8.4 | 44.7 | 38.6 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/gcnet/cascade_mask_rcnn_x101_32x4d_fpn_syncbn-backbone_1x_coco.py) | [model](http://download.openmmlab.com/mmdetection/v2.0/gcnet/cascade_mask_rcnn_x101_32x4d_fpn_syncbn-backbone_1x_coco/cascade_mask_rcnn_x101_32x4d_fpn_syncbn-backbone_1x_coco_20200310-d5ad2a5e.pth) | [log](http://download.openmmlab.com/mmdetection/v2.0/gcnet/cascade_mask_rcnn_x101_32x4d_fpn_syncbn-backbone_1x_coco/cascade_mask_rcnn_x101_32x4d_fpn_syncbn-backbone_1x_coco_20200310_115217.log.json) | -| X-101-FPN | Cascade Mask | GC(c3-c5, r16) | 1x | 10.3 | 7.7 | 46.2 | 39.7 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/gcnet/cascade_mask_rcnn_x101_32x4d_fpn_syncbn-backbone_r16_gcb_c3-c5_1x_coco.py) | [model](http://download.openmmlab.com/mmdetection/v2.0/gcnet/cascade_mask_rcnn_x101_32x4d_fpn_syncbn-backbone_r16_gcb_c3-c5_1x_coco/cascade_mask_rcnn_x101_32x4d_fpn_syncbn-backbone_r16_gcb_c3-c5_1x_coco_20200211-10bf2463.pth) | [log](http://download.openmmlab.com/mmdetection/v2.0/gcnet/cascade_mask_rcnn_x101_32x4d_fpn_syncbn-backbone_r16_gcb_c3-c5_1x_coco/cascade_mask_rcnn_x101_32x4d_fpn_syncbn-backbone_r16_gcb_c3-c5_1x_coco_20200211_184154.log.json) | -| X-101-FPN | Cascade Mask | GC(c3-c5, r4) | 1x | 10.6 | | 46.4 | 40.1 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/gcnet/cascade_mask_rcnn_x101_32x4d_fpn_syncbn-backbone_r4_gcb_c3-c5_1x_coco.py) | [model](http://download.openmmlab.com/mmdetection/v2.0/gcnet/cascade_mask_rcnn_x101_32x4d_fpn_syncbn-backbone_r4_gcb_c3-c5_1x_coco/cascade_mask_rcnn_x101_32x4d_fpn_syncbn-backbone_r4_gcb_c3-c5_1x_coco_20200703_180653-ed035291.pth) | [log](http://download.openmmlab.com/mmdetection/v2.0/gcnet/cascade_mask_rcnn_x101_32x4d_fpn_syncbn-backbone_r4_gcb_c3-c5_1x_coco/cascade_mask_rcnn_x101_32x4d_fpn_syncbn-backbone_r4_gcb_c3-c5_1x_coco_20200703_180653.log.json) | -| X-101-FPN | DCN Cascade Mask | - | 1x | | | 44.9 | 38.9 |[config](https://github.com/open-mmlab/mmdetection/tree/master/configs/gcnet/cascade_mask_rcnn_x101_32x4d_fpn_syncbn-backbone_dconv_c3-c5_1x_coco.py) | [model](http://download.openmmlab.com/mmdetection/v2.0/gcnet/cascade_mask_rcnn_x101_32x4d_fpn_syncbn-backbone_dconv_c3-c5_1x_coco/cascade_mask_rcnn_x101_32x4d_fpn_syncbn-backbone_dconv_c3-c5_1x_coco_20200516_182249-680fc3f2.pth) | [log](http://download.openmmlab.com/mmdetection/v2.0/gcnet/cascade_mask_rcnn_x101_32x4d_fpn_syncbn-backbone_dconv_c3-c5_1x_coco/cascade_mask_rcnn_x101_32x4d_fpn_syncbn-backbone_dconv_c3-c5_1x_coco_20200516_182249.log.json)| -| X-101-FPN | DCN Cascade Mask | GC(c3-c5, r16) | 1x | | | 44.6 | |[config](https://github.com/open-mmlab/mmdetection/tree/master/configs/gcnet/cascade_mask_rcnn_x101_32x4d_fpn_syncbn-backbone_dconv_c3-c5_r16_gcb_c3-c5_1x_coco.py) | [model](http://download.openmmlab.com/mmdetection/v2.0/gcnet/cascade_mask_rcnn_x101_32x4d_fpn_syncbn-backbone_dconv_c3-c5_r16_gcb_c3-c5_1x_coco/cascade_mask_rcnn_x101_32x4d_fpn_syncbn-backbone_dconv_c3-c5_r16_gcb_c3-c5_1x_coco_20200516_015634-08f56b56.pth) | [log](http://download.openmmlab.com/mmdetection/v2.0/gcnet/cascade_mask_rcnn_x101_32x4d_fpn_syncbn-backbone_dconv_c3-c5_r16_gcb_c3-c5_1x_coco/cascade_mask_rcnn_x101_32x4d_fpn_syncbn-backbone_dconv_c3-c5_r16_gcb_c3-c5_1x_coco_20200516_015634.log.json) | -| X-101-FPN | DCN Cascade Mask | GC(c3-c5, r4) | 1x | | | 45.7 | 39.5 |[config](https://github.com/open-mmlab/mmdetection/tree/master/configs/gcnet/cascade_mask_rcnn_x101_32x4d_fpn_syncbn-backbone_dconv_c3-c5_r4_gcb_c3-c5_1x_coco.py) | [model](http://download.openmmlab.com/mmdetection/v2.0/gcnet/cascade_mask_rcnn_x101_32x4d_fpn_syncbn-backbone_dconv_c3-c5_r4_gcb_c3-c5_1x_coco/cascade_mask_rcnn_x101_32x4d_fpn_syncbn-backbone_dconv_c3-c5_r4_gcb_c3-c5_1x_coco_20200518_041145-24cabcfd.pth) | [log](http://download.openmmlab.com/mmdetection/v2.0/gcnet/cascade_mask_rcnn_x101_32x4d_fpn_syncbn-backbone_dconv_c3-c5_r4_gcb_c3-c5_1x_coco/cascade_mask_rcnn_x101_32x4d_fpn_syncbn-backbone_dconv_c3-c5_r4_gcb_c3-c5_1x_coco_20200518_041145.log.json) | +| R-50-FPN | Mask | - | 1x | 4.4 | 16.6 | 38.4 | 34.6 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/gcnet/mask_rcnn_r50_fpn_syncbn-backbone_1x_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/gcnet/mask_rcnn_r50_fpn_syncbn-backbone_1x_coco/mask_rcnn_r50_fpn_syncbn-backbone_1x_coco_20200202-bb3eb55c.pth) | [log](https://download.openmmlab.com/mmdetection/v2.0/gcnet/mask_rcnn_r50_fpn_syncbn-backbone_1x_coco/mask_rcnn_r50_fpn_syncbn-backbone_1x_coco_20200202_214122.log.json) | +| R-50-FPN | Mask | GC(c3-c5, r16) | 1x | 5.0 | 15.5 | 40.4 | 36.2 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/gcnet/mask_rcnn_r50_fpn_syncbn-backbone_r16_gcb_c3-c5_1x_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/gcnet/mask_rcnn_r50_fpn_syncbn-backbone_r16_gcb_c3-c5_1x_coco/mask_rcnn_r50_fpn_syncbn-backbone_r16_gcb_c3-c5_1x_coco_20200202-587b99aa.pth) | [log](https://download.openmmlab.com/mmdetection/v2.0/gcnet/mask_rcnn_r50_fpn_syncbn-backbone_r16_gcb_c3-c5_1x_coco/mask_rcnn_r50_fpn_syncbn-backbone_r16_gcb_c3-c5_1x_coco_20200202_174907.log.json) | +| R-50-FPN | Mask | GC(c3-c5, r4) | 1x | 5.1 | 15.1 | 40.7 | 36.5 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/gcnet/mask_rcnn_r50_fpn_syncbn-backbone_r4_gcb_c3-c5_1x_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/gcnet/mask_rcnn_r50_fpn_syncbn-backbone_r4_gcb_c3-c5_1x_coco/mask_rcnn_r50_fpn_syncbn-backbone_r4_gcb_c3-c5_1x_coco_20200202-50b90e5c.pth) | [log](https://download.openmmlab.com/mmdetection/v2.0/gcnet/mask_rcnn_r50_fpn_syncbn-backbone_r4_gcb_c3-c5_1x_coco/mask_rcnn_r50_fpn_syncbn-backbone_r4_gcb_c3-c5_1x_coco_20200202_085547.log.json) | +| R-101-FPN | Mask | - | 1x | 6.4 | 13.3 | 40.5 | 36.3 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/gcnet/mask_rcnn_r101_fpn_syncbn-backbone_1x_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/gcnet/mask_rcnn_r101_fpn_syncbn-backbone_1x_coco/mask_rcnn_r101_fpn_syncbn-backbone_1x_coco_20200210-81658c8a.pth) | [log](https://download.openmmlab.com/mmdetection/v2.0/gcnet/mask_rcnn_r101_fpn_syncbn-backbone_1x_coco/mask_rcnn_r101_fpn_syncbn-backbone_1x_coco_20200210_220422.log.json) | +| R-101-FPN | Mask | GC(c3-c5, r16) | 1x | 7.6 | 12.0 | 42.2 | 37.8 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/gcnet/mask_rcnn_r101_fpn_syncbn-backbone_r16_gcb_c3-c5_1x_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/gcnet/mask_rcnn_r101_fpn_syncbn-backbone_r16_gcb_c3-c5_1x_coco/mask_rcnn_r101_fpn_syncbn-backbone_r16_gcb_c3-c5_1x_coco_20200207-945e77ca.pth) | [log](https://download.openmmlab.com/mmdetection/v2.0/gcnet/mask_rcnn_r101_fpn_syncbn-backbone_r16_gcb_c3-c5_1x_coco/mask_rcnn_r101_fpn_syncbn-backbone_r16_gcb_c3-c5_1x_coco_20200207_015330.log.json) | +| R-101-FPN | Mask | GC(c3-c5, r4) | 1x | 7.8 | 11.8 | 42.2 | 37.8 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/gcnet/mask_rcnn_r101_fpn_syncbn-backbone_r4_gcb_c3-c5_1x_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/gcnet/mask_rcnn_r101_fpn_syncbn-backbone_r4_gcb_c3-c5_1x_coco/mask_rcnn_r101_fpn_syncbn-backbone_r4_gcb_c3-c5_1x_coco_20200206-8407a3f0.pth) | [log](https://download.openmmlab.com/mmdetection/v2.0/gcnet/mask_rcnn_r101_fpn_syncbn-backbone_r4_gcb_c3-c5_1x_coco/mask_rcnn_r101_fpn_syncbn-backbone_r4_gcb_c3-c5_1x_coco_20200206_142508.log.json) | +| X-101-FPN | Mask | - | 1x | 7.6 | 11.3 | 42.4 | 37.7 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/gcnet/mask_rcnn_x101_32x4d_fpn_syncbn-backbone_1x_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/gcnet/mask_rcnn_x101_32x4d_fpn_syncbn-backbone_1x_coco/mask_rcnn_x101_32x4d_fpn_syncbn-backbone_1x_coco_20200211-7584841c.pth) | [log](https://download.openmmlab.com/mmdetection/v2.0/gcnet/mask_rcnn_x101_32x4d_fpn_syncbn-backbone_1x_coco/mask_rcnn_x101_32x4d_fpn_syncbn-backbone_1x_coco_20200211_054326.log.json) | +| X-101-FPN | Mask | GC(c3-c5, r16) | 1x | 8.8 | 9.8 | 43.5 | 38.6 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/gcnet/mask_rcnn_x101_32x4d_fpn_syncbn-backbone_r16_gcb_c3-c5_1x_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/gcnet/mask_rcnn_x101_32x4d_fpn_syncbn-backbone_r16_gcb_c3-c5_1x_coco/mask_rcnn_x101_32x4d_fpn_syncbn-backbone_r16_gcb_c3-c5_1x_coco_20200211-cbed3d2c.pth) | [log](https://download.openmmlab.com/mmdetection/v2.0/gcnet/mask_rcnn_x101_32x4d_fpn_syncbn-backbone_r16_gcb_c3-c5_1x_coco/mask_rcnn_x101_32x4d_fpn_syncbn-backbone_r16_gcb_c3-c5_1x_coco_20200211_164715.log.json) | +| X-101-FPN | Mask | GC(c3-c5, r4) | 1x | 9.0 | 9.7 | 43.9 | 39.0 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/gcnet/mask_rcnn_x101_32x4d_fpn_syncbn-backbone_r4_gcb_c3-c5_1x_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/gcnet/mask_rcnn_x101_32x4d_fpn_syncbn-backbone_r4_gcb_c3-c5_1x_coco/mask_rcnn_x101_32x4d_fpn_syncbn-backbone_r4_gcb_c3-c5_1x_coco_20200212-68164964.pth) | [log](https://download.openmmlab.com/mmdetection/v2.0/gcnet/mask_rcnn_x101_32x4d_fpn_syncbn-backbone_r4_gcb_c3-c5_1x_coco/mask_rcnn_x101_32x4d_fpn_syncbn-backbone_r4_gcb_c3-c5_1x_coco_20200212_070942.log.json) | +| X-101-FPN | Cascade Mask | - | 1x | 9.2 | 8.4 | 44.7 | 38.6 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/gcnet/cascade_mask_rcnn_x101_32x4d_fpn_syncbn-backbone_1x_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/gcnet/cascade_mask_rcnn_x101_32x4d_fpn_syncbn-backbone_1x_coco/cascade_mask_rcnn_x101_32x4d_fpn_syncbn-backbone_1x_coco_20200310-d5ad2a5e.pth) | [log](https://download.openmmlab.com/mmdetection/v2.0/gcnet/cascade_mask_rcnn_x101_32x4d_fpn_syncbn-backbone_1x_coco/cascade_mask_rcnn_x101_32x4d_fpn_syncbn-backbone_1x_coco_20200310_115217.log.json) | +| X-101-FPN | Cascade Mask | GC(c3-c5, r16) | 1x | 10.3 | 7.7 | 46.2 | 39.7 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/gcnet/cascade_mask_rcnn_x101_32x4d_fpn_syncbn-backbone_r16_gcb_c3-c5_1x_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/gcnet/cascade_mask_rcnn_x101_32x4d_fpn_syncbn-backbone_r16_gcb_c3-c5_1x_coco/cascade_mask_rcnn_x101_32x4d_fpn_syncbn-backbone_r16_gcb_c3-c5_1x_coco_20200211-10bf2463.pth) | [log](https://download.openmmlab.com/mmdetection/v2.0/gcnet/cascade_mask_rcnn_x101_32x4d_fpn_syncbn-backbone_r16_gcb_c3-c5_1x_coco/cascade_mask_rcnn_x101_32x4d_fpn_syncbn-backbone_r16_gcb_c3-c5_1x_coco_20200211_184154.log.json) | +| X-101-FPN | Cascade Mask | GC(c3-c5, r4) | 1x | 10.6 | | 46.4 | 40.1 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/gcnet/cascade_mask_rcnn_x101_32x4d_fpn_syncbn-backbone_r4_gcb_c3-c5_1x_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/gcnet/cascade_mask_rcnn_x101_32x4d_fpn_syncbn-backbone_r4_gcb_c3-c5_1x_coco/cascade_mask_rcnn_x101_32x4d_fpn_syncbn-backbone_r4_gcb_c3-c5_1x_coco_20200703_180653-ed035291.pth) | [log](https://download.openmmlab.com/mmdetection/v2.0/gcnet/cascade_mask_rcnn_x101_32x4d_fpn_syncbn-backbone_r4_gcb_c3-c5_1x_coco/cascade_mask_rcnn_x101_32x4d_fpn_syncbn-backbone_r4_gcb_c3-c5_1x_coco_20200703_180653.log.json) | +| X-101-FPN | DCN Cascade Mask | - | 1x | | | 47.5 | 40.9 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/gcnet/cascade_mask_rcnn_x101_32x4d_fpn_syncbn-backbone_dconv_c3-c5_1x_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/gcnet/cascade_mask_rcnn_x101_32x4d_fpn_syncbn-backbone_dconv_c3-c5_1x_coco/cascade_mask_rcnn_x101_32x4d_fpn_syncbn-backbone_dconv_c3-c5_1x_coco_20210615_211019-abbc39ea.pth) | [log](https://download.openmmlab.com/mmdetection/v2.0/gcnet/cascade_mask_rcnn_x101_32x4d_fpn_syncbn-backbone_dconv_c3-c5_1x_coco/cascade_mask_rcnn_x101_32x4d_fpn_syncbn-backbone_dconv_c3-c5_1x_coco_20210615_211019.log.json)| +| X-101-FPN | DCN Cascade Mask | GC(c3-c5, r16) | 1x | | | 48.0 | 41.3 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/gcnet/cascade_mask_rcnn_x101_32x4d_fpn_syncbn-backbone_dconv_c3-c5_r16_gcb_c3-c5_1x_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/gcnet/cascade_mask_rcnn_x101_32x4d_fpn_syncbn-backbone_dconv_c3-c5_r16_gcb_c3-c5_1x_coco/cascade_mask_rcnn_x101_32x4d_fpn_syncbn-backbone_dconv_c3-c5_r16_gcb_c3-c5_1x_coco_20210615_215648-44aa598a.pth) | [log](https://download.openmmlab.com/mmdetection/v2.0/gcnet/cascade_mask_rcnn_x101_32x4d_fpn_syncbn-backbone_dconv_c3-c5_r16_gcb_c3-c5_1x_coco/cascade_mask_rcnn_x101_32x4d_fpn_syncbn-backbone_dconv_c3-c5_r16_gcb_c3-c5_1x_coco_20210615_215648.log.json) | +| X-101-FPN | DCN Cascade Mask | GC(c3-c5, r4) | 1x | | | 47.9 | 41.1 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/gcnet/cascade_mask_rcnn_x101_32x4d_fpn_syncbn-backbone_dconv_c3-c5_r4_gcb_c3-c5_1x_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/gcnet/cascade_mask_rcnn_x101_32x4d_fpn_syncbn-backbone_dconv_c3-c5_r4_gcb_c3-c5_1x_coco/cascade_mask_rcnn_x101_32x4d_fpn_syncbn-backbone_dconv_c3-c5_r4_gcb_c3-c5_1x_coco_20210615_161851-720338ec.pth) | [log](https://download.openmmlab.com/mmdetection/v2.0/gcnet/cascade_mask_rcnn_x101_32x4d_fpn_syncbn-backbone_dconv_c3-c5_r4_gcb_c3-c5_1x_coco/cascade_mask_rcnn_x101_32x4d_fpn_syncbn-backbone_dconv_c3-c5_r4_gcb_c3-c5_1x_coco_20210615_161851.log.json) | **Notes:** diff --git a/configs/gcnet/cascade_mask_rcnn_x101_32x4d_fpn_syncbn-backbone_dconv_c3-c5_1x_coco.py b/configs/gcnet/cascade_mask_rcnn_x101_32x4d_fpn_syncbn-backbone_dconv_c3-c5_1x_coco.py index 464aef7..413499d 100644 --- a/configs/gcnet/cascade_mask_rcnn_x101_32x4d_fpn_syncbn-backbone_dconv_c3-c5_1x_coco.py +++ b/configs/gcnet/cascade_mask_rcnn_x101_32x4d_fpn_syncbn-backbone_dconv_c3-c5_1x_coco.py @@ -1,4 +1,4 @@ -_base_ = '../dcn/cascade_mask_rcnn_r50_fpn_dconv_c3-c5_1x_coco.py' +_base_ = '../dcn/cascade_mask_rcnn_x101_32x4d_fpn_dconv_c3-c5_1x_coco.py' model = dict( backbone=dict( norm_cfg=dict(type='SyncBN', requires_grad=True), norm_eval=False)) diff --git a/configs/gcnet/cascade_mask_rcnn_x101_32x4d_fpn_syncbn-backbone_dconv_c3-c5_r16_gcb_c3-c5_1x_coco.py b/configs/gcnet/cascade_mask_rcnn_x101_32x4d_fpn_syncbn-backbone_dconv_c3-c5_r16_gcb_c3-c5_1x_coco.py index fa4b6f1..50689aa 100644 --- a/configs/gcnet/cascade_mask_rcnn_x101_32x4d_fpn_syncbn-backbone_dconv_c3-c5_r16_gcb_c3-c5_1x_coco.py +++ b/configs/gcnet/cascade_mask_rcnn_x101_32x4d_fpn_syncbn-backbone_dconv_c3-c5_r16_gcb_c3-c5_1x_coco.py @@ -1,4 +1,4 @@ -_base_ = '../dcn/cascade_rcnn_r50_fpn_dconv_c3-c5_1x_coco.py' +_base_ = '../dcn/cascade_mask_rcnn_x101_32x4d_fpn_dconv_c3-c5_1x_coco.py' model = dict( backbone=dict( norm_cfg=dict(type='SyncBN', requires_grad=True), diff --git a/configs/gcnet/cascade_mask_rcnn_x101_32x4d_fpn_syncbn-backbone_dconv_c3-c5_r4_gcb_c3-c5_1x_coco.py b/configs/gcnet/cascade_mask_rcnn_x101_32x4d_fpn_syncbn-backbone_dconv_c3-c5_r4_gcb_c3-c5_1x_coco.py index b76e3e6..1367231 100644 --- a/configs/gcnet/cascade_mask_rcnn_x101_32x4d_fpn_syncbn-backbone_dconv_c3-c5_r4_gcb_c3-c5_1x_coco.py +++ b/configs/gcnet/cascade_mask_rcnn_x101_32x4d_fpn_syncbn-backbone_dconv_c3-c5_r4_gcb_c3-c5_1x_coco.py @@ -1,4 +1,4 @@ -_base_ = '../dcn/cascade_mask_rcnn_r50_fpn_dconv_c3-c5_1x_coco.py' +_base_ = '../dcn/cascade_mask_rcnn_x101_32x4d_fpn_dconv_c3-c5_1x_coco.py' model = dict( backbone=dict( norm_cfg=dict(type='SyncBN', requires_grad=True), diff --git a/configs/gcnet/metafile.yml b/configs/gcnet/metafile.yml new file mode 100644 index 0000000..1281122 --- /dev/null +++ b/configs/gcnet/metafile.yml @@ -0,0 +1,440 @@ +Collections: + - Name: GCNet + Metadata: + Training Data: COCO + Training Techniques: + - SGD with Momentum + - Weight Decay + Training Resources: 8x V100 GPUs + Architecture: + - Global Context Block + - FPN + - RPN + - ResNet + - ResNeXt + Paper: + URL: https://arxiv.org/abs/1904.11492 + Title: 'GCNet: Non-local Networks Meet Squeeze-Excitation Networks and Beyond' + README: configs/gcnet/README.md + Code: + URL: https://github.com/open-mmlab/mmdetection/blob/v2.0.0/mmdet/ops/context_block.py#L13 + Version: v2.0.0 + +Models: + - Name: mask_rcnn_r50_fpn_r16_gcb_c3-c5_1x_coco + In Collection: GCNet + Config: configs/gcnet/mask_rcnn_r50_fpn_r16_gcb_c3-c5_1x_coco.py + Metadata: + Training Memory (GB): 5.0 + Epochs: 12 + Results: + - Task: Object Detection + Dataset: COCO + Metrics: + box AP: 39.7 + - Task: Instance Segmentation + Dataset: COCO + Metrics: + mask AP: 35.9 + Weights: https://download.openmmlab.com/mmdetection/v2.0/gcnet/mask_rcnn_r50_fpn_r16_gcb_c3-c5_1x_coco/mask_rcnn_r50_fpn_r16_gcb_c3-c5_1x_coco_20200515_211915-187da160.pth + + - Name: mask_rcnn_r50_fpn_r4_gcb_c3-c5_1x_coco + In Collection: GCNet + Config: configs/gcnet/mask_rcnn_r50_fpn_r4_gcb_c3-c5_1x_coco.py + Metadata: + Training Memory (GB): 5.1 + inference time (ms/im): + - value: 66.67 + hardware: V100 + backend: PyTorch + batch size: 1 + mode: FP32 + resolution: (800, 1333) + Epochs: 12 + Results: + - Task: Object Detection + Dataset: COCO + Metrics: + box AP: 39.9 + - Task: Instance Segmentation + Dataset: COCO + Metrics: + mask AP: 36.0 + Weights: https://download.openmmlab.com/mmdetection/v2.0/gcnet/mask_rcnn_r50_fpn_r4_gcb_c3-c5_1x_coco/mask_rcnn_r50_fpn_r4_gcb_c3-c5_1x_coco_20200204-17235656.pth + + - Name: mask_rcnn_r101_fpn_r16_gcb_c3-c5_1x_coco + In Collection: GCNet + Config: configs/gcnet/mask_rcnn_r101_fpn_r16_gcb_c3-c5_1x_coco.py + Metadata: + Training Memory (GB): 7.6 + inference time (ms/im): + - value: 87.72 + hardware: V100 + backend: PyTorch + batch size: 1 + mode: FP32 + resolution: (800, 1333) + Epochs: 12 + Results: + - Task: Object Detection + Dataset: COCO + Metrics: + box AP: 41.3 + - Task: Instance Segmentation + Dataset: COCO + Metrics: + mask AP: 37.2 + Weights: https://download.openmmlab.com/mmdetection/v2.0/gcnet/mask_rcnn_r101_fpn_r16_gcb_c3-c5_1x_coco/mask_rcnn_r101_fpn_r16_gcb_c3-c5_1x_coco_20200205-e58ae947.pth + + - Name: mask_rcnn_r101_fpn_r4_gcb_c3-c5_1x_coco + In Collection: GCNet + Config: configs/gcnet/mask_rcnn_r101_fpn_r4_gcb_c3-c5_1x_coco.py + Metadata: + Training Memory (GB): 7.8 + inference time (ms/im): + - value: 86.21 + hardware: V100 + backend: PyTorch + batch size: 1 + mode: FP32 + resolution: (800, 1333) + Epochs: 12 + Results: + - Task: Object Detection + Dataset: COCO + Metrics: + box AP: 42.2 + - Task: Instance Segmentation + Dataset: COCO + Metrics: + mask AP: 37.8 + Weights: https://download.openmmlab.com/mmdetection/v2.0/gcnet/mask_rcnn_r101_fpn_r4_gcb_c3-c5_1x_coco/mask_rcnn_r101_fpn_r4_gcb_c3-c5_1x_coco_20200206-af22dc9d.pth + + - Name: mask_rcnn_r50_fpn_syncbn-backbone_1x_coco + In Collection: GCNet + Config: configs/gcnet/mask_rcnn_r50_fpn_syncbn-backbone_1x_coco.py + Metadata: + Training Memory (GB): 4.4 + inference time (ms/im): + - value: 60.24 + hardware: V100 + backend: PyTorch + batch size: 1 + mode: FP32 + resolution: (800, 1333) + Epochs: 12 + Results: + - Task: Object Detection + Dataset: COCO + Metrics: + box AP: 38.4 + - Task: Instance Segmentation + Dataset: COCO + Metrics: + mask AP: 34.6 + Weights: https://download.openmmlab.com/mmdetection/v2.0/gcnet/mask_rcnn_r50_fpn_syncbn-backbone_1x_coco/mask_rcnn_r50_fpn_syncbn-backbone_1x_coco_20200202-bb3eb55c.pth + + - Name: mask_rcnn_r50_fpn_syncbn-backbone_r16_gcb_c3-c5_1x_coco + In Collection: GCNet + Config: configs/gcnet/mask_rcnn_r50_fpn_syncbn-backbone_r16_gcb_c3-c5_1x_coco.py + Metadata: + Training Memory (GB): 5.0 + inference time (ms/im): + - value: 64.52 + hardware: V100 + backend: PyTorch + batch size: 1 + mode: FP32 + resolution: (800, 1333) + Epochs: 12 + Results: + - Task: Object Detection + Dataset: COCO + Metrics: + box AP: 40.4 + - Task: Instance Segmentation + Dataset: COCO + Metrics: + mask AP: 36.2 + Weights: https://download.openmmlab.com/mmdetection/v2.0/gcnet/mask_rcnn_r50_fpn_syncbn-backbone_r16_gcb_c3-c5_1x_coco/mask_rcnn_r50_fpn_syncbn-backbone_r16_gcb_c3-c5_1x_coco_20200202-587b99aa.pth + + - Name: mask_rcnn_r50_fpn_syncbn-backbone_r4_gcb_c3-c5_1x_coco + In Collection: GCNet + Config: configs/gcnet/mask_rcnn_r50_fpn_syncbn-backbone_r4_gcb_c3-c5_1x_coco.py + Metadata: + Training Memory (GB): 5.1 + inference time (ms/im): + - value: 66.23 + hardware: V100 + backend: PyTorch + batch size: 1 + mode: FP32 + resolution: (800, 1333) + Epochs: 12 + Results: + - Task: Object Detection + Dataset: COCO + Metrics: + box AP: 40.7 + - Task: Instance Segmentation + Dataset: COCO + Metrics: + mask AP: 36.5 + Weights: https://download.openmmlab.com/mmdetection/v2.0/gcnet/mask_rcnn_r50_fpn_syncbn-backbone_r4_gcb_c3-c5_1x_coco/mask_rcnn_r50_fpn_syncbn-backbone_r4_gcb_c3-c5_1x_coco_20200202-50b90e5c.pth + + - Name: mask_rcnn_r101_fpn_syncbn-backbone_1x_coco + In Collection: GCNet + Config: configs/gcnet/mask_rcnn_r101_fpn_syncbn-backbone_1x_coco.py + Metadata: + Training Memory (GB): 6.4 + inference time (ms/im): + - value: 75.19 + hardware: V100 + backend: PyTorch + batch size: 1 + mode: FP32 + resolution: (800, 1333) + Epochs: 12 + Results: + - Task: Object Detection + Dataset: COCO + Metrics: + box AP: 40.5 + - Task: Instance Segmentation + Dataset: COCO + Metrics: + mask AP: 36.3 + Weights: https://download.openmmlab.com/mmdetection/v2.0/gcnet/mask_rcnn_r101_fpn_syncbn-backbone_1x_coco/mask_rcnn_r101_fpn_syncbn-backbone_1x_coco_20200210-81658c8a.pth + + - Name: mask_rcnn_r101_fpn_syncbn-backbone_r16_gcb_c3-c5_1x_coco + In Collection: GCNet + Config: configs/gcnet/mask_rcnn_r101_fpn_syncbn-backbone_r16_gcb_c3-c5_1x_coco.py + Metadata: + Training Memory (GB): 7.6 + inference time (ms/im): + - value: 83.33 + hardware: V100 + backend: PyTorch + batch size: 1 + mode: FP32 + resolution: (800, 1333) + Epochs: 12 + Results: + - Task: Object Detection + Dataset: COCO + Metrics: + box AP: 42.2 + - Task: Instance Segmentation + Dataset: COCO + Metrics: + mask AP: 37.8 + Weights: https://download.openmmlab.com/mmdetection/v2.0/gcnet/mask_rcnn_r101_fpn_syncbn-backbone_r16_gcb_c3-c5_1x_coco/mask_rcnn_r101_fpn_syncbn-backbone_r16_gcb_c3-c5_1x_coco_20200207-945e77ca.pth + + - Name: mask_rcnn_r101_fpn_syncbn-backbone_r4_gcb_c3-c5_1x_coco + In Collection: GCNet + Config: configs/gcnet/mask_rcnn_r101_fpn_syncbn-backbone_r4_gcb_c3-c5_1x_coco.py + Metadata: + Training Memory (GB): 7.8 + inference time (ms/im): + - value: 84.75 + hardware: V100 + backend: PyTorch + batch size: 1 + mode: FP32 + resolution: (800, 1333) + Epochs: 12 + Results: + - Task: Object Detection + Dataset: COCO + Metrics: + box AP: 42.2 + - Task: Instance Segmentation + Dataset: COCO + Metrics: + mask AP: 37.8 + Weights: https://download.openmmlab.com/mmdetection/v2.0/gcnet/mask_rcnn_r101_fpn_syncbn-backbone_r4_gcb_c3-c5_1x_coco/mask_rcnn_r101_fpn_syncbn-backbone_r4_gcb_c3-c5_1x_coco_20200206-8407a3f0.pth + + - Name: mask_rcnn_x101_32x4d_fpn_syncbn-backbone_1x_coco + In Collection: GCNet + Config: configs/gcnet/mask_rcnn_x101_32x4d_fpn_syncbn-backbone_1x_coco.py + Metadata: + Training Memory (GB): 7.6 + inference time (ms/im): + - value: 88.5 + hardware: V100 + backend: PyTorch + batch size: 1 + mode: FP32 + resolution: (800, 1333) + Epochs: 12 + Results: + - Task: Object Detection + Dataset: COCO + Metrics: + box AP: 42.4 + - Task: Instance Segmentation + Dataset: COCO + Metrics: + mask AP: 37.7 + Weights: https://download.openmmlab.com/mmdetection/v2.0/gcnet/mask_rcnn_x101_32x4d_fpn_syncbn-backbone_1x_coco/mask_rcnn_x101_32x4d_fpn_syncbn-backbone_1x_coco_20200211-7584841c.pth + + - Name: mask_rcnn_x101_32x4d_fpn_syncbn-backbone_r16_gcb_c3-c5_1x_coco + In Collection: GCNet + Config: configs/gcnet/mask_rcnn_x101_32x4d_fpn_syncbn-backbone_r16_gcb_c3-c5_1x_coco.py + Metadata: + Training Memory (GB): 8.8 + inference time (ms/im): + - value: 102.04 + hardware: V100 + backend: PyTorch + batch size: 1 + mode: FP32 + resolution: (800, 1333) + Epochs: 12 + Results: + - Task: Object Detection + Dataset: COCO + Metrics: + box AP: 43.5 + - Task: Instance Segmentation + Dataset: COCO + Metrics: + mask AP: 38.6 + Weights: https://download.openmmlab.com/mmdetection/v2.0/gcnet/mask_rcnn_x101_32x4d_fpn_syncbn-backbone_r16_gcb_c3-c5_1x_coco/mask_rcnn_x101_32x4d_fpn_syncbn-backbone_r16_gcb_c3-c5_1x_coco_20200211-cbed3d2c.pth + + - Name: mask_rcnn_x101_32x4d_fpn_syncbn-backbone_r4_gcb_c3-c5_1x_coco + In Collection: GCNet + Config: configs/gcnet/mask_rcnn_x101_32x4d_fpn_syncbn-backbone_r4_gcb_c3-c5_1x_coco.py + Metadata: + Training Memory (GB): 9.0 + inference time (ms/im): + - value: 103.09 + hardware: V100 + backend: PyTorch + batch size: 1 + mode: FP32 + resolution: (800, 1333) + Epochs: 12 + Results: + - Task: Object Detection + Dataset: COCO + Metrics: + box AP: 43.9 + - Task: Instance Segmentation + Dataset: COCO + Metrics: + mask AP: 39.0 + Weights: https://download.openmmlab.com/mmdetection/v2.0/gcnet/mask_rcnn_x101_32x4d_fpn_syncbn-backbone_r4_gcb_c3-c5_1x_coco/mask_rcnn_x101_32x4d_fpn_syncbn-backbone_r4_gcb_c3-c5_1x_coco_20200212-68164964.pth + + - Name: cascade_mask_rcnn_x101_32x4d_fpn_syncbn-backbone_1x_coco + In Collection: GCNet + Config: configs/gcnet/cascade_mask_rcnn_x101_32x4d_fpn_syncbn-backbone_1x_coco.py + Metadata: + Training Memory (GB): 9.2 + inference time (ms/im): + - value: 119.05 + hardware: V100 + backend: PyTorch + batch size: 1 + mode: FP32 + resolution: (800, 1333) + Epochs: 12 + Results: + - Task: Object Detection + Dataset: COCO + Metrics: + box AP: 44.7 + - Task: Instance Segmentation + Dataset: COCO + Metrics: + mask AP: 38.6 + Weights: https://download.openmmlab.com/mmdetection/v2.0/gcnet/cascade_mask_rcnn_x101_32x4d_fpn_syncbn-backbone_1x_coco/cascade_mask_rcnn_x101_32x4d_fpn_syncbn-backbone_1x_coco_20200310-d5ad2a5e.pth + + - Name: cascade_mask_rcnn_x101_32x4d_fpn_syncbn-backbone_r16_gcb_c3-c5_1x_coco + In Collection: GCNet + Config: configs/gcnet/cascade_mask_rcnn_x101_32x4d_fpn_syncbn-backbone_r16_gcb_c3-c5_1x_coco.py + Metadata: + Training Memory (GB): 10.3 + inference time (ms/im): + - value: 129.87 + hardware: V100 + backend: PyTorch + batch size: 1 + mode: FP32 + resolution: (800, 1333) + Epochs: 12 + Results: + - Task: Object Detection + Dataset: COCO + Metrics: + box AP: 46.2 + - Task: Instance Segmentation + Dataset: COCO + Metrics: + mask AP: 39.7 + Weights: https://download.openmmlab.com/mmdetection/v2.0/gcnet/cascade_mask_rcnn_x101_32x4d_fpn_syncbn-backbone_r16_gcb_c3-c5_1x_coco/cascade_mask_rcnn_x101_32x4d_fpn_syncbn-backbone_r16_gcb_c3-c5_1x_coco_20200211-10bf2463.pth + + - Name: cascade_mask_rcnn_x101_32x4d_fpn_syncbn-backbone_r4_gcb_c3-c5_1x_coco + In Collection: GCNet + Config: configs/gcnet/cascade_mask_rcnn_x101_32x4d_fpn_syncbn-backbone_r4_gcb_c3-c5_1x_coco.py + Metadata: + Training Memory (GB): 10.6 + Epochs: 12 + Results: + - Task: Object Detection + Dataset: COCO + Metrics: + box AP: 46.4 + - Task: Instance Segmentation + Dataset: COCO + Metrics: + mask AP: 40.1 + Weights: https://download.openmmlab.com/mmdetection/v2.0/gcnet/cascade_mask_rcnn_x101_32x4d_fpn_syncbn-backbone_r4_gcb_c3-c5_1x_coco/cascade_mask_rcnn_x101_32x4d_fpn_syncbn-backbone_r4_gcb_c3-c5_1x_coco_20200703_180653-ed035291.pth + + - Name: cascade_mask_rcnn_x101_32x4d_fpn_syncbn-backbone_dconv_c3-c5_1x_coco + In Collection: GCNet + Config: configs/gcnet/cascade_mask_rcnn_x101_32x4d_fpn_syncbn-backbone_dconv_c3-c5_1x_coco.py + Metadata: + Epochs: 12 + Results: + - Task: Object Detection + Dataset: COCO + Metrics: + box AP: 47.5 + - Task: Instance Segmentation + Dataset: COCO + Metrics: + mask AP: 40.9 + Weights: https://download.openmmlab.com/mmdetection/v2.0/gcnet/cascade_mask_rcnn_x101_32x4d_fpn_syncbn-backbone_dconv_c3-c5_1x_coco/cascade_mask_rcnn_x101_32x4d_fpn_syncbn-backbone_dconv_c3-c5_1x_coco_20210615_211019-abbc39ea.pth + + - Name: cascade_mask_rcnn_x101_32x4d_fpn_syncbn-backbone_dconv_c3-c5_r16_gcb_c3-c5_1x_coco + In Collection: GCNet + Config: configs/gcnet/cascade_mask_rcnn_x101_32x4d_fpn_syncbn-backbone_dconv_c3-c5_r16_gcb_c3-c5_1x_coco.py + Metadata: + Epochs: 12 + Results: + - Task: Object Detection + Dataset: COCO + Metrics: + box AP: 48.0 + - Task: Instance Segmentation + Dataset: COCO + Metrics: + mask AP: 41.3 + Weights: https://download.openmmlab.com/mmdetection/v2.0/gcnet/cascade_mask_rcnn_x101_32x4d_fpn_syncbn-backbone_dconv_c3-c5_r16_gcb_c3-c5_1x_coco/cascade_mask_rcnn_x101_32x4d_fpn_syncbn-backbone_dconv_c3-c5_r16_gcb_c3-c5_1x_coco_20210615_215648-44aa598a.pth + + - Name: cascade_mask_rcnn_x101_32x4d_fpn_syncbn-backbone_dconv_c3-c5_r4_gcb_c3-c5_1x_coco + In Collection: GCNet + Config: configs/gcnet/cascade_mask_rcnn_x101_32x4d_fpn_syncbn-backbone_dconv_c3-c5_r4_gcb_c3-c5_1x_coco.py + Metadata: + Epochs: 12 + Results: + - Task: Object Detection + Dataset: COCO + Metrics: + box AP: 47.9 + - Task: Instance Segmentation + Dataset: COCO + Metrics: + mask AP: 41.1 + Weights: https://download.openmmlab.com/mmdetection/v2.0/gcnet/cascade_mask_rcnn_x101_32x4d_fpn_syncbn-backbone_dconv_c3-c5_r4_gcb_c3-c5_1x_coco/cascade_mask_rcnn_x101_32x4d_fpn_syncbn-backbone_dconv_c3-c5_r4_gcb_c3-c5_1x_coco_20210615_161851-720338ec.pth diff --git a/configs/gfl/README.md b/configs/gfl/README.md index 53ae22b..51a6aa1 100644 --- a/configs/gfl/README.md +++ b/configs/gfl/README.md @@ -2,7 +2,7 @@ ## Introduction -[ALGORITHM] + We provide config files to reproduce the object detection results in the paper [Generalized Focal Loss: Learning Qualified and Distributed Bounding Boxes for Dense Object Detection](https://arxiv.org/abs/2006.04388) @@ -19,12 +19,12 @@ We provide config files to reproduce the object detection results in the paper [ | Backbone | Style | Lr schd | Multi-scale Training| Inf time (fps) | box AP | Config | Download | |:-----------------:|:-------:|:-------:|:-------------------:|:--------------:|:------:|:------:|:--------:| -| R-50 | pytorch | 1x | No | 19.5 | 40.2 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/gfl/gfl_r50_fpn_1x_coco.py) | [model](http://download.openmmlab.com/mmdetection/v2.0/gfl/gfl_r50_fpn_1x_coco/gfl_r50_fpn_1x_coco_20200629_121244-25944287.pth) | [log](http://download.openmmlab.com/mmdetection/v2.0/gfl/gfl_r50_fpn_1x_coco/gfl_r50_fpn_1x_coco_20200629_121244.log.json) | -| R-50 | pytorch | 2x | Yes | 19.5 | 42.9 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/gfl/gfl_r50_fpn_mstrain_2x_coco.py) | [model](http://download.openmmlab.com/mmdetection/v2.0/gfl/gfl_r50_fpn_mstrain_2x_coco/gfl_r50_fpn_mstrain_2x_coco_20200629_213802-37bb1edc.pth) | [log](http://download.openmmlab.com/mmdetection/v2.0/gfl/gfl_r50_fpn_mstrain_2x_coco/gfl_r50_fpn_mstrain_2x_coco_20200629_213802.log.json) | -| R-101 | pytorch | 2x | Yes | 14.7 | 44.7 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/gfl/gfl_r101_fpn_mstrain_2x_coco.py) | [model](http://download.openmmlab.com/mmdetection/v2.0/gfl/gfl_r101_fpn_mstrain_2x_coco/gfl_r101_fpn_mstrain_2x_coco_20200629_200126-dd12f847.pth) | [log](http://download.openmmlab.com/mmdetection/v2.0/gfl/gfl_r101_fpn_mstrain_2x_coco/gfl_r101_fpn_mstrain_2x_coco_20200629_200126.log.json) | -| R-101-dcnv2 | pytorch | 2x | Yes | 12.9 | 47.1 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/gfl/gfl_r101_fpn_dconv_c3-c5_mstrain_2x_coco.py) | [model](http://download.openmmlab.com/mmdetection/v2.0/gfl/gfl_r101_fpn_dconv_c3-c5_mstrain_2x_coco/gfl_r101_fpn_dconv_c3-c5_mstrain_2x_coco_20200630_102002-134b07df.pth) | [log](http://download.openmmlab.com/mmdetection/v2.0/gfl/gfl_r101_fpn_dconv_c3-c5_mstrain_2x_coco/gfl_r101_fpn_dconv_c3-c5_mstrain_2x_coco_20200630_102002.log.json) | -| X-101-32x4d | pytorch | 2x | Yes | 12.1 | 45.9 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/gfl/gfl_x101_32x4d_fpn_mstrain_2x_coco.py) | [model](http://download.openmmlab.com/mmdetection/v2.0/gfl/gfl_x101_32x4d_fpn_mstrain_2x_coco/gfl_x101_32x4d_fpn_mstrain_2x_coco_20200630_102002-50c1ffdb.pth) | [log](http://download.openmmlab.com/mmdetection/v2.0/gfl/gfl_x101_32x4d_fpn_mstrain_2x_coco/gfl_x101_32x4d_fpn_mstrain_2x_coco_20200630_102002.log.json) | -| X-101-32x4d-dcnv2 | pytorch | 2x | Yes | 10.7 | 48.1 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/gfl/gfl_x101_32x4d_fpn_dconv_c4-c5_mstrain_2x_coco.py) | [model](http://download.openmmlab.com/mmdetection/v2.0/gfl/gfl_x101_32x4d_fpn_dconv_c4-c5_mstrain_2x_coco/gfl_x101_32x4d_fpn_dconv_c4-c5_mstrain_2x_coco_20200630_102002-14a2bf25.pth) | [log](http://download.openmmlab.com/mmdetection/v2.0/gfl/gfl_x101_32x4d_fpn_dconv_c4-c5_mstrain_2x_coco/gfl_x101_32x4d_fpn_dconv_c4-c5_mstrain_2x_coco_20200630_102002.log.json) | +| R-50 | pytorch | 1x | No | 19.5 | 40.2 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/gfl/gfl_r50_fpn_1x_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/gfl/gfl_r50_fpn_1x_coco/gfl_r50_fpn_1x_coco_20200629_121244-25944287.pth) | [log](https://download.openmmlab.com/mmdetection/v2.0/gfl/gfl_r50_fpn_1x_coco/gfl_r50_fpn_1x_coco_20200629_121244.log.json) | +| R-50 | pytorch | 2x | Yes | 19.5 | 42.9 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/gfl/gfl_r50_fpn_mstrain_2x_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/gfl/gfl_r50_fpn_mstrain_2x_coco/gfl_r50_fpn_mstrain_2x_coco_20200629_213802-37bb1edc.pth) | [log](https://download.openmmlab.com/mmdetection/v2.0/gfl/gfl_r50_fpn_mstrain_2x_coco/gfl_r50_fpn_mstrain_2x_coco_20200629_213802.log.json) | +| R-101 | pytorch | 2x | Yes | 14.7 | 44.7 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/gfl/gfl_r101_fpn_mstrain_2x_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/gfl/gfl_r101_fpn_mstrain_2x_coco/gfl_r101_fpn_mstrain_2x_coco_20200629_200126-dd12f847.pth) | [log](https://download.openmmlab.com/mmdetection/v2.0/gfl/gfl_r101_fpn_mstrain_2x_coco/gfl_r101_fpn_mstrain_2x_coco_20200629_200126.log.json) | +| R-101-dcnv2 | pytorch | 2x | Yes | 12.9 | 47.1 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/gfl/gfl_r101_fpn_dconv_c3-c5_mstrain_2x_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/gfl/gfl_r101_fpn_dconv_c3-c5_mstrain_2x_coco/gfl_r101_fpn_dconv_c3-c5_mstrain_2x_coco_20200630_102002-134b07df.pth) | [log](https://download.openmmlab.com/mmdetection/v2.0/gfl/gfl_r101_fpn_dconv_c3-c5_mstrain_2x_coco/gfl_r101_fpn_dconv_c3-c5_mstrain_2x_coco_20200630_102002.log.json) | +| X-101-32x4d | pytorch | 2x | Yes | 12.1 | 45.9 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/gfl/gfl_x101_32x4d_fpn_mstrain_2x_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/gfl/gfl_x101_32x4d_fpn_mstrain_2x_coco/gfl_x101_32x4d_fpn_mstrain_2x_coco_20200630_102002-50c1ffdb.pth) | [log](https://download.openmmlab.com/mmdetection/v2.0/gfl/gfl_x101_32x4d_fpn_mstrain_2x_coco/gfl_x101_32x4d_fpn_mstrain_2x_coco_20200630_102002.log.json) | +| X-101-32x4d-dcnv2 | pytorch | 2x | Yes | 10.7 | 48.1 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/gfl/gfl_x101_32x4d_fpn_dconv_c4-c5_mstrain_2x_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/gfl/gfl_x101_32x4d_fpn_dconv_c4-c5_mstrain_2x_coco/gfl_x101_32x4d_fpn_dconv_c4-c5_mstrain_2x_coco_20200630_102002-14a2bf25.pth) | [log](https://download.openmmlab.com/mmdetection/v2.0/gfl/gfl_x101_32x4d_fpn_dconv_c4-c5_mstrain_2x_coco/gfl_x101_32x4d_fpn_dconv_c4-c5_mstrain_2x_coco_20200630_102002.log.json) | [1] *1x and 2x mean the model is trained for 90K and 180K iterations, respectively.* \ [2] *All results are obtained with a single model and without any test time data augmentation such as multi-scale, flipping and etc..* \ diff --git a/configs/gfl/gfl_r101_fpn_dconv_c3-c5_mstrain_2x_coco.py b/configs/gfl/gfl_r101_fpn_dconv_c3-c5_mstrain_2x_coco.py index eab622b..b72c2b6 100644 --- a/configs/gfl/gfl_r101_fpn_dconv_c3-c5_mstrain_2x_coco.py +++ b/configs/gfl/gfl_r101_fpn_dconv_c3-c5_mstrain_2x_coco.py @@ -1,6 +1,5 @@ _base_ = './gfl_r50_fpn_mstrain_2x_coco.py' model = dict( - pretrained='torchvision://resnet101', backbone=dict( type='ResNet', depth=101, @@ -11,4 +10,6 @@ dcn=dict(type='DCN', deform_groups=1, fallback_on_stride=False), stage_with_dcn=(False, True, True, True), norm_eval=True, - style='pytorch')) + style='pytorch', + init_cfg=dict(type='Pretrained', + checkpoint='torchvision://resnet101'))) diff --git a/configs/gfl/gfl_r101_fpn_mstrain_2x_coco.py b/configs/gfl/gfl_r101_fpn_mstrain_2x_coco.py index c972d0c..e33b5c0 100644 --- a/configs/gfl/gfl_r101_fpn_mstrain_2x_coco.py +++ b/configs/gfl/gfl_r101_fpn_mstrain_2x_coco.py @@ -1,6 +1,5 @@ _base_ = './gfl_r50_fpn_mstrain_2x_coco.py' model = dict( - pretrained='torchvision://resnet101', backbone=dict( type='ResNet', depth=101, @@ -9,4 +8,6 @@ frozen_stages=1, norm_cfg=dict(type='BN', requires_grad=True), norm_eval=True, - style='pytorch')) + style='pytorch', + init_cfg=dict(type='Pretrained', + checkpoint='torchvision://resnet101'))) diff --git a/configs/gfl/gfl_r50_fpn_1x_coco.py b/configs/gfl/gfl_r50_fpn_1x_coco.py index 29fb077..cfd4b02 100644 --- a/configs/gfl/gfl_r50_fpn_1x_coco.py +++ b/configs/gfl/gfl_r50_fpn_1x_coco.py @@ -4,7 +4,6 @@ ] model = dict( type='GFL', - pretrained='torchvision://resnet50', backbone=dict( type='ResNet', depth=50, @@ -13,7 +12,8 @@ frozen_stages=1, norm_cfg=dict(type='BN', requires_grad=True), norm_eval=True, - style='pytorch'), + style='pytorch', + init_cfg=dict(type='Pretrained', checkpoint='torchvision://resnet50')), neck=dict( type='FPN', in_channels=[256, 512, 1024, 2048], diff --git a/configs/gfl/gfl_x101_32x4d_fpn_dconv_c4-c5_mstrain_2x_coco.py b/configs/gfl/gfl_x101_32x4d_fpn_dconv_c4-c5_mstrain_2x_coco.py index a2370e2..2539807 100644 --- a/configs/gfl/gfl_x101_32x4d_fpn_dconv_c4-c5_mstrain_2x_coco.py +++ b/configs/gfl/gfl_x101_32x4d_fpn_dconv_c4-c5_mstrain_2x_coco.py @@ -1,7 +1,6 @@ _base_ = './gfl_r50_fpn_mstrain_2x_coco.py' model = dict( type='GFL', - pretrained='open-mmlab://resnext101_32x4d', backbone=dict( type='ResNeXt', depth=101, @@ -14,4 +13,6 @@ dcn=dict(type='DCN', deform_groups=1, fallback_on_stride=False), stage_with_dcn=(False, False, True, True), norm_eval=True, - style='pytorch')) + style='pytorch', + init_cfg=dict( + type='Pretrained', checkpoint='open-mmlab://resnext101_32x4d'))) diff --git a/configs/gfl/gfl_x101_32x4d_fpn_mstrain_2x_coco.py b/configs/gfl/gfl_x101_32x4d_fpn_mstrain_2x_coco.py index 4e00a05..effda19 100644 --- a/configs/gfl/gfl_x101_32x4d_fpn_mstrain_2x_coco.py +++ b/configs/gfl/gfl_x101_32x4d_fpn_mstrain_2x_coco.py @@ -1,7 +1,6 @@ _base_ = './gfl_r50_fpn_mstrain_2x_coco.py' model = dict( type='GFL', - pretrained='open-mmlab://resnext101_32x4d', backbone=dict( type='ResNeXt', depth=101, @@ -12,4 +11,6 @@ frozen_stages=1, norm_cfg=dict(type='BN', requires_grad=True), norm_eval=True, - style='pytorch')) + style='pytorch', + init_cfg=dict( + type='Pretrained', checkpoint='open-mmlab://resnext101_32x4d'))) diff --git a/configs/gfl/metafile.yml b/configs/gfl/metafile.yml new file mode 100644 index 0000000..8f049c6 --- /dev/null +++ b/configs/gfl/metafile.yml @@ -0,0 +1,134 @@ +Collections: + - Name: Generalized Focal Loss + Metadata: + Training Data: COCO + Training Techniques: + - SGD with Momentum + - Weight Decay + Training Resources: 8x V100 GPUs + Architecture: + - Generalized Focal Loss + - FPN + - ResNet + Paper: + URL: https://arxiv.org/abs/2006.04388 + Title: 'Generalized Focal Loss: Learning Qualified and Distributed Bounding Boxes for Dense Object Detection' + README: configs/gfl/README.md + Code: + URL: https://github.com/open-mmlab/mmdetection/blob/v2.2.0/mmdet/models/detectors/gfl.py#L6 + Version: v2.2.0 + +Models: + - Name: gfl_r50_fpn_1x_coco + In Collection: Generalized Focal Loss + Config: configs/gfl/gfl_r50_fpn_1x_coco.py + Metadata: + inference time (ms/im): + - value: 51.28 + hardware: V100 + backend: PyTorch + batch size: 1 + mode: FP32 + resolution: (800, 1333) + Epochs: 12 + Results: + - Task: Object Detection + Dataset: COCO + Metrics: + box AP: 40.2 + Weights: https://download.openmmlab.com/mmdetection/v2.0/gfl/gfl_r50_fpn_1x_coco/gfl_r50_fpn_1x_coco_20200629_121244-25944287.pth + + - Name: gfl_r50_fpn_mstrain_2x_coco + In Collection: Generalized Focal Loss + Config: configs/gfl/gfl_r50_fpn_mstrain_2x_coco.py + Metadata: + inference time (ms/im): + - value: 51.28 + hardware: V100 + backend: PyTorch + batch size: 1 + mode: FP32 + resolution: (800, 1333) + Epochs: 24 + Results: + - Task: Object Detection + Dataset: COCO + Metrics: + box AP: 42.9 + Weights: https://download.openmmlab.com/mmdetection/v2.0/gfl/gfl_r50_fpn_mstrain_2x_coco/gfl_r50_fpn_mstrain_2x_coco_20200629_213802-37bb1edc.pth + + - Name: gfl_r101_fpn_mstrain_2x_coco + In Collection: Generalized Focal Loss + Config: configs/gfl/gfl_r101_fpn_mstrain_2x_coco.py + Metadata: + inference time (ms/im): + - value: 68.03 + hardware: V100 + backend: PyTorch + batch size: 1 + mode: FP32 + resolution: (800, 1333) + Epochs: 24 + Results: + - Task: Object Detection + Dataset: COCO + Metrics: + box AP: 44.7 + Weights: https://download.openmmlab.com/mmdetection/v2.0/gfl/gfl_r101_fpn_mstrain_2x_coco/gfl_r101_fpn_mstrain_2x_coco_20200629_200126-dd12f847.pth + + - Name: gfl_r101_fpn_dconv_c3-c5_mstrain_2x_coco + In Collection: Generalized Focal Loss + Config: configs/gfl/gfl_r101_fpn_dconv_c3-c5_mstrain_2x_coco.py + Metadata: + inference time (ms/im): + - value: 77.52 + hardware: V100 + backend: PyTorch + batch size: 1 + mode: FP32 + resolution: (800, 1333) + Epochs: 24 + Results: + - Task: Object Detection + Dataset: COCO + Metrics: + box AP: 47.1 + Weights: https://download.openmmlab.com/mmdetection/v2.0/gfl/gfl_r101_fpn_dconv_c3-c5_mstrain_2x_coco/gfl_r101_fpn_dconv_c3-c5_mstrain_2x_coco_20200630_102002-134b07df.pth + + - Name: gfl_x101_32x4d_fpn_mstrain_2x_coco + In Collection: Generalized Focal Loss + Config: configs/gfl/gfl_x101_32x4d_fpn_mstrain_2x_coco.py + Metadata: + inference time (ms/im): + - value: 82.64 + hardware: V100 + backend: PyTorch + batch size: 1 + mode: FP32 + resolution: (800, 1333) + Epochs: 24 + Results: + - Task: Object Detection + Dataset: COCO + Metrics: + box AP: 45.9 + Weights: https://download.openmmlab.com/mmdetection/v2.0/gfl/gfl_x101_32x4d_fpn_mstrain_2x_coco/gfl_x101_32x4d_fpn_mstrain_2x_coco_20200630_102002-50c1ffdb.pth + + - Name: gfl_x101_32x4d_fpn_dconv_c4-c5_mstrain_2x_coco + In Collection: Generalized Focal Loss + Config: configs/gfl/gfl_x101_32x4d_fpn_dconv_c4-c5_mstrain_2x_coco.py + Metadata: + inference time (ms/im): + - value: 93.46 + hardware: V100 + backend: PyTorch + batch size: 1 + mode: FP32 + resolution: (800, 1333) + Epochs: 24 + Results: + - Task: Object Detection + Dataset: COCO + Metrics: + box AP: 48.1 + Weights: https://download.openmmlab.com/mmdetection/v2.0/gfl/gfl_x101_32x4d_fpn_dconv_c4-c5_mstrain_2x_coco/gfl_x101_32x4d_fpn_dconv_c4-c5_mstrain_2x_coco_20200630_102002-14a2bf25.pth diff --git a/configs/ghm/README.md b/configs/ghm/README.md index 6a70bcd..2a51ed8 100644 --- a/configs/ghm/README.md +++ b/configs/ghm/README.md @@ -2,7 +2,7 @@ ## Introduction -[ALGORITHM] + ``` @inproceedings{li2019gradient, @@ -17,7 +17,7 @@ | Backbone | Style | Lr schd | Mem (GB) | Inf time (fps) | box AP | Config | Download | | :-------------: | :-----: | :-----: | :------: | :------------: | :----: | :------: | :--------: | -| R-50-FPN | pytorch | 1x | 4.0 | 3.3 | 37.0 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/ghm/retinanet_ghm_r50_fpn_1x_coco.py) | [model](http://download.openmmlab.com/mmdetection/v2.0/ghm/retinanet_ghm_r50_fpn_1x_coco/retinanet_ghm_r50_fpn_1x_coco_20200130-a437fda3.pth) | [log](http://download.openmmlab.com/mmdetection/v2.0/ghm/retinanet_ghm_r50_fpn_1x_coco/retinanet_ghm_r50_fpn_1x_coco_20200130_004213.log.json) | -| R-101-FPN | pytorch | 1x | 6.0 | 4.4 | 39.1 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/ghm/retinanet_ghm_r101_fpn_1x_coco.py) | [model](http://download.openmmlab.com/mmdetection/v2.0/ghm/retinanet_ghm_r101_fpn_1x_coco/retinanet_ghm_r101_fpn_1x_coco_20200130-c148ee8f.pth) | [log](http://download.openmmlab.com/mmdetection/v2.0/ghm/retinanet_ghm_r101_fpn_1x_coco/retinanet_ghm_r101_fpn_1x_coco_20200130_145259.log.json) | -| X-101-32x4d-FPN | pytorch | 1x | 7.2 | 5.1 | 40.7 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/ghm/retinanet_ghm_x101_32x4d_fpn_1x_coco.py) | [model](http://download.openmmlab.com/mmdetection/v2.0/ghm/retinanet_ghm_x101_32x4d_fpn_1x_coco/retinanet_ghm_x101_32x4d_fpn_1x_coco_20200131-e4333bd0.pth) | [log](http://download.openmmlab.com/mmdetection/v2.0/ghm/retinanet_ghm_x101_32x4d_fpn_1x_coco/retinanet_ghm_x101_32x4d_fpn_1x_coco_20200131_113653.log.json) | -| X-101-64x4d-FPN | pytorch | 1x | 10.3 | 5.2 | 41.4 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/ghm/retinanet_ghm_x101_64x4d_fpn_1x_coco.py) | [model](http://download.openmmlab.com/mmdetection/v2.0/ghm/retinanet_ghm_x101_64x4d_fpn_1x_coco/retinanet_ghm_x101_64x4d_fpn_1x_coco_20200131-dd381cef.pth) | [log](http://download.openmmlab.com/mmdetection/v2.0/ghm/retinanet_ghm_x101_64x4d_fpn_1x_coco/retinanet_ghm_x101_64x4d_fpn_1x_coco_20200131_113723.log.json) | +| R-50-FPN | pytorch | 1x | 4.0 | 3.3 | 37.0 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/ghm/retinanet_ghm_r50_fpn_1x_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/ghm/retinanet_ghm_r50_fpn_1x_coco/retinanet_ghm_r50_fpn_1x_coco_20200130-a437fda3.pth) | [log](https://download.openmmlab.com/mmdetection/v2.0/ghm/retinanet_ghm_r50_fpn_1x_coco/retinanet_ghm_r50_fpn_1x_coco_20200130_004213.log.json) | +| R-101-FPN | pytorch | 1x | 6.0 | 4.4 | 39.1 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/ghm/retinanet_ghm_r101_fpn_1x_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/ghm/retinanet_ghm_r101_fpn_1x_coco/retinanet_ghm_r101_fpn_1x_coco_20200130-c148ee8f.pth) | [log](https://download.openmmlab.com/mmdetection/v2.0/ghm/retinanet_ghm_r101_fpn_1x_coco/retinanet_ghm_r101_fpn_1x_coco_20200130_145259.log.json) | +| X-101-32x4d-FPN | pytorch | 1x | 7.2 | 5.1 | 40.7 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/ghm/retinanet_ghm_x101_32x4d_fpn_1x_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/ghm/retinanet_ghm_x101_32x4d_fpn_1x_coco/retinanet_ghm_x101_32x4d_fpn_1x_coco_20200131-e4333bd0.pth) | [log](https://download.openmmlab.com/mmdetection/v2.0/ghm/retinanet_ghm_x101_32x4d_fpn_1x_coco/retinanet_ghm_x101_32x4d_fpn_1x_coco_20200131_113653.log.json) | +| X-101-64x4d-FPN | pytorch | 1x | 10.3 | 5.2 | 41.4 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/ghm/retinanet_ghm_x101_64x4d_fpn_1x_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/ghm/retinanet_ghm_x101_64x4d_fpn_1x_coco/retinanet_ghm_x101_64x4d_fpn_1x_coco_20200131-dd381cef.pth) | [log](https://download.openmmlab.com/mmdetection/v2.0/ghm/retinanet_ghm_x101_64x4d_fpn_1x_coco/retinanet_ghm_x101_64x4d_fpn_1x_coco_20200131_113723.log.json) | diff --git a/configs/ghm/metafile.yml b/configs/ghm/metafile.yml new file mode 100644 index 0000000..b4f488c --- /dev/null +++ b/configs/ghm/metafile.yml @@ -0,0 +1,101 @@ +Collections: + - Name: GHM + Metadata: + Training Data: COCO + Training Techniques: + - SGD with Momentum + - Weight Decay + Training Resources: 8x V100 GPUs + Architecture: + - GHM-C + - GHM-R + - FPN + - ResNet + Paper: + URL: https://arxiv.org/abs/1811.05181 + Title: 'Gradient Harmonized Single-stage Detector' + README: configs/ghm/README.md + Code: + URL: https://github.com/open-mmlab/mmdetection/blob/v2.0.0/mmdet/models/losses/ghm_loss.py#L21 + Version: v2.0.0 + +Models: + - Name: retinanet_ghm_r50_fpn_1x_coco + In Collection: GHM + Config: configs/ghm/retinanet_ghm_r50_fpn_1x_coco.py + Metadata: + Training Memory (GB): 4.0 + inference time (ms/im): + - value: 303.03 + hardware: V100 + backend: PyTorch + batch size: 1 + mode: FP32 + resolution: (800, 1333) + Epochs: 12 + Results: + - Task: Object Detection + Dataset: COCO + Metrics: + box AP: 37.0 + Weights: https://download.openmmlab.com/mmdetection/v2.0/ghm/retinanet_ghm_r50_fpn_1x_coco/retinanet_ghm_r50_fpn_1x_coco_20200130-a437fda3.pth + + - Name: retinanet_ghm_r101_fpn_1x_coco + In Collection: GHM + Config: configs/ghm/retinanet_ghm_r101_fpn_1x_coco.py + Metadata: + Training Memory (GB): 6.0 + inference time (ms/im): + - value: 227.27 + hardware: V100 + backend: PyTorch + batch size: 1 + mode: FP32 + resolution: (800, 1333) + Epochs: 12 + Results: + - Task: Object Detection + Dataset: COCO + Metrics: + box AP: 39.1 + Weights: https://download.openmmlab.com/mmdetection/v2.0/ghm/retinanet_ghm_r101_fpn_1x_coco/retinanet_ghm_r101_fpn_1x_coco_20200130-c148ee8f.pth + + - Name: retinanet_ghm_x101_32x4d_fpn_1x_coco + In Collection: GHM + Config: configs/ghm/retinanet_ghm_x101_32x4d_fpn_1x_coco.py + Metadata: + Training Memory (GB): 7.2 + inference time (ms/im): + - value: 196.08 + hardware: V100 + backend: PyTorch + batch size: 1 + mode: FP32 + resolution: (800, 1333) + Epochs: 12 + Results: + - Task: Object Detection + Dataset: COCO + Metrics: + box AP: 40.7 + Weights: https://download.openmmlab.com/mmdetection/v2.0/ghm/retinanet_ghm_x101_32x4d_fpn_1x_coco/retinanet_ghm_x101_32x4d_fpn_1x_coco_20200131-e4333bd0.pth + + - Name: retinanet_ghm_x101_64x4d_fpn_1x_coco + In Collection: GHM + Config: configs/ghm/retinanet_ghm_x101_64x4d_fpn_1x_coco.py + Metadata: + Training Memory (GB): 10.3 + inference time (ms/im): + - value: 192.31 + hardware: V100 + backend: PyTorch + batch size: 1 + mode: FP32 + resolution: (800, 1333) + Epochs: 12 + Results: + - Task: Object Detection + Dataset: COCO + Metrics: + box AP: 41.4 + Weights: https://download.openmmlab.com/mmdetection/v2.0/ghm/retinanet_ghm_x101_64x4d_fpn_1x_coco/retinanet_ghm_x101_64x4d_fpn_1x_coco_20200131-dd381cef.pth diff --git a/configs/ghm/retinanet_ghm_r101_fpn_1x_coco.py b/configs/ghm/retinanet_ghm_r101_fpn_1x_coco.py index 18f899a..aaf6fc2 100644 --- a/configs/ghm/retinanet_ghm_r101_fpn_1x_coco.py +++ b/configs/ghm/retinanet_ghm_r101_fpn_1x_coco.py @@ -1,2 +1,6 @@ _base_ = './retinanet_ghm_r50_fpn_1x_coco.py' -model = dict(pretrained='torchvision://resnet101', backbone=dict(depth=101)) +model = dict( + backbone=dict( + depth=101, + init_cfg=dict(type='Pretrained', + checkpoint='torchvision://resnet101'))) diff --git a/configs/ghm/retinanet_ghm_x101_32x4d_fpn_1x_coco.py b/configs/ghm/retinanet_ghm_x101_32x4d_fpn_1x_coco.py index a89fc13..cd2e4cc 100644 --- a/configs/ghm/retinanet_ghm_x101_32x4d_fpn_1x_coco.py +++ b/configs/ghm/retinanet_ghm_x101_32x4d_fpn_1x_coco.py @@ -1,6 +1,5 @@ _base_ = './retinanet_ghm_r50_fpn_1x_coco.py' model = dict( - pretrained='open-mmlab://resnext101_32x4d', backbone=dict( type='ResNeXt', depth=101, @@ -10,4 +9,6 @@ out_indices=(0, 1, 2, 3), frozen_stages=1, norm_cfg=dict(type='BN', requires_grad=True), - style='pytorch')) + style='pytorch', + init_cfg=dict( + type='Pretrained', checkpoint='open-mmlab://resnext101_32x4d'))) diff --git a/configs/ghm/retinanet_ghm_x101_64x4d_fpn_1x_coco.py b/configs/ghm/retinanet_ghm_x101_64x4d_fpn_1x_coco.py index 88013f5..b6107d8 100644 --- a/configs/ghm/retinanet_ghm_x101_64x4d_fpn_1x_coco.py +++ b/configs/ghm/retinanet_ghm_x101_64x4d_fpn_1x_coco.py @@ -1,6 +1,5 @@ _base_ = './retinanet_ghm_r50_fpn_1x_coco.py' model = dict( - pretrained='open-mmlab://resnext101_64x4d', backbone=dict( type='ResNeXt', depth=101, @@ -10,4 +9,6 @@ out_indices=(0, 1, 2, 3), frozen_stages=1, norm_cfg=dict(type='BN', requires_grad=True), - style='pytorch')) + style='pytorch', + init_cfg=dict( + type='Pretrained', checkpoint='open-mmlab://resnext101_64x4d'))) diff --git a/configs/gn+ws/README.md b/configs/gn+ws/README.md index 988fb13..8001b0d 100644 --- a/configs/gn+ws/README.md +++ b/configs/gn+ws/README.md @@ -2,7 +2,7 @@ ## Introduction -[ALGORITHM] + ``` @article{weightstandardization, @@ -19,23 +19,23 @@ Faster R-CNN | Backbone | Style | Normalization | Lr schd | Mem (GB) | Inf time (fps) | box AP | mask AP | Config | Download | |:---------:|:-------:|:-------------:|:-------:|:--------:|:--------------:|:------:|:-------:|:------:|:--------:| -| R-50-FPN | pytorch | GN+WS | 1x | 5.9 | 11.7 | 39.7 | - | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/gn%2Bws/faster_rcnn_r50_fpn_gn_ws-all_1x_coco.py) | [model](http://download.openmmlab.com/mmdetection/v2.0/gn%2Bws/faster_rcnn_r50_fpn_gn_ws-all_1x_coco/faster_rcnn_r50_fpn_gn_ws-all_1x_coco_20200130-613d9fe2.pth) | [log](http://download.openmmlab.com/mmdetection/v2.0/gn%2Bws/faster_rcnn_r50_fpn_gn_ws-all_1x_coco/faster_rcnn_r50_fpn_gn_ws-all_1x_coco_20200130_210936.log.json) | -| R-101-FPN | pytorch | GN+WS | 1x | 8.9 | 9.0 | 41.7 | - | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/gn%2Bws/faster_rcnn_r101_fpn_gn_ws-all_1x_coco.py) | [model](http://download.openmmlab.com/mmdetection/v2.0/gn%2Bws/faster_rcnn_r101_fpn_gn_ws-all_1x_coco/faster_rcnn_r101_fpn_gn_ws-all_1x_coco_20200205-a93b0d75.pth) | [log](http://download.openmmlab.com/mmdetection/v2.0/gn%2Bws/faster_rcnn_r101_fpn_gn_ws-all_1x_coco/faster_rcnn_r101_fpn_gn_ws-all_1x_coco_20200205_232146.log.json) | -| X-50-32x4d-FPN | pytorch | GN+WS | 1x | 7.0 | 10.3 | 40.7 | - | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/gn%2Bws/faster_rcnn_x50_32x4d_fpn_gn_ws-all_1x_coco.py) | [model](http://download.openmmlab.com/mmdetection/v2.0/gn%2Bws/faster_rcnn_x50_32x4d_fpn_gn_ws-all_1x_coco/faster_rcnn_x50_32x4d_fpn_gn_ws-all_1x_coco_20200203-839c5d9d.pth) | [log](http://download.openmmlab.com/mmdetection/v2.0/gn%2Bws/faster_rcnn_x50_32x4d_fpn_gn_ws-all_1x_coco/faster_rcnn_x50_32x4d_fpn_gn_ws-all_1x_coco_20200203_220113.log.json) | -| X-101-32x4d-FPN | pytorch | GN+WS | 1x | 10.8 | 7.6 | 42.1 | - | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/gn%2Bws/faster_rcnn_x101_32x4d_fpn_gn_ws-all_1x_coco.py) | [model](http://download.openmmlab.com/mmdetection/v2.0/gn%2Bws/faster_rcnn_x101_32x4d_fpn_gn_ws-all_1x_coco/faster_rcnn_x101_32x4d_fpn_gn_ws-all_1x_coco_20200212-27da1bc2.pth) | [log](http://download.openmmlab.com/mmdetection/v2.0/gn%2Bws/faster_rcnn_x101_32x4d_fpn_gn_ws-all_1x_coco/faster_rcnn_x101_32x4d_fpn_gn_ws-all_1x_coco_20200212_195302.log.json) | +| R-50-FPN | pytorch | GN+WS | 1x | 5.9 | 11.7 | 39.7 | - | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/gn%2Bws/faster_rcnn_r50_fpn_gn_ws-all_1x_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/gn%2Bws/faster_rcnn_r50_fpn_gn_ws-all_1x_coco/faster_rcnn_r50_fpn_gn_ws-all_1x_coco_20200130-613d9fe2.pth) | [log](https://download.openmmlab.com/mmdetection/v2.0/gn%2Bws/faster_rcnn_r50_fpn_gn_ws-all_1x_coco/faster_rcnn_r50_fpn_gn_ws-all_1x_coco_20200130_210936.log.json) | +| R-101-FPN | pytorch | GN+WS | 1x | 8.9 | 9.0 | 41.7 | - | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/gn%2Bws/faster_rcnn_r101_fpn_gn_ws-all_1x_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/gn%2Bws/faster_rcnn_r101_fpn_gn_ws-all_1x_coco/faster_rcnn_r101_fpn_gn_ws-all_1x_coco_20200205-a93b0d75.pth) | [log](https://download.openmmlab.com/mmdetection/v2.0/gn%2Bws/faster_rcnn_r101_fpn_gn_ws-all_1x_coco/faster_rcnn_r101_fpn_gn_ws-all_1x_coco_20200205_232146.log.json) | +| X-50-32x4d-FPN | pytorch | GN+WS | 1x | 7.0 | 10.3 | 40.7 | - | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/gn%2Bws/faster_rcnn_x50_32x4d_fpn_gn_ws-all_1x_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/gn%2Bws/faster_rcnn_x50_32x4d_fpn_gn_ws-all_1x_coco/faster_rcnn_x50_32x4d_fpn_gn_ws-all_1x_coco_20200203-839c5d9d.pth) | [log](https://download.openmmlab.com/mmdetection/v2.0/gn%2Bws/faster_rcnn_x50_32x4d_fpn_gn_ws-all_1x_coco/faster_rcnn_x50_32x4d_fpn_gn_ws-all_1x_coco_20200203_220113.log.json) | +| X-101-32x4d-FPN | pytorch | GN+WS | 1x | 10.8 | 7.6 | 42.1 | - | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/gn%2Bws/faster_rcnn_x101_32x4d_fpn_gn_ws-all_1x_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/gn%2Bws/faster_rcnn_x101_32x4d_fpn_gn_ws-all_1x_coco/faster_rcnn_x101_32x4d_fpn_gn_ws-all_1x_coco_20200212-27da1bc2.pth) | [log](https://download.openmmlab.com/mmdetection/v2.0/gn%2Bws/faster_rcnn_x101_32x4d_fpn_gn_ws-all_1x_coco/faster_rcnn_x101_32x4d_fpn_gn_ws-all_1x_coco_20200212_195302.log.json) | Mask R-CNN | Backbone | Style | Normalization | Lr schd | Mem (GB) | Inf time (fps) | box AP | mask AP | Config | Download | |:---------:|:-------:|:-------------:|:---------:|:--------:|:--------------:|:------:|:-------:|:------:|:--------:| -| R-50-FPN | pytorch | GN+WS | 2x | 7.3 | 10.5 | 40.6 | 36.6 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/gn%2Bws/mask_rcnn_r50_fpn_gn_ws-all_2x_coco.py) | [model](http://download.openmmlab.com/mmdetection/v2.0/gn%2Bws/mask_rcnn_r50_fpn_gn_ws-all_2x_coco/mask_rcnn_r50_fpn_gn_ws-all_2x_coco_20200226-16acb762.pth) | [log](http://download.openmmlab.com/mmdetection/v2.0/gn%2Bws/mask_rcnn_r50_fpn_gn_ws-all_2x_coco/mask_rcnn_r50_fpn_gn_ws-all_2x_coco_20200226_062128.log.json) | -| R-101-FPN | pytorch | GN+WS | 2x | 10.3 | 8.6 | 42.0 | 37.7 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/gn%2Bws/mask_rcnn_r101_fpn_gn_ws-all_2x_coco.py) | [model](http://download.openmmlab.com/mmdetection/v2.0/gn%2Bws/mask_rcnn_r101_fpn_gn_ws-all_2x_coco/mask_rcnn_r101_fpn_gn_ws-all_2x_coco_20200212-ea357cd9.pth) | [log](http://download.openmmlab.com/mmdetection/v2.0/gn%2Bws/mask_rcnn_r101_fpn_gn_ws-all_2x_coco/mask_rcnn_r101_fpn_gn_ws-all_2x_coco_20200212_213627.log.json) | -| X-50-32x4d-FPN | pytorch | GN+WS | 2x | 8.4 | 9.3 | 41.1 | 37.0 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/gn%2Bws/mask_rcnn_x50_32x4d_fpn_gn_ws-all_2x_coco.py) | [model](http://download.openmmlab.com/mmdetection/v2.0/gn%2Bws/mask_rcnn_x50_32x4d_fpn_gn_ws-all_2x_coco/mask_rcnn_x50_32x4d_fpn_gn_ws-all_2x_coco_20200216-649fdb6f.pth) | [log](http://download.openmmlab.com/mmdetection/v2.0/gn%2Bws/mask_rcnn_x50_32x4d_fpn_gn_ws-all_2x_coco/mask_rcnn_x50_32x4d_fpn_gn_ws-all_2x_coco_20200216_201500.log.json) | -| X-101-32x4d-FPN | pytorch | GN+WS | 2x | 12.2 | 7.1 | 42.1 | 37.9 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/gn%2Bws/mask_rcnn_x101_32x4d_fpn_gn_ws-all_2x_coco.py) | [model](http://download.openmmlab.com/mmdetection/v2.0/gn%2Bws/mask_rcnn_x101_32x4d_fpn_gn_ws-all_2x_coco/mask_rcnn_x101_32x4d_fpn_gn_ws-all_2x_coco_20200319-33fb95b5.pth) | [log](http://download.openmmlab.com/mmdetection/v2.0/gn%2Bws/mask_rcnn_x101_32x4d_fpn_gn_ws-all_2x_coco/mask_rcnn_x101_32x4d_fpn_gn_ws-all_2x_coco_20200319_104101.log.json) | -| R-50-FPN | pytorch | GN+WS | 20-23-24e | 7.3 | - | 41.1 | 37.1 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/gn%2Bws/mask_rcnn_r50_fpn_gn_ws-all_20_23_24e_coco.py) | [model](http://download.openmmlab.com/mmdetection/v2.0/gn%2Bws/mask_rcnn_r50_fpn_gn_ws-all_20_23_24e_coco/mask_rcnn_r50_fpn_gn_ws-all_20_23_24e_coco_20200213-487d1283.pth) | [log](http://download.openmmlab.com/mmdetection/v2.0/gn%2Bws/mask_rcnn_r50_fpn_gn_ws-all_20_23_24e_coco/mask_rcnn_r50_fpn_gn_ws-all_20_23_24e_coco_20200213_035123.log.json) | -| R-101-FPN | pytorch | GN+WS | 20-23-24e | 10.3 | - | 43.1 | 38.6 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/gn%2Bws/mask_rcnn_r101_fpn_gn_ws-all_20_23_24e_coco.py) | [model](http://download.openmmlab.com/mmdetection/v2.0/gn%2Bws/mask_rcnn_r101_fpn_gn_ws-all_20_23_24e_coco/mask_rcnn_r101_fpn_gn_ws-all_20_23_24e_coco_20200213-57b5a50f.pth) | [log](http://download.openmmlab.com/mmdetection/v2.0/gn%2Bws/mask_rcnn_r101_fpn_gn_ws-all_20_23_24e_coco/mask_rcnn_r101_fpn_gn_ws-all_20_23_24e_coco_20200213_130142.log.json) | -| X-50-32x4d-FPN | pytorch | GN+WS | 20-23-24e | 8.4 | - | 42.1 | 38.0 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/gn%2Bws/mask_rcnn_x50_32x4d_fpn_gn_ws-all_20_23_24e_coco.py) | [model](http://download.openmmlab.com/mmdetection/v2.0/gn%2Bws/mask_rcnn_x50_32x4d_fpn_gn_ws-all_20_23_24e_coco/mask_rcnn_x50_32x4d_fpn_gn_ws-all_20_23_24e_coco_20200226-969bcb2c.pth) | [log](http://download.openmmlab.com/mmdetection/v2.0/gn%2Bws/mask_rcnn_x50_32x4d_fpn_gn_ws-all_20_23_24e_coco/mask_rcnn_x50_32x4d_fpn_gn_ws-all_20_23_24e_coco_20200226_093732.log.json) | -| X-101-32x4d-FPN | pytorch | GN+WS | 20-23-24e | 12.2 | - | 42.7 | 38.5 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/gn%2Bws/mask_rcnn_x101_32x4d_fpn_gn_ws-all_20_23_24e_coco.py) | [model](http://download.openmmlab.com/mmdetection/v2.0/gn%2Bws/mask_rcnn_x101_32x4d_fpn_gn_ws-all_20_23_24e_coco/mask_rcnn_x101_32x4d_fpn_gn_ws-all_20_23_24e_coco_20200316-e6cd35ef.pth) | [log](http://download.openmmlab.com/mmdetection/v2.0/gn%2Bws/mask_rcnn_x101_32x4d_fpn_gn_ws-all_20_23_24e_coco/mask_rcnn_x101_32x4d_fpn_gn_ws-all_20_23_24e_coco_20200316_013741.log.json) | +| R-50-FPN | pytorch | GN+WS | 2x | 7.3 | 10.5 | 40.6 | 36.6 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/gn%2Bws/mask_rcnn_r50_fpn_gn_ws-all_2x_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/gn%2Bws/mask_rcnn_r50_fpn_gn_ws-all_2x_coco/mask_rcnn_r50_fpn_gn_ws-all_2x_coco_20200226-16acb762.pth) | [log](https://download.openmmlab.com/mmdetection/v2.0/gn%2Bws/mask_rcnn_r50_fpn_gn_ws-all_2x_coco/mask_rcnn_r50_fpn_gn_ws-all_2x_coco_20200226_062128.log.json) | +| R-101-FPN | pytorch | GN+WS | 2x | 10.3 | 8.6 | 42.0 | 37.7 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/gn%2Bws/mask_rcnn_r101_fpn_gn_ws-all_2x_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/gn%2Bws/mask_rcnn_r101_fpn_gn_ws-all_2x_coco/mask_rcnn_r101_fpn_gn_ws-all_2x_coco_20200212-ea357cd9.pth) | [log](https://download.openmmlab.com/mmdetection/v2.0/gn%2Bws/mask_rcnn_r101_fpn_gn_ws-all_2x_coco/mask_rcnn_r101_fpn_gn_ws-all_2x_coco_20200212_213627.log.json) | +| X-50-32x4d-FPN | pytorch | GN+WS | 2x | 8.4 | 9.3 | 41.1 | 37.0 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/gn%2Bws/mask_rcnn_x50_32x4d_fpn_gn_ws-all_2x_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/gn%2Bws/mask_rcnn_x50_32x4d_fpn_gn_ws-all_2x_coco/mask_rcnn_x50_32x4d_fpn_gn_ws-all_2x_coco_20200216-649fdb6f.pth) | [log](https://download.openmmlab.com/mmdetection/v2.0/gn%2Bws/mask_rcnn_x50_32x4d_fpn_gn_ws-all_2x_coco/mask_rcnn_x50_32x4d_fpn_gn_ws-all_2x_coco_20200216_201500.log.json) | +| X-101-32x4d-FPN | pytorch | GN+WS | 2x | 12.2 | 7.1 | 42.1 | 37.9 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/gn%2Bws/mask_rcnn_x101_32x4d_fpn_gn_ws-all_2x_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/gn%2Bws/mask_rcnn_x101_32x4d_fpn_gn_ws-all_2x_coco/mask_rcnn_x101_32x4d_fpn_gn_ws-all_2x_coco_20200319-33fb95b5.pth) | [log](https://download.openmmlab.com/mmdetection/v2.0/gn%2Bws/mask_rcnn_x101_32x4d_fpn_gn_ws-all_2x_coco/mask_rcnn_x101_32x4d_fpn_gn_ws-all_2x_coco_20200319_104101.log.json) | +| R-50-FPN | pytorch | GN+WS | 20-23-24e | 7.3 | - | 41.1 | 37.1 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/gn%2Bws/mask_rcnn_r50_fpn_gn_ws-all_20_23_24e_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/gn%2Bws/mask_rcnn_r50_fpn_gn_ws-all_20_23_24e_coco/mask_rcnn_r50_fpn_gn_ws-all_20_23_24e_coco_20200213-487d1283.pth) | [log](https://download.openmmlab.com/mmdetection/v2.0/gn%2Bws/mask_rcnn_r50_fpn_gn_ws-all_20_23_24e_coco/mask_rcnn_r50_fpn_gn_ws-all_20_23_24e_coco_20200213_035123.log.json) | +| R-101-FPN | pytorch | GN+WS | 20-23-24e | 10.3 | - | 43.1 | 38.6 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/gn%2Bws/mask_rcnn_r101_fpn_gn_ws-all_20_23_24e_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/gn%2Bws/mask_rcnn_r101_fpn_gn_ws-all_20_23_24e_coco/mask_rcnn_r101_fpn_gn_ws-all_20_23_24e_coco_20200213-57b5a50f.pth) | [log](https://download.openmmlab.com/mmdetection/v2.0/gn%2Bws/mask_rcnn_r101_fpn_gn_ws-all_20_23_24e_coco/mask_rcnn_r101_fpn_gn_ws-all_20_23_24e_coco_20200213_130142.log.json) | +| X-50-32x4d-FPN | pytorch | GN+WS | 20-23-24e | 8.4 | - | 42.1 | 38.0 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/gn%2Bws/mask_rcnn_x50_32x4d_fpn_gn_ws-all_20_23_24e_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/gn%2Bws/mask_rcnn_x50_32x4d_fpn_gn_ws-all_20_23_24e_coco/mask_rcnn_x50_32x4d_fpn_gn_ws-all_20_23_24e_coco_20200226-969bcb2c.pth) | [log](https://download.openmmlab.com/mmdetection/v2.0/gn%2Bws/mask_rcnn_x50_32x4d_fpn_gn_ws-all_20_23_24e_coco/mask_rcnn_x50_32x4d_fpn_gn_ws-all_20_23_24e_coco_20200226_093732.log.json) | +| X-101-32x4d-FPN | pytorch | GN+WS | 20-23-24e | 12.2 | - | 42.7 | 38.5 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/gn%2Bws/mask_rcnn_x101_32x4d_fpn_gn_ws-all_20_23_24e_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/gn%2Bws/mask_rcnn_x101_32x4d_fpn_gn_ws-all_20_23_24e_coco/mask_rcnn_x101_32x4d_fpn_gn_ws-all_20_23_24e_coco_20200316-e6cd35ef.pth) | [log](https://download.openmmlab.com/mmdetection/v2.0/gn%2Bws/mask_rcnn_x101_32x4d_fpn_gn_ws-all_20_23_24e_coco/mask_rcnn_x101_32x4d_fpn_gn_ws-all_20_23_24e_coco_20200316_013741.log.json) | Note: diff --git a/configs/gn+ws/faster_rcnn_r101_fpn_gn_ws-all_1x_coco.py b/configs/gn+ws/faster_rcnn_r101_fpn_gn_ws-all_1x_coco.py index a5f6bd2..cd2cb2b 100644 --- a/configs/gn+ws/faster_rcnn_r101_fpn_gn_ws-all_1x_coco.py +++ b/configs/gn+ws/faster_rcnn_r101_fpn_gn_ws-all_1x_coco.py @@ -1,3 +1,6 @@ _base_ = './faster_rcnn_r50_fpn_gn_ws-all_1x_coco.py' model = dict( - pretrained='open-mmlab://jhu/resnet101_gn_ws', backbone=dict(depth=101)) + backbone=dict( + depth=101, + init_cfg=dict( + type='Pretrained', checkpoint='open-mmlab://jhu/resnet101_gn_ws'))) diff --git a/configs/gn+ws/faster_rcnn_r50_fpn_gn_ws-all_1x_coco.py b/configs/gn+ws/faster_rcnn_r50_fpn_gn_ws-all_1x_coco.py index 497267b..1b326b8 100644 --- a/configs/gn+ws/faster_rcnn_r50_fpn_gn_ws-all_1x_coco.py +++ b/configs/gn+ws/faster_rcnn_r50_fpn_gn_ws-all_1x_coco.py @@ -2,8 +2,11 @@ conv_cfg = dict(type='ConvWS') norm_cfg = dict(type='GN', num_groups=32, requires_grad=True) model = dict( - pretrained='open-mmlab://jhu/resnet50_gn_ws', - backbone=dict(conv_cfg=conv_cfg, norm_cfg=norm_cfg), + backbone=dict( + conv_cfg=conv_cfg, + norm_cfg=norm_cfg, + init_cfg=dict( + type='Pretrained', checkpoint='open-mmlab://jhu/resnet50_gn_ws')), neck=dict(conv_cfg=conv_cfg, norm_cfg=norm_cfg), roi_head=dict( bbox_head=dict( diff --git a/configs/gn+ws/faster_rcnn_x101_32x4d_fpn_gn_ws-all_1x_coco.py b/configs/gn+ws/faster_rcnn_x101_32x4d_fpn_gn_ws-all_1x_coco.py index 061ca69..f64ae89 100644 --- a/configs/gn+ws/faster_rcnn_x101_32x4d_fpn_gn_ws-all_1x_coco.py +++ b/configs/gn+ws/faster_rcnn_x101_32x4d_fpn_gn_ws-all_1x_coco.py @@ -2,7 +2,6 @@ conv_cfg = dict(type='ConvWS') norm_cfg = dict(type='GN', num_groups=32, requires_grad=True) model = dict( - pretrained='open-mmlab://jhu/resnext101_32x4d_gn_ws', backbone=dict( type='ResNeXt', depth=101, @@ -13,4 +12,7 @@ frozen_stages=1, style='pytorch', conv_cfg=conv_cfg, - norm_cfg=norm_cfg)) + norm_cfg=norm_cfg, + init_cfg=dict( + type='Pretrained', + checkpoint='open-mmlab://jhu/resnext101_32x4d_gn_ws'))) diff --git a/configs/gn+ws/faster_rcnn_x50_32x4d_fpn_gn_ws-all_1x_coco.py b/configs/gn+ws/faster_rcnn_x50_32x4d_fpn_gn_ws-all_1x_coco.py index 1268980..246851b 100644 --- a/configs/gn+ws/faster_rcnn_x50_32x4d_fpn_gn_ws-all_1x_coco.py +++ b/configs/gn+ws/faster_rcnn_x50_32x4d_fpn_gn_ws-all_1x_coco.py @@ -2,7 +2,6 @@ conv_cfg = dict(type='ConvWS') norm_cfg = dict(type='GN', num_groups=32, requires_grad=True) model = dict( - pretrained='open-mmlab://jhu/resnext50_32x4d_gn_ws', backbone=dict( type='ResNeXt', depth=50, @@ -13,4 +12,7 @@ frozen_stages=1, style='pytorch', conv_cfg=conv_cfg, - norm_cfg=norm_cfg)) + norm_cfg=norm_cfg, + init_cfg=dict( + type='Pretrained', + checkpoint='open-mmlab://jhu/resnext50_32x4d_gn_ws'))) diff --git a/configs/gn+ws/mask_rcnn_r101_fpn_gn_ws-all_2x_coco.py b/configs/gn+ws/mask_rcnn_r101_fpn_gn_ws-all_2x_coco.py index 4be6817..a9fa6a2 100644 --- a/configs/gn+ws/mask_rcnn_r101_fpn_gn_ws-all_2x_coco.py +++ b/configs/gn+ws/mask_rcnn_r101_fpn_gn_ws-all_2x_coco.py @@ -1,3 +1,6 @@ _base_ = './mask_rcnn_r50_fpn_gn_ws-all_2x_coco.py' model = dict( - pretrained='open-mmlab://jhu/resnet101_gn_ws', backbone=dict(depth=101)) + backbone=dict( + depth=101, + init_cfg=dict( + type='Pretrained', checkpoint='open-mmlab://jhu/resnet101_gn_ws'))) diff --git a/configs/gn+ws/mask_rcnn_r50_fpn_gn_ws-all_2x_coco.py b/configs/gn+ws/mask_rcnn_r50_fpn_gn_ws-all_2x_coco.py index b83e7b5..63be60f 100644 --- a/configs/gn+ws/mask_rcnn_r50_fpn_gn_ws-all_2x_coco.py +++ b/configs/gn+ws/mask_rcnn_r50_fpn_gn_ws-all_2x_coco.py @@ -2,8 +2,11 @@ conv_cfg = dict(type='ConvWS') norm_cfg = dict(type='GN', num_groups=32, requires_grad=True) model = dict( - pretrained='open-mmlab://jhu/resnet50_gn_ws', - backbone=dict(conv_cfg=conv_cfg, norm_cfg=norm_cfg), + backbone=dict( + conv_cfg=conv_cfg, + norm_cfg=norm_cfg, + init_cfg=dict( + type='Pretrained', checkpoint='open-mmlab://jhu/resnet50_gn_ws')), neck=dict(conv_cfg=conv_cfg, norm_cfg=norm_cfg), roi_head=dict( bbox_head=dict( diff --git a/configs/gn+ws/mask_rcnn_x101_32x4d_fpn_gn_ws-all_2x_coco.py b/configs/gn+ws/mask_rcnn_x101_32x4d_fpn_gn_ws-all_2x_coco.py index dbe8877..6498b03 100644 --- a/configs/gn+ws/mask_rcnn_x101_32x4d_fpn_gn_ws-all_2x_coco.py +++ b/configs/gn+ws/mask_rcnn_x101_32x4d_fpn_gn_ws-all_2x_coco.py @@ -3,7 +3,6 @@ conv_cfg = dict(type='ConvWS') norm_cfg = dict(type='GN', num_groups=32, requires_grad=True) model = dict( - pretrained='open-mmlab://jhu/resnext101_32x4d_gn_ws', backbone=dict( type='ResNeXt', depth=101, @@ -14,4 +13,7 @@ frozen_stages=1, style='pytorch', conv_cfg=conv_cfg, - norm_cfg=norm_cfg)) + norm_cfg=norm_cfg, + init_cfg=dict( + type='Pretrained', + checkpoint='open-mmlab://jhu/resnext101_32x4d_gn_ws'))) diff --git a/configs/gn+ws/mask_rcnn_x50_32x4d_fpn_gn_ws-all_2x_coco.py b/configs/gn+ws/mask_rcnn_x50_32x4d_fpn_gn_ws-all_2x_coco.py index 9bbc86e..7fac317 100644 --- a/configs/gn+ws/mask_rcnn_x50_32x4d_fpn_gn_ws-all_2x_coco.py +++ b/configs/gn+ws/mask_rcnn_x50_32x4d_fpn_gn_ws-all_2x_coco.py @@ -3,7 +3,6 @@ conv_cfg = dict(type='ConvWS') norm_cfg = dict(type='GN', num_groups=32, requires_grad=True) model = dict( - pretrained='open-mmlab://jhu/resnext50_32x4d_gn_ws', backbone=dict( type='ResNeXt', depth=50, @@ -14,4 +13,7 @@ frozen_stages=1, style='pytorch', conv_cfg=conv_cfg, - norm_cfg=norm_cfg)) + norm_cfg=norm_cfg, + init_cfg=dict( + type='Pretrained', + checkpoint='open-mmlab://jhu/resnext50_32x4d_gn_ws'))) diff --git a/configs/gn+ws/metafile.yml b/configs/gn+ws/metafile.yml new file mode 100644 index 0000000..bc89359 --- /dev/null +++ b/configs/gn+ws/metafile.yml @@ -0,0 +1,263 @@ +Collections: + - Name: Weight Standardization + Metadata: + Training Data: COCO + Training Techniques: + - SGD with Momentum + - Weight Decay + Training Resources: 8x V100 GPUs + Architecture: + - Group Normalization + - Weight Standardization + Paper: + URL: https://arxiv.org/abs/1903.10520 + Title: 'Weight Standardization' + README: configs/gn+ws/README.md + Code: + URL: https://github.com/open-mmlab/mmdetection/blob/v2.0.0/configs/gn%2Bws/mask_rcnn_r50_fpn_gn_ws-all_2x_coco.py + Version: v2.0.0 + +Models: + - Name: faster_rcnn_r50_fpn_gn_ws-all_1x_coco + In Collection: Weight Standardization + Config: configs/gn%2Bws/faster_rcnn_r50_fpn_gn_ws-all_1x_coco.py + Metadata: + Training Memory (GB): 5.9 + inference time (ms/im): + - value: 85.47 + hardware: V100 + backend: PyTorch + batch size: 1 + mode: FP32 + resolution: (800, 1333) + Epochs: 12 + Results: + - Task: Object Detection + Dataset: COCO + Metrics: + box AP: 39.7 + Weights: https://download.openmmlab.com/mmdetection/v2.0/gn%2Bws/faster_rcnn_r50_fpn_gn_ws-all_1x_coco/faster_rcnn_r50_fpn_gn_ws-all_1x_coco_20200130-613d9fe2.pth + + - Name: faster_rcnn_r101_fpn_gn_ws-all_1x_coco + In Collection: Weight Standardization + Config: configs/gn%2Bws/faster_rcnn_r101_fpn_gn_ws-all_1x_coco.py + Metadata: + Training Memory (GB): 8.9 + inference time (ms/im): + - value: 111.11 + hardware: V100 + backend: PyTorch + batch size: 1 + mode: FP32 + resolution: (800, 1333) + Epochs: 12 + Results: + - Task: Object Detection + Dataset: COCO + Metrics: + box AP: 41.7 + Weights: https://download.openmmlab.com/mmdetection/v2.0/gn%2Bws/faster_rcnn_r101_fpn_gn_ws-all_1x_coco/faster_rcnn_r101_fpn_gn_ws-all_1x_coco_20200205-a93b0d75.pth + + - Name: faster_rcnn_x50_32x4d_fpn_gn_ws-all_1x_coco + In Collection: Weight Standardization + Config: configs/gn%2Bws/faster_rcnn_x50_32x4d_fpn_gn_ws-all_1x_coco.py + Metadata: + Training Memory (GB): 7.0 + inference time (ms/im): + - value: 97.09 + hardware: V100 + backend: PyTorch + batch size: 1 + mode: FP32 + resolution: (800, 1333) + Epochs: 12 + Results: + - Task: Object Detection + Dataset: COCO + Metrics: + box AP: 40.7 + Weights: https://download.openmmlab.com/mmdetection/v2.0/gn%2Bws/faster_rcnn_x50_32x4d_fpn_gn_ws-all_1x_coco/faster_rcnn_x50_32x4d_fpn_gn_ws-all_1x_coco_20200203-839c5d9d.pth + + - Name: faster_rcnn_x101_32x4d_fpn_gn_ws-all_1x_coco + In Collection: Weight Standardization + Config: configs/gn%2Bws/faster_rcnn_x101_32x4d_fpn_gn_ws-all_1x_coco.py + Metadata: + Training Memory (GB): 10.8 + inference time (ms/im): + - value: 131.58 + hardware: V100 + backend: PyTorch + batch size: 1 + mode: FP32 + resolution: (800, 1333) + Epochs: 12 + Results: + - Task: Object Detection + Dataset: COCO + Metrics: + box AP: 42.1 + Weights: https://download.openmmlab.com/mmdetection/v2.0/gn%2Bws/faster_rcnn_x101_32x4d_fpn_gn_ws-all_1x_coco/faster_rcnn_x101_32x4d_fpn_gn_ws-all_1x_coco_20200212-27da1bc2.pth + + - Name: mask_rcnn_r50_fpn_gn_ws-all_2x_coco + In Collection: Weight Standardization + Config: configs/gn%2Bws/mask_rcnn_r50_fpn_gn_ws-all_2x_coco.py + Metadata: + Training Memory (GB): 7.3 + inference time (ms/im): + - value: 95.24 + hardware: V100 + backend: PyTorch + batch size: 1 + mode: FP32 + resolution: (800, 1333) + Epochs: 24 + Results: + - Task: Object Detection + Dataset: COCO + Metrics: + box AP: 40.6 + - Task: Instance Segmentation + Dataset: COCO + Metrics: + mask AP: 36.6 + Weights: https://download.openmmlab.com/mmdetection/v2.0/gn%2Bws/mask_rcnn_r50_fpn_gn_ws-all_2x_coco/mask_rcnn_r50_fpn_gn_ws-all_2x_coco_20200226-16acb762.pth + + - Name: mask_rcnn_r101_fpn_gn_ws-all_2x_coco + In Collection: Weight Standardization + Config: configs/gn%2Bws/mask_rcnn_r101_fpn_gn_ws-all_2x_coco.py + Metadata: + Training Memory (GB): 10.3 + inference time (ms/im): + - value: 116.28 + hardware: V100 + backend: PyTorch + batch size: 1 + mode: FP32 + resolution: (800, 1333) + Epochs: 24 + Results: + - Task: Object Detection + Dataset: COCO + Metrics: + box AP: 42.0 + - Task: Instance Segmentation + Dataset: COCO + Metrics: + mask AP: 37.7 + Weights: https://download.openmmlab.com/mmdetection/v2.0/gn%2Bws/mask_rcnn_r101_fpn_gn_ws-all_2x_coco/mask_rcnn_r101_fpn_gn_ws-all_2x_coco_20200212-ea357cd9.pth + + - Name: mask_rcnn_x50_32x4d_fpn_gn_ws-all_2x_coco + In Collection: Weight Standardization + Config: configs/gn%2Bws/mask_rcnn_x50_32x4d_fpn_gn_ws-all_2x_coco.py + Metadata: + Training Memory (GB): 8.4 + inference time (ms/im): + - value: 107.53 + hardware: V100 + backend: PyTorch + batch size: 1 + mode: FP32 + resolution: (800, 1333) + Epochs: 24 + Results: + - Task: Object Detection + Dataset: COCO + Metrics: + box AP: 41.1 + - Task: Instance Segmentation + Dataset: COCO + Metrics: + mask AP: 37.0 + Weights: https://download.openmmlab.com/mmdetection/v2.0/gn%2Bws/mask_rcnn_x50_32x4d_fpn_gn_ws-all_2x_coco/mask_rcnn_x50_32x4d_fpn_gn_ws-all_2x_coco_20200216-649fdb6f.pth + + - Name: mask_rcnn_x101_32x4d_fpn_gn_ws-all_2x_coco + In Collection: Weight Standardization + Config: configs/gn%2Bws/mask_rcnn_x101_32x4d_fpn_gn_ws-all_2x_coco.py + Metadata: + Training Memory (GB): 12.2 + inference time (ms/im): + - value: 140.85 + hardware: V100 + backend: PyTorch + batch size: 1 + mode: FP32 + resolution: (800, 1333) + Epochs: 24 + Results: + - Task: Object Detection + Dataset: COCO + Metrics: + box AP: 42.1 + - Task: Instance Segmentation + Dataset: COCO + Metrics: + mask AP: 37.9 + Weights: https://download.openmmlab.com/mmdetection/v2.0/gn%2Bws/mask_rcnn_x101_32x4d_fpn_gn_ws-all_2x_coco/mask_rcnn_x101_32x4d_fpn_gn_ws-all_2x_coco_20200319-33fb95b5.pth + + - Name: mask_rcnn_r50_fpn_gn_ws-all_20_23_24e_coco + In Collection: Weight Standardization + Config: configs/gn%2Bws/mask_rcnn_r50_fpn_gn_ws-all_20_23_24e_coco.py + Metadata: + Training Memory (GB): 7.3 + Epochs: 24 + Results: + - Task: Object Detection + Dataset: COCO + Metrics: + box AP: 41.1 + - Task: Instance Segmentation + Dataset: COCO + Metrics: + mask AP: 37.1 + Weights: https://download.openmmlab.com/mmdetection/v2.0/gn%2Bws/mask_rcnn_r50_fpn_gn_ws-all_20_23_24e_coco/mask_rcnn_r50_fpn_gn_ws-all_20_23_24e_coco_20200213-487d1283.pth + + - Name: mask_rcnn_r101_fpn_gn_ws-all_20_23_24e_coco + In Collection: Weight Standardization + Config: configs/gn%2Bws/mask_rcnn_r101_fpn_gn_ws-all_20_23_24e_coco.py + Metadata: + Training Memory (GB): 10.3 + Epochs: 24 + Results: + - Task: Object Detection + Dataset: COCO + Metrics: + box AP: 43.1 + - Task: Instance Segmentation + Dataset: COCO + Metrics: + mask AP: 38.6 + Weights: https://download.openmmlab.com/mmdetection/v2.0/gn%2Bws/mask_rcnn_r101_fpn_gn_ws-all_20_23_24e_coco/mask_rcnn_r101_fpn_gn_ws-all_20_23_24e_coco_20200213-57b5a50f.pth + + - Name: mask_rcnn_x50_32x4d_fpn_gn_ws-all_20_23_24e_coco + In Collection: Weight Standardization + Config: configs/gn%2Bws/mask_rcnn_x50_32x4d_fpn_gn_ws-all_20_23_24e_coco.py + Metadata: + Training Memory (GB): 8.4 + Epochs: 24 + Results: + - Task: Object Detection + Dataset: COCO + Metrics: + box AP: 42.1 + - Task: Instance Segmentation + Dataset: COCO + Metrics: + mask AP: 38.0 + Weights: https://download.openmmlab.com/mmdetection/v2.0/gn%2Bws/mask_rcnn_x50_32x4d_fpn_gn_ws-all_20_23_24e_coco/mask_rcnn_x50_32x4d_fpn_gn_ws-all_20_23_24e_coco_20200226-969bcb2c.pth + + - Name: mask_rcnn_x101_32x4d_fpn_gn_ws-all_20_23_24e_coco + In Collection: Weight Standardization + Config: configs/gn%2Bws/mask_rcnn_x101_32x4d_fpn_gn_ws-all_20_23_24e_coco.py + Metadata: + Training Memory (GB): 12.2 + Epochs: 24 + Results: + - Task: Object Detection + Dataset: COCO + Metrics: + box AP: 42.7 + - Task: Instance Segmentation + Dataset: COCO + Metrics: + mask AP: 38.5 + Weights: https://download.openmmlab.com/mmdetection/v2.0/gn%2Bws/mask_rcnn_x101_32x4d_fpn_gn_ws-all_20_23_24e_coco/mask_rcnn_x101_32x4d_fpn_gn_ws-all_20_23_24e_coco_20200316-e6cd35ef.pth diff --git a/configs/gn/README.md b/configs/gn/README.md index 48ee7ad..5c6e41f 100644 --- a/configs/gn/README.md +++ b/configs/gn/README.md @@ -2,7 +2,7 @@ ## Introduction -[ALGORITHM] + ```latex @inproceedings{wu2018group, @@ -17,12 +17,12 @@ | Backbone | model | Lr schd | Mem (GB) | Inf time (fps) | box AP | mask AP | Config | Download | |:-------------:|:----------:|:-------:|:--------:|:--------------:|:------:|:-------:|:------:|:--------:| -| R-50-FPN (d) | Mask R-CNN | 2x | 7.1 | 11.0 | 40.2 | 36.4 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/gn/mask_rcnn_r50_fpn_gn-all_2x_coco.py) | [model](http://download.openmmlab.com/mmdetection/v2.0/gn/mask_rcnn_r50_fpn_gn-all_2x_coco/mask_rcnn_r50_fpn_gn-all_2x_coco_20200206-8eee02a6.pth) | [log](http://download.openmmlab.com/mmdetection/v2.0/gn/mask_rcnn_r50_fpn_gn-all_2x_coco/mask_rcnn_r50_fpn_gn-all_2x_coco_20200206_050355.log.json) | -| R-50-FPN (d) | Mask R-CNN | 3x | 7.1 | - | 40.5 | 36.7 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/gn/mask_rcnn_r50_fpn_gn-all_3x_coco.py) | [model](http://download.openmmlab.com/mmdetection/v2.0/gn/mask_rcnn_r50_fpn_gn-all_3x_coco/mask_rcnn_r50_fpn_gn-all_3x_coco_20200214-8b23b1e5.pth) | [log](http://download.openmmlab.com/mmdetection/v2.0/gn/mask_rcnn_r50_fpn_gn-all_3x_coco/mask_rcnn_r50_fpn_gn-all_3x_coco_20200214_063512.log.json) | -| R-101-FPN (d) | Mask R-CNN | 2x | 9.9 | 9.0 | 41.9 | 37.6 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/gn/mask_rcnn_r101_fpn_gn-all_2x_coco.py) | [model](http://download.openmmlab.com/mmdetection/v2.0/gn/mask_rcnn_r101_fpn_gn-all_2x_coco/mask_rcnn_r101_fpn_gn-all_2x_coco_20200205-d96b1b50.pth) | [log](http://download.openmmlab.com/mmdetection/v2.0/gn/mask_rcnn_r101_fpn_gn-all_2x_coco/mask_rcnn_r101_fpn_gn-all_2x_coco_20200205_234402.log.json) | -| R-101-FPN (d) | Mask R-CNN | 3x | 9.9 | | 42.1 | 38.0 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/gn/mask_rcnn_r101_fpn_gn-all_3x_coco.py) | [model](http://download.openmmlab.com/mmdetection/v2.0/gn/mask_rcnn_r101_fpn_gn-all_3x_coco/mask_rcnn_r101_fpn_gn-all_3x_coco_20200513_181609-0df864f4.pth) | [log](http://download.openmmlab.com/mmdetection/v2.0/gn/mask_rcnn_r101_fpn_gn-all_3x_coco/mask_rcnn_r101_fpn_gn-all_3x_coco_20200513_181609.log.json) | -| R-50-FPN (c) | Mask R-CNN | 2x | 7.1 | 10.9 | 40.0 | 36.1 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/gn/mask_rcnn_r50_fpn_gn-all_contrib_2x_coco.py) | [model](http://download.openmmlab.com/mmdetection/v2.0/gn/mask_rcnn_r50_fpn_gn-all_contrib_2x_coco/mask_rcnn_r50_fpn_gn-all_contrib_2x_coco_20200207-20d3e849.pth) | [log](http://download.openmmlab.com/mmdetection/v2.0/gn/mask_rcnn_r50_fpn_gn-all_contrib_2x_coco/mask_rcnn_r50_fpn_gn-all_contrib_2x_coco_20200207_225832.log.json) | -| R-50-FPN (c) | Mask R-CNN | 3x | 7.1 | - | 40.1 | 36.2 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/gn/mask_rcnn_r50_fpn_gn-all_contrib_3x_coco.py) | [model](http://download.openmmlab.com/mmdetection/v2.0/gn/mask_rcnn_r50_fpn_gn-all_contrib_3x_coco/mask_rcnn_r50_fpn_gn-all_contrib_3x_coco_20200225-542aefbc.pth) | [log](http://download.openmmlab.com/mmdetection/v2.0/gn/mask_rcnn_r50_fpn_gn-all_contrib_3x_coco/mask_rcnn_r50_fpn_gn-all_contrib_3x_coco_20200225_235135.log.json) | +| R-50-FPN (d) | Mask R-CNN | 2x | 7.1 | 11.0 | 40.2 | 36.4 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/gn/mask_rcnn_r50_fpn_gn-all_2x_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/gn/mask_rcnn_r50_fpn_gn-all_2x_coco/mask_rcnn_r50_fpn_gn-all_2x_coco_20200206-8eee02a6.pth) | [log](https://download.openmmlab.com/mmdetection/v2.0/gn/mask_rcnn_r50_fpn_gn-all_2x_coco/mask_rcnn_r50_fpn_gn-all_2x_coco_20200206_050355.log.json) | +| R-50-FPN (d) | Mask R-CNN | 3x | 7.1 | - | 40.5 | 36.7 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/gn/mask_rcnn_r50_fpn_gn-all_3x_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/gn/mask_rcnn_r50_fpn_gn-all_3x_coco/mask_rcnn_r50_fpn_gn-all_3x_coco_20200214-8b23b1e5.pth) | [log](https://download.openmmlab.com/mmdetection/v2.0/gn/mask_rcnn_r50_fpn_gn-all_3x_coco/mask_rcnn_r50_fpn_gn-all_3x_coco_20200214_063512.log.json) | +| R-101-FPN (d) | Mask R-CNN | 2x | 9.9 | 9.0 | 41.9 | 37.6 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/gn/mask_rcnn_r101_fpn_gn-all_2x_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/gn/mask_rcnn_r101_fpn_gn-all_2x_coco/mask_rcnn_r101_fpn_gn-all_2x_coco_20200205-d96b1b50.pth) | [log](https://download.openmmlab.com/mmdetection/v2.0/gn/mask_rcnn_r101_fpn_gn-all_2x_coco/mask_rcnn_r101_fpn_gn-all_2x_coco_20200205_234402.log.json) | +| R-101-FPN (d) | Mask R-CNN | 3x | 9.9 | | 42.1 | 38.0 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/gn/mask_rcnn_r101_fpn_gn-all_3x_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/gn/mask_rcnn_r101_fpn_gn-all_3x_coco/mask_rcnn_r101_fpn_gn-all_3x_coco_20200513_181609-0df864f4.pth) | [log](https://download.openmmlab.com/mmdetection/v2.0/gn/mask_rcnn_r101_fpn_gn-all_3x_coco/mask_rcnn_r101_fpn_gn-all_3x_coco_20200513_181609.log.json) | +| R-50-FPN (c) | Mask R-CNN | 2x | 7.1 | 10.9 | 40.0 | 36.1 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/gn/mask_rcnn_r50_fpn_gn-all_contrib_2x_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/gn/mask_rcnn_r50_fpn_gn-all_contrib_2x_coco/mask_rcnn_r50_fpn_gn-all_contrib_2x_coco_20200207-20d3e849.pth) | [log](https://download.openmmlab.com/mmdetection/v2.0/gn/mask_rcnn_r50_fpn_gn-all_contrib_2x_coco/mask_rcnn_r50_fpn_gn-all_contrib_2x_coco_20200207_225832.log.json) | +| R-50-FPN (c) | Mask R-CNN | 3x | 7.1 | - | 40.1 | 36.2 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/gn/mask_rcnn_r50_fpn_gn-all_contrib_3x_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/gn/mask_rcnn_r50_fpn_gn-all_contrib_3x_coco/mask_rcnn_r50_fpn_gn-all_contrib_3x_coco_20200225-542aefbc.pth) | [log](https://download.openmmlab.com/mmdetection/v2.0/gn/mask_rcnn_r50_fpn_gn-all_contrib_3x_coco/mask_rcnn_r50_fpn_gn-all_contrib_3x_coco_20200225_235135.log.json) | **Notes:** diff --git a/configs/gn/mask_rcnn_r101_fpn_gn-all_2x_coco.py b/configs/gn/mask_rcnn_r101_fpn_gn-all_2x_coco.py index 0fcc558..a505ba0 100644 --- a/configs/gn/mask_rcnn_r101_fpn_gn-all_2x_coco.py +++ b/configs/gn/mask_rcnn_r101_fpn_gn-all_2x_coco.py @@ -1,3 +1,7 @@ _base_ = './mask_rcnn_r50_fpn_gn-all_2x_coco.py' model = dict( - pretrained='open-mmlab://detectron/resnet101_gn', backbone=dict(depth=101)) + backbone=dict( + depth=101, + init_cfg=dict( + type='Pretrained', + checkpoint='open-mmlab://detectron/resnet101_gn'))) diff --git a/configs/gn/mask_rcnn_r50_fpn_gn-all_2x_coco.py b/configs/gn/mask_rcnn_r50_fpn_gn-all_2x_coco.py index 9c85d26..1de7d98 100644 --- a/configs/gn/mask_rcnn_r50_fpn_gn-all_2x_coco.py +++ b/configs/gn/mask_rcnn_r50_fpn_gn-all_2x_coco.py @@ -1,8 +1,11 @@ _base_ = '../mask_rcnn/mask_rcnn_r50_fpn_1x_coco.py' norm_cfg = dict(type='GN', num_groups=32, requires_grad=True) model = dict( - pretrained='open-mmlab://detectron/resnet50_gn', - backbone=dict(norm_cfg=norm_cfg), + backbone=dict( + norm_cfg=norm_cfg, + init_cfg=dict( + type='Pretrained', + checkpoint='open-mmlab://detectron/resnet50_gn')), neck=dict(norm_cfg=norm_cfg), roi_head=dict( bbox_head=dict( diff --git a/configs/gn/mask_rcnn_r50_fpn_gn-all_contrib_2x_coco.py b/configs/gn/mask_rcnn_r50_fpn_gn-all_contrib_2x_coco.py index 89caaaf..2f430fd 100644 --- a/configs/gn/mask_rcnn_r50_fpn_gn-all_contrib_2x_coco.py +++ b/configs/gn/mask_rcnn_r50_fpn_gn-all_contrib_2x_coco.py @@ -1,8 +1,10 @@ _base_ = '../mask_rcnn/mask_rcnn_r50_fpn_1x_coco.py' norm_cfg = dict(type='GN', num_groups=32, requires_grad=True) model = dict( - pretrained='open-mmlab://contrib/resnet50_gn', - backbone=dict(norm_cfg=norm_cfg), + backbone=dict( + norm_cfg=norm_cfg, + init_cfg=dict( + type='Pretrained', checkpoint='open-mmlab://contrib/resnet50_gn')), neck=dict(norm_cfg=norm_cfg), roi_head=dict( bbox_head=dict( diff --git a/configs/gn/metafile.yml b/configs/gn/metafile.yml new file mode 100644 index 0000000..4a1ecae --- /dev/null +++ b/configs/gn/metafile.yml @@ -0,0 +1,162 @@ +Collections: + - Name: Group Normalization + Metadata: + Training Data: COCO + Training Techniques: + - SGD with Momentum + - Weight Decay + Training Resources: 8x V100 GPUs + Architecture: + - Group Normalization + Paper: + URL: https://arxiv.org/abs/1803.08494 + Title: 'Group Normalization' + README: configs/gn/README.md + Code: + URL: https://github.com/open-mmlab/mmdetection/blob/v2.0.0/configs/gn/mask_rcnn_r50_fpn_gn-all_2x_coco.py + Version: v2.0.0 + +Models: + - Name: mask_rcnn_r50_fpn_gn-all_2x_coco + In Collection: Group Normalization + Config: configs/gn/mask_rcnn_r50_fpn_gn-all_2x_coco.py + Metadata: + Training Memory (GB): 7.1 + inference time (ms/im): + - value: 90.91 + hardware: V100 + backend: PyTorch + batch size: 1 + mode: FP32 + resolution: (800, 1333) + Epochs: 24 + Results: + - Task: Object Detection + Dataset: COCO + Metrics: + box AP: 40.2 + - Task: Instance Segmentation + Dataset: COCO + Metrics: + mask AP: 36.4 + Weights: https://download.openmmlab.com/mmdetection/v2.0/gn/mask_rcnn_r50_fpn_gn-all_2x_coco/mask_rcnn_r50_fpn_gn-all_2x_coco_20200206-8eee02a6.pth + + - Name: mask_rcnn_r50_fpn_gn-all_3x_coco + In Collection: Group Normalization + Config: configs/gn/mask_rcnn_r50_fpn_gn-all_3x_coco.py + Metadata: + Training Memory (GB): 7.1 + inference time (ms/im): + - value: 90.91 + hardware: V100 + backend: PyTorch + batch size: 1 + mode: FP32 + resolution: (800, 1333) + Epochs: 36 + Results: + - Task: Object Detection + Dataset: COCO + Metrics: + box AP: 40.5 + - Task: Instance Segmentation + Dataset: COCO + Metrics: + mask AP: 36.7 + Weights: https://download.openmmlab.com/mmdetection/v2.0/gn/mask_rcnn_r50_fpn_gn-all_3x_coco/mask_rcnn_r50_fpn_gn-all_3x_coco_20200214-8b23b1e5.pth + + - Name: mask_rcnn_r101_fpn_gn-all_2x_coco + In Collection: Group Normalization + Config: configs/gn/mask_rcnn_r101_fpn_gn-all_2x_coco.py + Metadata: + Training Memory (GB): 9.9 + inference time (ms/im): + - value: 111.11 + hardware: V100 + backend: PyTorch + batch size: 1 + mode: FP32 + resolution: (800, 1333) + Epochs: 24 + Results: + - Task: Object Detection + Dataset: COCO + Metrics: + box AP: 41.9 + - Task: Instance Segmentation + Dataset: COCO + Metrics: + mask AP: 37.6 + Weights: https://download.openmmlab.com/mmdetection/v2.0/gn/mask_rcnn_r101_fpn_gn-all_2x_coco/mask_rcnn_r101_fpn_gn-all_2x_coco_20200205-d96b1b50.pth + + - Name: mask_rcnn_r101_fpn_gn-all_3x_coco + In Collection: Group Normalization + Config: configs/gn/mask_rcnn_r101_fpn_gn-all_3x_coco.py + Metadata: + Training Memory (GB): 9.9 + inference time (ms/im): + - value: 111.11 + hardware: V100 + backend: PyTorch + batch size: 1 + mode: FP32 + resolution: (800, 1333) + Epochs: 36 + Results: + - Task: Object Detection + Dataset: COCO + Metrics: + box AP: 42.1 + - Task: Instance Segmentation + Dataset: COCO + Metrics: + mask AP: 38.0 + Weights: https://download.openmmlab.com/mmdetection/v2.0/gn/mask_rcnn_r101_fpn_gn-all_3x_coco/mask_rcnn_r101_fpn_gn-all_3x_coco_20200513_181609-0df864f4.pth + + - Name: mask_rcnn_r50_fpn_gn-all_contrib_2x_coco + In Collection: Group Normalization + Config: configs/gn/mask_rcnn_r50_fpn_gn-all_contrib_2x_coco.py + Metadata: + Training Memory (GB): 7.1 + inference time (ms/im): + - value: 91.74 + hardware: V100 + backend: PyTorch + batch size: 1 + mode: FP32 + resolution: (800, 1333) + Epochs: 24 + Results: + - Task: Object Detection + Dataset: COCO + Metrics: + box AP: 40.0 + - Task: Instance Segmentation + Dataset: COCO + Metrics: + mask AP: 36.1 + Weights: https://download.openmmlab.com/mmdetection/v2.0/gn/mask_rcnn_r50_fpn_gn-all_contrib_2x_coco/mask_rcnn_r50_fpn_gn-all_contrib_2x_coco_20200207-20d3e849.pth + + - Name: mask_rcnn_r50_fpn_gn-all_contrib_3x_coco + In Collection: Group Normalization + Config: configs/gn/mask_rcnn_r50_fpn_gn-all_contrib_3x_coco.py + Metadata: + Training Memory (GB): 7.1 + inference time (ms/im): + - value: 91.74 + hardware: V100 + backend: PyTorch + batch size: 1 + mode: FP32 + resolution: (800, 1333) + Epochs: 36 + Results: + - Task: Object Detection + Dataset: COCO + Metrics: + box AP: 40.1 + - Task: Instance Segmentation + Dataset: COCO + Metrics: + mask AP: 36.2 + Weights: https://download.openmmlab.com/mmdetection/v2.0/gn/mask_rcnn_r50_fpn_gn-all_contrib_3x_coco/mask_rcnn_r50_fpn_gn-all_contrib_3x_coco_20200225-542aefbc.pth diff --git a/configs/grid_rcnn/README.md b/configs/grid_rcnn/README.md index a1e8352..30cb6e0 100644 --- a/configs/grid_rcnn/README.md +++ b/configs/grid_rcnn/README.md @@ -2,7 +2,7 @@ ## Introduction -[ALGORITHM] + ```latex @inproceedings{lu2019grid, @@ -24,10 +24,10 @@ | Backbone | Lr schd | Mem (GB) | Inf time (fps) | box AP | Config | Download | |:-----------:|:-------:|:--------:|:--------------:|:------:|:------:|:--------:| -| R-50 | 2x | 5.1 | 15.0 | 40.4 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/grid_rcnn/grid_rcnn_r50_fpn_gn-head_2x_coco.py) | [model](http://download.openmmlab.com/mmdetection/v2.0/grid_rcnn/grid_rcnn_r50_fpn_gn-head_2x_coco/grid_rcnn_r50_fpn_gn-head_2x_coco_20200130-6cca8223.pth) | [log](http://download.openmmlab.com/mmdetection/v2.0/grid_rcnn/grid_rcnn_r50_fpn_gn-head_2x_coco/grid_rcnn_r50_fpn_gn-head_2x_coco_20200130_221140.log.json) | -| R-101 | 2x | 7.0 | 12.6 | 41.5 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/grid_rcnn/grid_rcnn_r101_fpn_gn-head_2x_coco.py) | [model](http://download.openmmlab.com/mmdetection/v2.0/grid_rcnn/grid_rcnn_r101_fpn_gn-head_2x_coco/grid_rcnn_r101_fpn_gn-head_2x_coco_20200309-d6eca030.pth) | [log](http://download.openmmlab.com/mmdetection/v2.0/grid_rcnn/grid_rcnn_r101_fpn_gn-head_2x_coco/grid_rcnn_r101_fpn_gn-head_2x_coco_20200309_164224.log.json) | -| X-101-32x4d | 2x | 8.3 | 10.8 | 42.9 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/grid_rcnn/grid_rcnn_x101_32x4d_fpn_gn-head_2x_coco.py) | [model](http://download.openmmlab.com/mmdetection/v2.0/grid_rcnn/grid_rcnn_x101_32x4d_fpn_gn-head_2x_coco/grid_rcnn_x101_32x4d_fpn_gn-head_2x_coco_20200130-d8f0e3ff.pth) | [log](http://download.openmmlab.com/mmdetection/v2.0/grid_rcnn/grid_rcnn_x101_32x4d_fpn_gn-head_2x_coco/grid_rcnn_x101_32x4d_fpn_gn-head_2x_coco_20200130_215413.log.json) | -| X-101-64x4d | 2x | 11.3 | 7.7 | 43.0 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/grid_rcnn/grid_rcnn_x101_64x4d_fpn_gn-head_2x_coco.py) | [model](http://download.openmmlab.com/mmdetection/v2.0/grid_rcnn/grid_rcnn_x101_64x4d_fpn_gn-head_2x_coco/grid_rcnn_x101_64x4d_fpn_gn-head_2x_coco_20200204-ec76a754.pth) | [log](http://download.openmmlab.com/mmdetection/v2.0/grid_rcnn/grid_rcnn_x101_64x4d_fpn_gn-head_2x_coco/grid_rcnn_x101_64x4d_fpn_gn-head_2x_coco_20200204_080641.log.json) | +| R-50 | 2x | 5.1 | 15.0 | 40.4 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/grid_rcnn/grid_rcnn_r50_fpn_gn-head_2x_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/grid_rcnn/grid_rcnn_r50_fpn_gn-head_2x_coco/grid_rcnn_r50_fpn_gn-head_2x_coco_20200130-6cca8223.pth) | [log](https://download.openmmlab.com/mmdetection/v2.0/grid_rcnn/grid_rcnn_r50_fpn_gn-head_2x_coco/grid_rcnn_r50_fpn_gn-head_2x_coco_20200130_221140.log.json) | +| R-101 | 2x | 7.0 | 12.6 | 41.5 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/grid_rcnn/grid_rcnn_r101_fpn_gn-head_2x_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/grid_rcnn/grid_rcnn_r101_fpn_gn-head_2x_coco/grid_rcnn_r101_fpn_gn-head_2x_coco_20200309-d6eca030.pth) | [log](https://download.openmmlab.com/mmdetection/v2.0/grid_rcnn/grid_rcnn_r101_fpn_gn-head_2x_coco/grid_rcnn_r101_fpn_gn-head_2x_coco_20200309_164224.log.json) | +| X-101-32x4d | 2x | 8.3 | 10.8 | 42.9 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/grid_rcnn/grid_rcnn_x101_32x4d_fpn_gn-head_2x_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/grid_rcnn/grid_rcnn_x101_32x4d_fpn_gn-head_2x_coco/grid_rcnn_x101_32x4d_fpn_gn-head_2x_coco_20200130-d8f0e3ff.pth) | [log](https://download.openmmlab.com/mmdetection/v2.0/grid_rcnn/grid_rcnn_x101_32x4d_fpn_gn-head_2x_coco/grid_rcnn_x101_32x4d_fpn_gn-head_2x_coco_20200130_215413.log.json) | +| X-101-64x4d | 2x | 11.3 | 7.7 | 43.0 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/grid_rcnn/grid_rcnn_x101_64x4d_fpn_gn-head_2x_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/grid_rcnn/grid_rcnn_x101_64x4d_fpn_gn-head_2x_coco/grid_rcnn_x101_64x4d_fpn_gn-head_2x_coco_20200204-ec76a754.pth) | [log](https://download.openmmlab.com/mmdetection/v2.0/grid_rcnn/grid_rcnn_x101_64x4d_fpn_gn-head_2x_coco/grid_rcnn_x101_64x4d_fpn_gn-head_2x_coco_20200204_080641.log.json) | **Notes:** diff --git a/configs/grid_rcnn/grid_rcnn_r101_fpn_gn-head_2x_coco.py b/configs/grid_rcnn/grid_rcnn_r101_fpn_gn-head_2x_coco.py index cf8b648..1bb5889 100644 --- a/configs/grid_rcnn/grid_rcnn_r101_fpn_gn-head_2x_coco.py +++ b/configs/grid_rcnn/grid_rcnn_r101_fpn_gn-head_2x_coco.py @@ -1,3 +1,7 @@ _base_ = './grid_rcnn_r50_fpn_gn-head_2x_coco.py' -model = dict(pretrained='torchvision://resnet101', backbone=dict(depth=101)) +model = dict( + backbone=dict( + depth=101, + init_cfg=dict(type='Pretrained', + checkpoint='torchvision://resnet101'))) diff --git a/configs/grid_rcnn/grid_rcnn_r50_fpn_gn-head_2x_coco.py b/configs/grid_rcnn/grid_rcnn_r50_fpn_gn-head_2x_coco.py index 6ed5bcb..df63cd5 100644 --- a/configs/grid_rcnn/grid_rcnn_r50_fpn_gn-head_2x_coco.py +++ b/configs/grid_rcnn/grid_rcnn_r50_fpn_gn-head_2x_coco.py @@ -4,7 +4,6 @@ # model settings model = dict( type='GridRCNN', - pretrained='torchvision://resnet50', backbone=dict( type='ResNet', depth=50, @@ -13,7 +12,8 @@ frozen_stages=1, norm_cfg=dict(type='BN', requires_grad=True), norm_eval=True, - style='pytorch'), + style='pytorch', + init_cfg=dict(type='Pretrained', checkpoint='torchvision://resnet50')), neck=dict( type='FPN', in_channels=[256, 512, 1024, 2048], diff --git a/configs/grid_rcnn/grid_rcnn_x101_32x4d_fpn_gn-head_2x_coco.py b/configs/grid_rcnn/grid_rcnn_x101_32x4d_fpn_gn-head_2x_coco.py index 14c1eb2..3bc8516 100644 --- a/configs/grid_rcnn/grid_rcnn_x101_32x4d_fpn_gn-head_2x_coco.py +++ b/configs/grid_rcnn/grid_rcnn_x101_32x4d_fpn_gn-head_2x_coco.py @@ -1,6 +1,5 @@ _base_ = './grid_rcnn_r50_fpn_gn-head_2x_coco.py' model = dict( - pretrained='open-mmlab://resnext101_32x4d', backbone=dict( type='ResNeXt', depth=101, @@ -9,7 +8,9 @@ num_stages=4, out_indices=(0, 1, 2, 3), frozen_stages=1, - style='pytorch')) + style='pytorch', + init_cfg=dict( + type='Pretrained', checkpoint='open-mmlab://resnext101_32x4d'))) # optimizer optimizer = dict(type='SGD', lr=0.02, momentum=0.9, weight_decay=0.0001) optimizer_config = dict(grad_clip=None) diff --git a/configs/grid_rcnn/grid_rcnn_x101_64x4d_fpn_gn-head_2x_coco.py b/configs/grid_rcnn/grid_rcnn_x101_64x4d_fpn_gn-head_2x_coco.py index 2fdc53c..c78f8f6 100644 --- a/configs/grid_rcnn/grid_rcnn_x101_64x4d_fpn_gn-head_2x_coco.py +++ b/configs/grid_rcnn/grid_rcnn_x101_64x4d_fpn_gn-head_2x_coco.py @@ -1,6 +1,5 @@ _base_ = './grid_rcnn_x101_32x4d_fpn_gn-head_2x_coco.py' model = dict( - pretrained='open-mmlab://resnext101_64x4d', backbone=dict( type='ResNeXt', depth=101, @@ -9,4 +8,6 @@ num_stages=4, out_indices=(0, 1, 2, 3), frozen_stages=1, - style='pytorch')) + style='pytorch', + init_cfg=dict( + type='Pretrained', checkpoint='open-mmlab://resnext101_64x4d'))) diff --git a/configs/grid_rcnn/metafile.yml b/configs/grid_rcnn/metafile.yml new file mode 100644 index 0000000..d1aa851 --- /dev/null +++ b/configs/grid_rcnn/metafile.yml @@ -0,0 +1,101 @@ +Collections: + - Name: Grid R-CNN + Metadata: + Training Data: COCO + Training Techniques: + - SGD with Momentum + - Weight Decay + Training Resources: 8x V100 GPUs + Architecture: + - RPN + - Dilated Convolution + - ResNet + - RoIAlign + Paper: + URL: https://arxiv.org/abs/1906.05688 + Title: 'Grid R-CNN' + README: configs/grid_rcnn/README.md + Code: + URL: https://github.com/open-mmlab/mmdetection/blob/v2.0.0/mmdet/models/detectors/grid_rcnn.py#L6 + Version: v2.0.0 + +Models: + - Name: grid_rcnn_r50_fpn_gn-head_2x_coco + In Collection: Grid R-CNN + Config: configs/grid_rcnn/grid_rcnn_r50_fpn_gn-head_2x_coco.py + Metadata: + Training Memory (GB): 5.1 + inference time (ms/im): + - value: 66.67 + hardware: V100 + backend: PyTorch + batch size: 1 + mode: FP32 + resolution: (800, 1333) + Epochs: 24 + Results: + - Task: Object Detection + Dataset: COCO + Metrics: + box AP: 40.4 + Weights: https://download.openmmlab.com/mmdetection/v2.0/grid_rcnn/grid_rcnn_r50_fpn_gn-head_2x_coco/grid_rcnn_r50_fpn_gn-head_2x_coco_20200130-6cca8223.pth + + - Name: grid_rcnn_r101_fpn_gn-head_2x_coco + In Collection: Grid R-CNN + Config: configs/grid_rcnn/grid_rcnn_r101_fpn_gn-head_2x_coco.py + Metadata: + Training Memory (GB): 7.0 + inference time (ms/im): + - value: 79.37 + hardware: V100 + backend: PyTorch + batch size: 1 + mode: FP32 + resolution: (800, 1333) + Epochs: 24 + Results: + - Task: Object Detection + Dataset: COCO + Metrics: + box AP: 41.5 + Weights: https://download.openmmlab.com/mmdetection/v2.0/grid_rcnn/grid_rcnn_r101_fpn_gn-head_2x_coco/grid_rcnn_r101_fpn_gn-head_2x_coco_20200309-d6eca030.pth + + - Name: grid_rcnn_x101_32x4d_fpn_gn-head_2x_coco + In Collection: Grid R-CNN + Config: configs/grid_rcnn/grid_rcnn_x101_32x4d_fpn_gn-head_2x_coco.py + Metadata: + Training Memory (GB): 8.3 + inference time (ms/im): + - value: 92.59 + hardware: V100 + backend: PyTorch + batch size: 1 + mode: FP32 + resolution: (800, 1333) + Epochs: 24 + Results: + - Task: Object Detection + Dataset: COCO + Metrics: + box AP: 42.9 + Weights: https://download.openmmlab.com/mmdetection/v2.0/grid_rcnn/grid_rcnn_x101_32x4d_fpn_gn-head_2x_coco/grid_rcnn_x101_32x4d_fpn_gn-head_2x_coco_20200130-d8f0e3ff.pth + + - Name: grid_rcnn_x101_64x4d_fpn_gn-head_2x_coco + In Collection: Grid R-CNN + Config: configs/grid_rcnn/grid_rcnn_x101_64x4d_fpn_gn-head_2x_coco.py + Metadata: + Training Memory (GB): 11.3 + inference time (ms/im): + - value: 129.87 + hardware: V100 + backend: PyTorch + batch size: 1 + mode: FP32 + resolution: (800, 1333) + Epochs: 24 + Results: + - Task: Object Detection + Dataset: COCO + Metrics: + box AP: 43.0 + Weights: https://download.openmmlab.com/mmdetection/v2.0/grid_rcnn/grid_rcnn_x101_64x4d_fpn_gn-head_2x_coco/grid_rcnn_x101_64x4d_fpn_gn-head_2x_coco_20200204-ec76a754.pth diff --git a/configs/groie/README.md b/configs/groie/README.md index 490349d..c38b70b 100644 --- a/configs/groie/README.md +++ b/configs/groie/README.md @@ -11,7 +11,7 @@ on COCO object detection. ## Introduction -[ALGORITHM] + This paper is motivated by the need to overcome to the limitations of existing RoI extractors which select only one (the best) layer from FPN. @@ -33,16 +33,16 @@ the trained models. | Backbone | Method | Lr schd | box AP | mask AP | Config | Download| | :-------: | :--------------: | :-----: | :----: | :-----: | :-------:| :--------:| -| R-50-FPN | Faster Original | 1x | 37.4 | | [config](../faster_rcnn/faster_rcnn_r50_fpn_1x_coco.py) | [model](http://download.openmmlab.com/mmdetection/v2.0/faster_rcnn/faster_rcnn_r50_fpn_1x_coco/faster_rcnn_r50_fpn_1x_coco_20200130-047c8118.pth) | [log](http://download.openmmlab.com/mmdetection/v2.0/faster_rcnn/faster_rcnn_r50_fpn_1x_coco/faster_rcnn_r50_fpn_1x_coco_20200130_204655.log.json) | -| R-50-FPN | + GRoIE | 1x | 38.3 | | [config](./faster_rcnn_r50_fpn_groie_1x_coco.py) | [model](http://download.openmmlab.com/mmdetection/v2.0/groie/faster_rcnn_r50_fpn_groie_1x_coco/faster_rcnn_r50_fpn_groie_1x_coco_20200604_211715-66ee9516.pth) | [log](http://download.openmmlab.com/mmdetection/v2.0/groie/faster_rcnn_r50_fpn_groie_1x_coco/faster_rcnn_r50_fpn_groie_1x_coco_20200604_211715.log.json) | -| R-50-FPN | Grid R-CNN | 1x | 39.1 | | [config](./grid_rcnn_r50_fpn_gn-head_1x_coco.py)| [model](http://download.openmmlab.com/mmdetection/v2.0/groie/grid_rcnn_r50_fpn_gn-head_1x_coco/grid_rcnn_r50_fpn_gn-head_1x_coco_20200605_202059-64f00ee8.pth) | [log](http://download.openmmlab.com/mmdetection/v2.0/groie/grid_rcnn_r50_fpn_gn-head_1x_coco/grid_rcnn_r50_fpn_gn-head_1x_coco_20200605_202059.log.json) | +| R-50-FPN | Faster Original | 1x | 37.4 | | [config](../faster_rcnn/faster_rcnn_r50_fpn_1x_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/faster_rcnn/faster_rcnn_r50_fpn_1x_coco/faster_rcnn_r50_fpn_1x_coco_20200130-047c8118.pth) | [log](https://download.openmmlab.com/mmdetection/v2.0/faster_rcnn/faster_rcnn_r50_fpn_1x_coco/faster_rcnn_r50_fpn_1x_coco_20200130_204655.log.json) | +| R-50-FPN | + GRoIE | 1x | 38.3 | | [config](./faster_rcnn_r50_fpn_groie_1x_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/groie/faster_rcnn_r50_fpn_groie_1x_coco/faster_rcnn_r50_fpn_groie_1x_coco_20200604_211715-66ee9516.pth) | [log](https://download.openmmlab.com/mmdetection/v2.0/groie/faster_rcnn_r50_fpn_groie_1x_coco/faster_rcnn_r50_fpn_groie_1x_coco_20200604_211715.log.json) | +| R-50-FPN | Grid R-CNN | 1x | 39.1 | | [config](./grid_rcnn_r50_fpn_gn-head_1x_coco.py)| [model](https://download.openmmlab.com/mmdetection/v2.0/groie/grid_rcnn_r50_fpn_gn-head_1x_coco/grid_rcnn_r50_fpn_gn-head_1x_coco_20200605_202059-64f00ee8.pth) | [log](https://download.openmmlab.com/mmdetection/v2.0/groie/grid_rcnn_r50_fpn_gn-head_1x_coco/grid_rcnn_r50_fpn_gn-head_1x_coco_20200605_202059.log.json) | | R-50-FPN | + GRoIE | 1x | | | [config](./grid_rcnn_r50_fpn_gn-head_groie_1x_coco.py)|| -| R-50-FPN | Mask R-CNN | 1x | 38.2 | 34.7 | [config](../mask_rcnn/mask_rcnn_r50_fpn_1x_coco.py)| [model](http://download.openmmlab.com/mmdetection/v2.0/mask_rcnn/mask_rcnn_r50_fpn_1x_coco/mask_rcnn_r50_fpn_1x_coco_20200205-d4b0c5d6.pth) | [log](http://download.openmmlab.com/mmdetection/v2.0/mask_rcnn/mask_rcnn_r50_fpn_1x_coco/mask_rcnn_r50_fpn_1x_coco_20200205_050542.log.json) | -| R-50-FPN | + GRoIE | 1x | 39.0 | 36.0 | [config](./mask_rcnn_r50_fpn_groie_1x_coco.py) | [model](http://download.openmmlab.com/mmdetection/v2.0/groie/mask_rcnn_r50_fpn_groie_1x_coco/mask_rcnn_r50_fpn_groie_1x_coco_20200604_211715-50d90c74.pth) | [log](http://download.openmmlab.com/mmdetection/v2.0/groie/mask_rcnn_r50_fpn_groie_1x_coco/mask_rcnn_r50_fpn_groie_1x_coco_20200604_211715.log.json) | -| R-50-FPN | GC-Net | 1x | 40.7 | 36.5 | [config](../gcnet/mask_rcnn_r50_fpn_syncbn-backbone_r4_gcb_c3-c5_1x_coco.py) | [model](http://download.openmmlab.com/mmdetection/v2.0/gcnet/mask_rcnn_r50_fpn_syncbn-backbone_r4_gcb_c3-c5_1x_coco/mask_rcnn_r50_fpn_syncbn-backbone_r4_gcb_c3-c5_1x_coco_20200202-50b90e5c.pth) | [log](http://download.openmmlab.com/mmdetection/v2.0/gcnet/mask_rcnn_r50_fpn_syncbn-backbone_r4_gcb_c3-c5_1x_coco/mask_rcnn_r50_fpn_syncbn-backbone_r4_gcb_c3-c5_1x_coco_20200202_085547.log.json) | -| R-50-FPN | + GRoIE | 1x | 41.0 | 37.8 | [config](./mask_rcnn_r50_fpn_syncbn-backbone_r4_gcb_c3-c5_groie_1x_coco.py) |[model](http://download.openmmlab.com/mmdetection/v2.0/groie/mask_rcnn_r50_fpn_syncbn-backbone_r4_gcb_c3-c5_groie_1x_coco/mask_rcnn_r50_fpn_syncbn-backbone_r4_gcb_c3-c5_groie_1x_coco_20200604_211715-42eb79e1.pth) | [log](http://download.openmmlab.com/mmdetection/v2.0/groie/mask_rcnn_r50_fpn_syncbn-backbone_r4_gcb_c3-c5_groie_1x_coco/mask_rcnn_r50_fpn_syncbn-backbone_r4_gcb_c3-c5_groie_1x_coco_20200604_211715-42eb79e1.pth) | -| R-101-FPN | GC-Net | 1x | 42.2 | 37.8 | [config](../gcnet/mask_rcnn_r101_fpn_syncbn-backbone_r4_gcb_c3-c5_1x_coco.py) | [model](http://download.openmmlab.com/mmdetection/v2.0/gcnet/mask_rcnn_r101_fpn_syncbn-backbone_r4_gcb_c3-c5_1x_coco/mask_rcnn_r101_fpn_syncbn-backbone_r4_gcb_c3-c5_1x_coco_20200206-8407a3f0.pth) | [log](http://download.openmmlab.com/mmdetection/v2.0/gcnet/mask_rcnn_r101_fpn_syncbn-backbone_r4_gcb_c3-c5_1x_coco/mask_rcnn_r101_fpn_syncbn-backbone_r4_gcb_c3-c5_1x_coco_20200206_142508.log.json) | -| R-101-FPN | + GRoIE | 1x | | | [config](./mask_rcnn_r101_fpn_syncbn-backbone_r4_gcb_c3-c5_groie_1x_coco.py)| [model](http://download.openmmlab.com/mmdetection/v2.0/groie/mask_rcnn_r101_fpn_syncbn-backbone_r4_gcb_c3-c5_groie_1x_coco/mask_rcnn_r101_fpn_syncbn-backbone_r4_gcb_c3-c5_groie_1x_coco_20200607_224507-8daae01c.pth) | [log](http://download.openmmlab.com/mmdetection/v2.0/groie/mask_rcnn_r101_fpn_syncbn-backbone_r4_gcb_c3-c5_groie_1x_coco/mask_rcnn_r101_fpn_syncbn-backbone_r4_gcb_c3-c5_groie_1x_coco_20200607_224507.log.json) | +| R-50-FPN | Mask R-CNN | 1x | 38.2 | 34.7 | [config](../mask_rcnn/mask_rcnn_r50_fpn_1x_coco.py)| [model](https://download.openmmlab.com/mmdetection/v2.0/mask_rcnn/mask_rcnn_r50_fpn_1x_coco/mask_rcnn_r50_fpn_1x_coco_20200205-d4b0c5d6.pth) | [log](https://download.openmmlab.com/mmdetection/v2.0/mask_rcnn/mask_rcnn_r50_fpn_1x_coco/mask_rcnn_r50_fpn_1x_coco_20200205_050542.log.json) | +| R-50-FPN | + GRoIE | 1x | 39.0 | 36.0 | [config](./mask_rcnn_r50_fpn_groie_1x_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/groie/mask_rcnn_r50_fpn_groie_1x_coco/mask_rcnn_r50_fpn_groie_1x_coco_20200604_211715-50d90c74.pth) | [log](https://download.openmmlab.com/mmdetection/v2.0/groie/mask_rcnn_r50_fpn_groie_1x_coco/mask_rcnn_r50_fpn_groie_1x_coco_20200604_211715.log.json) | +| R-50-FPN | GC-Net | 1x | 40.7 | 36.5 | [config](../gcnet/mask_rcnn_r50_fpn_syncbn-backbone_r4_gcb_c3-c5_1x_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/gcnet/mask_rcnn_r50_fpn_syncbn-backbone_r4_gcb_c3-c5_1x_coco/mask_rcnn_r50_fpn_syncbn-backbone_r4_gcb_c3-c5_1x_coco_20200202-50b90e5c.pth) | [log](https://download.openmmlab.com/mmdetection/v2.0/gcnet/mask_rcnn_r50_fpn_syncbn-backbone_r4_gcb_c3-c5_1x_coco/mask_rcnn_r50_fpn_syncbn-backbone_r4_gcb_c3-c5_1x_coco_20200202_085547.log.json) | +| R-50-FPN | + GRoIE | 1x | 41.0 | 37.8 | [config](./mask_rcnn_r50_fpn_syncbn-backbone_r4_gcb_c3-c5_groie_1x_coco.py) |[model](https://download.openmmlab.com/mmdetection/v2.0/groie/mask_rcnn_r50_fpn_syncbn-backbone_r4_gcb_c3-c5_groie_1x_coco/mask_rcnn_r50_fpn_syncbn-backbone_r4_gcb_c3-c5_groie_1x_coco_20200604_211715-42eb79e1.pth) | [log](https://download.openmmlab.com/mmdetection/v2.0/groie/mask_rcnn_r50_fpn_syncbn-backbone_r4_gcb_c3-c5_groie_1x_coco/mask_rcnn_r50_fpn_syncbn-backbone_r4_gcb_c3-c5_groie_1x_coco_20200604_211715-42eb79e1.pth) | +| R-101-FPN | GC-Net | 1x | 42.2 | 37.8 | [config](../gcnet/mask_rcnn_r101_fpn_syncbn-backbone_r4_gcb_c3-c5_1x_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/gcnet/mask_rcnn_r101_fpn_syncbn-backbone_r4_gcb_c3-c5_1x_coco/mask_rcnn_r101_fpn_syncbn-backbone_r4_gcb_c3-c5_1x_coco_20200206-8407a3f0.pth) | [log](https://download.openmmlab.com/mmdetection/v2.0/gcnet/mask_rcnn_r101_fpn_syncbn-backbone_r4_gcb_c3-c5_1x_coco/mask_rcnn_r101_fpn_syncbn-backbone_r4_gcb_c3-c5_1x_coco_20200206_142508.log.json) | +| R-101-FPN | + GRoIE | 1x | | | [config](./mask_rcnn_r101_fpn_syncbn-backbone_r4_gcb_c3-c5_groie_1x_coco.py)| [model](https://download.openmmlab.com/mmdetection/v2.0/groie/mask_rcnn_r101_fpn_syncbn-backbone_r4_gcb_c3-c5_groie_1x_coco/mask_rcnn_r101_fpn_syncbn-backbone_r4_gcb_c3-c5_groie_1x_coco_20200607_224507-8daae01c.pth) | [log](https://download.openmmlab.com/mmdetection/v2.0/groie/mask_rcnn_r101_fpn_syncbn-backbone_r4_gcb_c3-c5_groie_1x_coco/mask_rcnn_r101_fpn_syncbn-backbone_r4_gcb_c3-c5_groie_1x_coco_20200607_224507.log.json) | ## Citation diff --git a/configs/groie/metafile.yml b/configs/groie/metafile.yml new file mode 100644 index 0000000..269cb39 --- /dev/null +++ b/configs/groie/metafile.yml @@ -0,0 +1,93 @@ +Collections: + - Name: GRoIE + Metadata: + Training Data: COCO + Training Techniques: + - SGD with Momentum + - Weight Decay + Training Resources: 8x V100 GPUs + Architecture: + - Generic RoI Extractor + - FPN + - RPN + - ResNet + - RoIAlign + Paper: + URL: https://arxiv.org/abs/2004.13665 + Title: 'A novel Region of Interest Extraction Layer for Instance Segmentation' + README: configs/groie/README.md + Code: + URL: https://github.com/open-mmlab/mmdetection/blob/v2.1.0/mmdet/models/roi_heads/roi_extractors/groie.py#L15 + Version: v2.1.0 + +Models: + - Name: faster_rcnn_r50_fpn_groie_1x_coco + In Collection: GRoIE + Config: configs/groie/faster_rcnn_r50_fpn_groie_1x_coco.py + Metadata: + Epochs: 12 + Results: + - Task: Object Detection + Dataset: COCO + Metrics: + box AP: 38.3 + Weights: https://download.openmmlab.com/mmdetection/v2.0/groie/faster_rcnn_r50_fpn_groie_1x_coco/faster_rcnn_r50_fpn_groie_1x_coco_20200604_211715-66ee9516.pth + + - Name: grid_rcnn_r50_fpn_gn-head_groie_1x_coco + In Collection: GRoIE + Config: configs/groie/grid_rcnn_r50_fpn_gn-head_groie_1x_coco.py + Metadata: + Epochs: 12 + Results: + - Task: Object Detection + Dataset: COCO + Metrics: + box AP: 39.1 + + - Name: mask_rcnn_r50_fpn_groie_1x_coco + In Collection: GRoIE + Config: configs/groie/mask_rcnn_r50_fpn_groie_1x_coco.py + Metadata: + Epochs: 12 + Results: + - Task: Object Detection + Dataset: COCO + Metrics: + box AP: 39.0 + - Task: Instance Segmentation + Dataset: COCO + Metrics: + mask AP: 36.0 + Weights: https://download.openmmlab.com/mmdetection/v2.0/groie/mask_rcnn_r50_fpn_groie_1x_coco/mask_rcnn_r50_fpn_groie_1x_coco_20200604_211715-50d90c74.pth + + - Name: mask_rcnn_r50_fpn_syncbn-backbone_r4_gcb_c3-c5_groie_1x_coco + In Collection: GRoIE + Config: configs/groie/mask_rcnn_r50_fpn_syncbn-backbone_r4_gcb_c3-c5_groie_1x_coco.py + Metadata: + Epochs: 12 + Results: + - Task: Object Detection + Dataset: COCO + Metrics: + box AP: 41.0 + - Task: Instance Segmentation + Dataset: COCO + Metrics: + mask AP: 37.8 + Weights: https://download.openmmlab.com/mmdetection/v2.0/groie/mask_rcnn_r50_fpn_syncbn-backbone_r4_gcb_c3-c5_groie_1x_coco/mask_rcnn_r50_fpn_syncbn-backbone_r4_gcb_c3-c5_groie_1x_coco_20200604_211715-42eb79e1.pth + + - Name: mask_rcnn_r101_fpn_syncbn-backbone_r4_gcb_c3-c5_groie_1x_coco + In Collection: GRoIE + Config: configs/groie/mask_rcnn_r101_fpn_syncbn-backbone_r4_gcb_c3-c5_groie_1x_coco.py + Metadata: + Epochs: 12 + Results: + - Task: Object Detection + Dataset: COCO + Metrics: + box AP: 42.6 + - Task: Instance Segmentation + Dataset: COCO + Metrics: + mask AP: 38.7 + Weights: https://download.openmmlab.com/mmdetection/v2.0/groie/mask_rcnn_r101_fpn_syncbn-backbone_r4_gcb_c3-c5_groie_1x_coco/mask_rcnn_r101_fpn_syncbn-backbone_r4_gcb_c3-c5_groie_1x_coco_20200607_224507-8daae01c.pth diff --git a/configs/guided_anchoring/README.md b/configs/guided_anchoring/README.md index 4c3c86c..34d6b0d 100644 --- a/configs/guided_anchoring/README.md +++ b/configs/guided_anchoring/README.md @@ -2,7 +2,7 @@ ## Introduction -[ALGORITHM] + We provide config files to reproduce the results in the CVPR 2019 paper for [Region Proposal by Guided Anchoring](https://arxiv.org/abs/1901.03278). @@ -21,21 +21,21 @@ The results on COCO 2017 val is shown in the below table. (results on test-dev a | Method | Backbone | Style | Lr schd | Mem (GB) | Inf time (fps) | AR 1000 | Config | Download | | :----: | :-------------: | :-----: | :-----: | :------: | :------------: | :-----: | :------: | :--------: | -| GA-RPN | R-50-FPN | caffe | 1x | 5.3 | 15.8 | 68.4 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/guided_anchoring/ga_rpn_r50_caffe_fpn_1x_coco.py) | [model](http://download.openmmlab.com/mmdetection/v2.0/guided_anchoring/ga_rpn_r50_caffe_fpn_1x_coco/ga_rpn_r50_caffe_fpn_1x_coco_20200531-899008a6.pth) | [log](http://download.openmmlab.com/mmdetection/v2.0/guided_anchoring/ga_rpn_r50_caffe_fpn_1x_coco/ga_rpn_r50_caffe_fpn_1x_coco_20200531_011819.log.json) | -| GA-RPN | R-101-FPN | caffe | 1x | 7.3 | 13.0 | 69.5 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/guided_anchoring/ga_rpn_r101_caffe_fpn_1x_coco.py) | [model](http://download.openmmlab.com/mmdetection/v2.0/guided_anchoring/ga_rpn_r101_caffe_fpn_1x_coco/ga_rpn_r101_caffe_fpn_1x_coco_20200531-ca9ba8fb.pth) | [log](http://download.openmmlab.com/mmdetection/v2.0/guided_anchoring/ga_rpn_r101_caffe_fpn_1x_coco/ga_rpn_r101_caffe_fpn_1x_coco_20200531_011812.log.json) | -| GA-RPN | X-101-32x4d-FPN | pytorch | 1x | 8.5 | 10.0 | 70.6 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/guided_anchoring/ga_rpn_x101_32x4d_fpn_1x_coco.py) | [model](http://download.openmmlab.com/mmdetection/v2.0/guided_anchoring/ga_rpn_x101_32x4d_fpn_1x_coco/ga_rpn_x101_32x4d_fpn_1x_coco_20200220-c28d1b18.pth) | [log](http://download.openmmlab.com/mmdetection/v2.0/guided_anchoring/ga_rpn_x101_32x4d_fpn_1x_coco/ga_rpn_x101_32x4d_fpn_1x_coco_20200220_221326.log.json) | -| GA-RPN | X-101-64x4d-FPN | pytorch | 1x | 7.1 | 7.5 | 71.2 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/guided_anchoring/ga_rpn_x101_64x4d_fpn_1x_coco.py) | [model](http://download.openmmlab.com/mmdetection/v2.0/guided_anchoring/ga_rpn_x101_64x4d_fpn_1x_coco/ga_rpn_x101_64x4d_fpn_1x_coco_20200225-3c6e1aa2.pth) | [log](http://download.openmmlab.com/mmdetection/v2.0/guided_anchoring/ga_rpn_x101_64x4d_fpn_1x_coco/ga_rpn_x101_64x4d_fpn_1x_coco_20200225_152704.log.json) | +| GA-RPN | R-50-FPN | caffe | 1x | 5.3 | 15.8 | 68.4 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/guided_anchoring/ga_rpn_r50_caffe_fpn_1x_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/guided_anchoring/ga_rpn_r50_caffe_fpn_1x_coco/ga_rpn_r50_caffe_fpn_1x_coco_20200531-899008a6.pth) | [log](https://download.openmmlab.com/mmdetection/v2.0/guided_anchoring/ga_rpn_r50_caffe_fpn_1x_coco/ga_rpn_r50_caffe_fpn_1x_coco_20200531_011819.log.json) | +| GA-RPN | R-101-FPN | caffe | 1x | 7.3 | 13.0 | 69.5 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/guided_anchoring/ga_rpn_r101_caffe_fpn_1x_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/guided_anchoring/ga_rpn_r101_caffe_fpn_1x_coco/ga_rpn_r101_caffe_fpn_1x_coco_20200531-ca9ba8fb.pth) | [log](https://download.openmmlab.com/mmdetection/v2.0/guided_anchoring/ga_rpn_r101_caffe_fpn_1x_coco/ga_rpn_r101_caffe_fpn_1x_coco_20200531_011812.log.json) | +| GA-RPN | X-101-32x4d-FPN | pytorch | 1x | 8.5 | 10.0 | 70.6 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/guided_anchoring/ga_rpn_x101_32x4d_fpn_1x_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/guided_anchoring/ga_rpn_x101_32x4d_fpn_1x_coco/ga_rpn_x101_32x4d_fpn_1x_coco_20200220-c28d1b18.pth) | [log](https://download.openmmlab.com/mmdetection/v2.0/guided_anchoring/ga_rpn_x101_32x4d_fpn_1x_coco/ga_rpn_x101_32x4d_fpn_1x_coco_20200220_221326.log.json) | +| GA-RPN | X-101-64x4d-FPN | pytorch | 1x | 7.1 | 7.5 | 71.2 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/guided_anchoring/ga_rpn_x101_64x4d_fpn_1x_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/guided_anchoring/ga_rpn_x101_64x4d_fpn_1x_coco/ga_rpn_x101_64x4d_fpn_1x_coco_20200225-3c6e1aa2.pth) | [log](https://download.openmmlab.com/mmdetection/v2.0/guided_anchoring/ga_rpn_x101_64x4d_fpn_1x_coco/ga_rpn_x101_64x4d_fpn_1x_coco_20200225_152704.log.json) | | Method | Backbone | Style | Lr schd | Mem (GB) | Inf time (fps) | box AP | Config | Download | | :------------: | :-------------: | :-----: | :-----: | :------: | :------------: | :----: | :------: | :--------: | -| GA-Faster RCNN | R-50-FPN | caffe | 1x | 5.5 | | 39.6 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/guided_anchoring/ga_faster_r50_caffe_fpn_1x_coco.py) | [model](http://download.openmmlab.com/mmdetection/v2.0/guided_anchoring/ga_faster_r50_caffe_fpn_1x_coco/ga_faster_r50_caffe_fpn_1x_coco_20200702_000718-a11ccfe6.pth) | [log](http://download.openmmlab.com/mmdetection/v2.0/guided_anchoring/ga_faster_r50_caffe_fpn_1x_coco/ga_faster_r50_caffe_fpn_1x_coco_20200702_000718.log.json) | -| GA-Faster RCNN | R-101-FPN | caffe | 1x | 7.5 | | 41.5 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/guided_anchoring/ga_faster_r101_caffe_fpn_1x_coco.py) | [model](http://download.openmmlab.com/mmdetection/v2.0/guided_anchoring/ga_faster_r101_caffe_fpn_1x_coco/ga_faster_r101_caffe_fpn_1x_coco_bbox_mAP-0.415_20200505_115528-fb82e499.pth) | [log](http://download.openmmlab.com/mmdetection/v2.0/guided_anchoring/ga_faster_r101_caffe_fpn_1x_coco/ga_faster_r101_caffe_fpn_1x_coco_20200505_115528.log.json) | -| GA-Faster RCNN | X-101-32x4d-FPN | pytorch | 1x | 8.7 | 9.7 | 43.0 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/guided_anchoring/ga_faster_x101_32x4d_fpn_1x_coco.py) | [model](http://download.openmmlab.com/mmdetection/v2.0/guided_anchoring/ga_faster_x101_32x4d_fpn_1x_coco/ga_faster_x101_32x4d_fpn_1x_coco_20200215-1ded9da3.pth) | [log](http://download.openmmlab.com/mmdetection/v2.0/guided_anchoring/ga_faster_x101_32x4d_fpn_1x_coco/ga_faster_x101_32x4d_fpn_1x_coco_20200215_184547.log.json) | -| GA-Faster RCNN | X-101-64x4d-FPN | pytorch | 1x | 11.8 | 7.3 | 43.9 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/guided_anchoring/ga_faster_x101_64x4d_fpn_1x_coco.py) | [model](http://download.openmmlab.com/mmdetection/v2.0/guided_anchoring/ga_faster_x101_64x4d_fpn_1x_coco/ga_faster_x101_64x4d_fpn_1x_coco_20200215-0fa7bde7.pth) | [log](http://download.openmmlab.com/mmdetection/v2.0/guided_anchoring/ga_faster_x101_64x4d_fpn_1x_coco/ga_faster_x101_64x4d_fpn_1x_coco_20200215_104455.log.json) | +| GA-Faster RCNN | R-50-FPN | caffe | 1x | 5.5 | | 39.6 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/guided_anchoring/ga_faster_r50_caffe_fpn_1x_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/guided_anchoring/ga_faster_r50_caffe_fpn_1x_coco/ga_faster_r50_caffe_fpn_1x_coco_20200702_000718-a11ccfe6.pth) | [log](https://download.openmmlab.com/mmdetection/v2.0/guided_anchoring/ga_faster_r50_caffe_fpn_1x_coco/ga_faster_r50_caffe_fpn_1x_coco_20200702_000718.log.json) | +| GA-Faster RCNN | R-101-FPN | caffe | 1x | 7.5 | | 41.5 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/guided_anchoring/ga_faster_r101_caffe_fpn_1x_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/guided_anchoring/ga_faster_r101_caffe_fpn_1x_coco/ga_faster_r101_caffe_fpn_1x_coco_bbox_mAP-0.415_20200505_115528-fb82e499.pth) | [log](https://download.openmmlab.com/mmdetection/v2.0/guided_anchoring/ga_faster_r101_caffe_fpn_1x_coco/ga_faster_r101_caffe_fpn_1x_coco_20200505_115528.log.json) | +| GA-Faster RCNN | X-101-32x4d-FPN | pytorch | 1x | 8.7 | 9.7 | 43.0 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/guided_anchoring/ga_faster_x101_32x4d_fpn_1x_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/guided_anchoring/ga_faster_x101_32x4d_fpn_1x_coco/ga_faster_x101_32x4d_fpn_1x_coco_20200215-1ded9da3.pth) | [log](https://download.openmmlab.com/mmdetection/v2.0/guided_anchoring/ga_faster_x101_32x4d_fpn_1x_coco/ga_faster_x101_32x4d_fpn_1x_coco_20200215_184547.log.json) | +| GA-Faster RCNN | X-101-64x4d-FPN | pytorch | 1x | 11.8 | 7.3 | 43.9 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/guided_anchoring/ga_faster_x101_64x4d_fpn_1x_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/guided_anchoring/ga_faster_x101_64x4d_fpn_1x_coco/ga_faster_x101_64x4d_fpn_1x_coco_20200215-0fa7bde7.pth) | [log](https://download.openmmlab.com/mmdetection/v2.0/guided_anchoring/ga_faster_x101_64x4d_fpn_1x_coco/ga_faster_x101_64x4d_fpn_1x_coco_20200215_104455.log.json) | | GA-RetinaNet | R-50-FPN | caffe | 1x | 3.5 | 16.8 | 36.9 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/guided_anchoring/ga_retinanet_r50_caffe_fpn_1x_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/guided_anchoring/ga_retinanet_r50_caffe_fpn_1x_coco/ga_retinanet_r50_caffe_fpn_1x_coco_20201020-39581c6f.pth) | [log](https://download.openmmlab.com/mmdetection/v2.0/guided_anchoring/ga_retinanet_r50_caffe_fpn_1x_coco/ga_retinanet_r50_caffe_fpn_1x_coco_20201020_225450.log.json) | -| GA-RetinaNet | R-101-FPN | caffe | 1x | 5.5 | 12.9 | 39.0 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/guided_anchoring/ga_retinanet_r101_caffe_fpn_1x_coco.py) | [model](http://download.openmmlab.com/mmdetection/v2.0/guided_anchoring/ga_retinanet_r101_caffe_fpn_1x_coco/ga_retinanet_r101_caffe_fpn_1x_coco_20200531-6266453c.pth) | [log](http://download.openmmlab.com/mmdetection/v2.0/guided_anchoring/ga_retinanet_r101_caffe_fpn_1x_coco/ga_retinanet_r101_caffe_fpn_1x_coco_20200531_012847.log.json) | -| GA-RetinaNet | X-101-32x4d-FPN | pytorch | 1x | 6.9 | 10.6 | 40.5 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/guided_anchoring/ga_retinanet_x101_32x4d_fpn_1x_coco.py) | [model](http://download.openmmlab.com/mmdetection/v2.0/guided_anchoring/ga_retinanet_x101_32x4d_fpn_1x_coco/ga_retinanet_x101_32x4d_fpn_1x_coco_20200219-40c56caa.pth) | [log](http://download.openmmlab.com/mmdetection/v2.0/guided_anchoring/ga_retinanet_x101_32x4d_fpn_1x_coco/ga_retinanet_x101_32x4d_fpn_1x_coco_20200219_223025.log.json) | -| GA-RetinaNet | X-101-64x4d-FPN | pytorch | 1x | 9.9 | 7.7 | 41.3 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/guided_anchoring/ga_retinanet_x101_64x4d_fpn_1x_coco.py) | [model](http://download.openmmlab.com/mmdetection/v2.0/guided_anchoring/ga_retinanet_x101_64x4d_fpn_1x_coco/ga_retinanet_x101_64x4d_fpn_1x_coco_20200226-ef9f7f1f.pth) | [log](http://download.openmmlab.com/mmdetection/v2.0/guided_anchoring/ga_retinanet_x101_64x4d_fpn_1x_coco/ga_retinanet_x101_64x4d_fpn_1x_coco_20200226_221123.log.json) | +| GA-RetinaNet | R-101-FPN | caffe | 1x | 5.5 | 12.9 | 39.0 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/guided_anchoring/ga_retinanet_r101_caffe_fpn_1x_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/guided_anchoring/ga_retinanet_r101_caffe_fpn_1x_coco/ga_retinanet_r101_caffe_fpn_1x_coco_20200531-6266453c.pth) | [log](https://download.openmmlab.com/mmdetection/v2.0/guided_anchoring/ga_retinanet_r101_caffe_fpn_1x_coco/ga_retinanet_r101_caffe_fpn_1x_coco_20200531_012847.log.json) | +| GA-RetinaNet | X-101-32x4d-FPN | pytorch | 1x | 6.9 | 10.6 | 40.5 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/guided_anchoring/ga_retinanet_x101_32x4d_fpn_1x_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/guided_anchoring/ga_retinanet_x101_32x4d_fpn_1x_coco/ga_retinanet_x101_32x4d_fpn_1x_coco_20200219-40c56caa.pth) | [log](https://download.openmmlab.com/mmdetection/v2.0/guided_anchoring/ga_retinanet_x101_32x4d_fpn_1x_coco/ga_retinanet_x101_32x4d_fpn_1x_coco_20200219_223025.log.json) | +| GA-RetinaNet | X-101-64x4d-FPN | pytorch | 1x | 9.9 | 7.7 | 41.3 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/guided_anchoring/ga_retinanet_x101_64x4d_fpn_1x_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/guided_anchoring/ga_retinanet_x101_64x4d_fpn_1x_coco/ga_retinanet_x101_64x4d_fpn_1x_coco_20200226-ef9f7f1f.pth) | [log](https://download.openmmlab.com/mmdetection/v2.0/guided_anchoring/ga_retinanet_x101_64x4d_fpn_1x_coco/ga_retinanet_x101_64x4d_fpn_1x_coco_20200226_221123.log.json) | - In the Guided Anchoring paper, `score_thr` is set to 0.001 in Fast/Faster RCNN and 0.05 in RetinaNet for both baselines and Guided Anchoring. diff --git a/configs/guided_anchoring/ga_fast_r50_caffe_fpn_1x_coco.py b/configs/guided_anchoring/ga_fast_r50_caffe_fpn_1x_coco.py index e15bc29..8fc203c 100644 --- a/configs/guided_anchoring/ga_fast_r50_caffe_fpn_1x_coco.py +++ b/configs/guided_anchoring/ga_fast_r50_caffe_fpn_1x_coco.py @@ -1,6 +1,5 @@ _base_ = '../fast_rcnn/fast_rcnn_r50_fpn_1x_coco.py' model = dict( - pretrained='open-mmlab://detectron2/resnet50_caffe', backbone=dict( type='ResNet', depth=50, @@ -9,7 +8,10 @@ frozen_stages=1, norm_cfg=dict(type='BN', requires_grad=False), norm_eval=True, - style='caffe'), + style='caffe', + init_cfg=dict( + type='Pretrained', + checkpoint='open-mmlab://detectron2/resnet50_caffe')), roi_head=dict( bbox_head=dict(bbox_coder=dict(target_stds=[0.05, 0.05, 0.1, 0.1]))), # model training and testing settings diff --git a/configs/guided_anchoring/ga_faster_r101_caffe_fpn_1x_coco.py b/configs/guided_anchoring/ga_faster_r101_caffe_fpn_1x_coco.py index f438a47..a40e7c6 100644 --- a/configs/guided_anchoring/ga_faster_r101_caffe_fpn_1x_coco.py +++ b/configs/guided_anchoring/ga_faster_r101_caffe_fpn_1x_coco.py @@ -1,4 +1,7 @@ _base_ = './ga_faster_r50_caffe_fpn_1x_coco.py' model = dict( - pretrained='open-mmlab://detectron2/resnet101_caffe', - backbone=dict(depth=101)) + backbone=dict( + depth=101, + init_cfg=dict( + type='Pretrained', + checkpoint='open-mmlab://detectron2/resnet101_caffe'))) diff --git a/configs/guided_anchoring/ga_faster_x101_32x4d_fpn_1x_coco.py b/configs/guided_anchoring/ga_faster_x101_32x4d_fpn_1x_coco.py index c9a035f..f1dda94 100644 --- a/configs/guided_anchoring/ga_faster_x101_32x4d_fpn_1x_coco.py +++ b/configs/guided_anchoring/ga_faster_x101_32x4d_fpn_1x_coco.py @@ -1,6 +1,5 @@ _base_ = './ga_faster_r50_fpn_1x_coco.py' model = dict( - pretrained='open-mmlab://resnext101_32x4d', backbone=dict( type='ResNeXt', depth=101, @@ -10,4 +9,6 @@ out_indices=(0, 1, 2, 3), frozen_stages=1, norm_cfg=dict(type='BN', requires_grad=True), - style='pytorch')) + style='pytorch', + init_cfg=dict( + type='Pretrained', checkpoint='open-mmlab://resnext101_32x4d'))) diff --git a/configs/guided_anchoring/ga_faster_x101_64x4d_fpn_1x_coco.py b/configs/guided_anchoring/ga_faster_x101_64x4d_fpn_1x_coco.py index 87bbfdc..fb9e2af 100644 --- a/configs/guided_anchoring/ga_faster_x101_64x4d_fpn_1x_coco.py +++ b/configs/guided_anchoring/ga_faster_x101_64x4d_fpn_1x_coco.py @@ -1,6 +1,5 @@ _base_ = './ga_faster_r50_fpn_1x_coco.py' model = dict( - pretrained='open-mmlab://resnext101_64x4d', backbone=dict( type='ResNeXt', depth=101, @@ -10,4 +9,6 @@ out_indices=(0, 1, 2, 3), frozen_stages=1, norm_cfg=dict(type='BN', requires_grad=True), - style='pytorch')) + style='pytorch', + init_cfg=dict( + type='Pretrained', checkpoint='open-mmlab://resnext101_64x4d'))) diff --git a/configs/guided_anchoring/ga_retinanet_r101_caffe_fpn_1x_coco.py b/configs/guided_anchoring/ga_retinanet_r101_caffe_fpn_1x_coco.py index 0048965..1b1cccd 100644 --- a/configs/guided_anchoring/ga_retinanet_r101_caffe_fpn_1x_coco.py +++ b/configs/guided_anchoring/ga_retinanet_r101_caffe_fpn_1x_coco.py @@ -1,4 +1,7 @@ _base_ = './ga_retinanet_r50_caffe_fpn_1x_coco.py' model = dict( - pretrained='open-mmlab://detectron2/resnet101_caffe', - backbone=dict(depth=101)) + backbone=dict( + depth=101, + init_cfg=dict( + type='Pretrained', + checkpoint='open-mmlab://detectron2/resnet101_caffe'))) diff --git a/configs/guided_anchoring/ga_retinanet_r101_caffe_fpn_mstrain_2x.py b/configs/guided_anchoring/ga_retinanet_r101_caffe_fpn_mstrain_2x.py index 85fa2f5..260895b 100644 --- a/configs/guided_anchoring/ga_retinanet_r101_caffe_fpn_mstrain_2x.py +++ b/configs/guided_anchoring/ga_retinanet_r101_caffe_fpn_mstrain_2x.py @@ -3,7 +3,6 @@ # model settings model = dict( type='RetinaNet', - pretrained='open-mmlab://detectron2/resnet101_caffe', backbone=dict( type='ResNet', depth=101, @@ -12,7 +11,10 @@ frozen_stages=1, norm_cfg=dict(type='BN', requires_grad=False), norm_eval=True, - style='caffe'), + style='caffe', + init_cfg=dict( + type='Pretrained', + checkpoint='open-mmlab://detectron2/resnet101_caffe')), neck=dict( type='FPN', in_channels=[256, 512, 1024, 2048], diff --git a/configs/guided_anchoring/ga_retinanet_x101_32x4d_fpn_1x_coco.py b/configs/guided_anchoring/ga_retinanet_x101_32x4d_fpn_1x_coco.py index 18daadd..c5eb34f 100644 --- a/configs/guided_anchoring/ga_retinanet_x101_32x4d_fpn_1x_coco.py +++ b/configs/guided_anchoring/ga_retinanet_x101_32x4d_fpn_1x_coco.py @@ -1,6 +1,5 @@ _base_ = './ga_retinanet_r50_fpn_1x_coco.py' model = dict( - pretrained='open-mmlab://resnext101_32x4d', backbone=dict( type='ResNeXt', depth=101, @@ -10,4 +9,6 @@ out_indices=(0, 1, 2, 3), frozen_stages=1, norm_cfg=dict(type='BN', requires_grad=True), - style='pytorch')) + style='pytorch', + init_cfg=dict( + type='Pretrained', checkpoint='open-mmlab://resnext101_32x4d'))) diff --git a/configs/guided_anchoring/ga_retinanet_x101_64x4d_fpn_1x_coco.py b/configs/guided_anchoring/ga_retinanet_x101_64x4d_fpn_1x_coco.py index 1b18c2b..5c69a6f 100644 --- a/configs/guided_anchoring/ga_retinanet_x101_64x4d_fpn_1x_coco.py +++ b/configs/guided_anchoring/ga_retinanet_x101_64x4d_fpn_1x_coco.py @@ -1,6 +1,5 @@ _base_ = './ga_retinanet_r50_fpn_1x_coco.py' model = dict( - pretrained='open-mmlab://resnext101_64x4d', backbone=dict( type='ResNeXt', depth=101, @@ -10,4 +9,6 @@ out_indices=(0, 1, 2, 3), frozen_stages=1, norm_cfg=dict(type='BN', requires_grad=True), - style='pytorch')) + style='pytorch', + init_cfg=dict( + type='Pretrained', checkpoint='open-mmlab://resnext101_64x4d'))) diff --git a/configs/guided_anchoring/ga_rpn_r101_caffe_fpn_1x_coco.py b/configs/guided_anchoring/ga_rpn_r101_caffe_fpn_1x_coco.py index 8d15476..039703e 100644 --- a/configs/guided_anchoring/ga_rpn_r101_caffe_fpn_1x_coco.py +++ b/configs/guided_anchoring/ga_rpn_r101_caffe_fpn_1x_coco.py @@ -1,5 +1,8 @@ _base_ = './ga_rpn_r50_caffe_fpn_1x_coco.py' # model settings model = dict( - pretrained='open-mmlab://detectron2/resnet101_caffe', - backbone=dict(depth=101)) + backbone=dict( + depth=101, + init_cfg=dict( + type='Pretrained', + checkpoint='open-mmlab://detectron2/resnet101_caffe'))) diff --git a/configs/guided_anchoring/ga_rpn_x101_32x4d_fpn_1x_coco.py b/configs/guided_anchoring/ga_rpn_x101_32x4d_fpn_1x_coco.py index 1e0fe49..cccc985 100644 --- a/configs/guided_anchoring/ga_rpn_x101_32x4d_fpn_1x_coco.py +++ b/configs/guided_anchoring/ga_rpn_x101_32x4d_fpn_1x_coco.py @@ -1,6 +1,5 @@ _base_ = './ga_rpn_r50_fpn_1x_coco.py' model = dict( - pretrained='open-mmlab://resnext101_32x4d', backbone=dict( type='ResNeXt', depth=101, @@ -10,4 +9,6 @@ out_indices=(0, 1, 2, 3), frozen_stages=1, norm_cfg=dict(type='BN', requires_grad=True), - style='pytorch')) + style='pytorch', + init_cfg=dict( + type='Pretrained', checkpoint='open-mmlab://resnext101_32x4d'))) diff --git a/configs/guided_anchoring/ga_rpn_x101_64x4d_fpn_1x_coco.py b/configs/guided_anchoring/ga_rpn_x101_64x4d_fpn_1x_coco.py index bf66b6b..4e134d2 100644 --- a/configs/guided_anchoring/ga_rpn_x101_64x4d_fpn_1x_coco.py +++ b/configs/guided_anchoring/ga_rpn_x101_64x4d_fpn_1x_coco.py @@ -1,6 +1,5 @@ _base_ = './ga_rpn_r50_fpn_1x_coco.py' model = dict( - pretrained='open-mmlab://resnext101_64x4d', backbone=dict( type='ResNeXt', depth=101, @@ -10,4 +9,6 @@ out_indices=(0, 1, 2, 3), frozen_stages=1, norm_cfg=dict(type='BN', requires_grad=True), - style='pytorch')) + style='pytorch', + init_cfg=dict( + type='Pretrained', checkpoint='open-mmlab://resnext101_64x4d'))) diff --git a/configs/guided_anchoring/metafile.yml b/configs/guided_anchoring/metafile.yml new file mode 100644 index 0000000..3019d4a --- /dev/null +++ b/configs/guided_anchoring/metafile.yml @@ -0,0 +1,246 @@ +Collections: + - Name: Guided Anchoring + Metadata: + Training Data: COCO + Training Techniques: + - SGD with Momentum + - Weight Decay + Training Resources: 8x V100 GPUs + Architecture: + - FPN + - Guided Anchoring + - ResNet + Paper: + URL: https://arxiv.org/abs/1901.03278 + Title: 'Region Proposal by Guided Anchoring' + README: configs/guided_anchoring/README.md + Code: + URL: https://github.com/open-mmlab/mmdetection/blob/v2.0.0/mmdet/models/dense_heads/ga_retina_head.py#L10 + Version: v2.0.0 + +Models: + - Name: ga_rpn_r50_caffe_fpn_1x_coco + In Collection: Guided Anchoring + Config: configs/guided_anchoring/ga_rpn_r50_caffe_fpn_1x_coco.py + Metadata: + Training Memory (GB): 5.3 + inference time (ms/im): + - value: 63.29 + hardware: V100 + backend: PyTorch + batch size: 1 + mode: FP32 + resolution: (800, 1333) + Epochs: 12 + Results: + - Task: Region Proposal + Dataset: COCO + Metrics: + AR@1000: 68.4 + Weights: https://download.openmmlab.com/mmdetection/v2.0/guided_anchoring/ga_rpn_r50_caffe_fpn_1x_coco/ga_rpn_r50_caffe_fpn_1x_coco_20200531-899008a6.pth + + - Name: ga_rpn_r101_caffe_fpn_1x_coco.py + In Collection: Guided Anchoring + Config: configs/guided_anchoring/ga_rpn_r101_caffe_fpn_1x_coco.py.py + Metadata: + Training Memory (GB): 7.3 + inference time (ms/im): + - value: 76.92 + hardware: V100 + backend: PyTorch + batch size: 1 + mode: FP32 + resolution: (800, 1333) + Epochs: 12 + Results: + - Task: Region Proposal + Dataset: COCO + Metrics: + AR@1000: 69.5 + Weights: https://download.openmmlab.com/mmdetection/v2.0/guided_anchoring/ga_rpn_r101_caffe_fpn_1x_coco/ga_rpn_r101_caffe_fpn_1x_coco_20200531-ca9ba8fb.pth + + - Name: ga_rpn_x101_32x4d_fpn_1x_coco.py + In Collection: Guided Anchoring + Config: configs/guided_anchoring/ga_rpn_x101_32x4d_fpn_1x_coco.py.py + Metadata: + Training Memory (GB): 8.5 + inference time (ms/im): + - value: 100 + hardware: V100 + backend: PyTorch + batch size: 1 + mode: FP32 + resolution: (800, 1333) + Epochs: 12 + Results: + - Task: Region Proposal + Dataset: COCO + Metrics: + AR@1000: 70.6 + Weights: https://download.openmmlab.com/mmdetection/v2.0/guided_anchoring/ga_rpn_x101_32x4d_fpn_1x_coco/ga_rpn_x101_32x4d_fpn_1x_coco_20200220-c28d1b18.pth + + - Name: ga_rpn_x101_64x4d_fpn_1x_coco.py.py + In Collection: Guided Anchoring + Config: configs/guided_anchoring/ga_rpn_x101_64x4d_fpn_1x_coco.py.py.py + Metadata: + Training Memory (GB): 7.1 + inference time (ms/im): + - value: 133.33 + hardware: V100 + backend: PyTorch + batch size: 1 + mode: FP32 + resolution: (800, 1333) + Epochs: 12 + Results: + - Task: Region Proposal + Dataset: COCO + Metrics: + AR@1000: 70.6 + Weights: https://download.openmmlab.com/mmdetection/v2.0/guided_anchoring/ga_rpn_x101_64x4d_fpn_1x_coco/ga_rpn_x101_64x4d_fpn_1x_coco_20200225-3c6e1aa2.pth + + - Name: ga_faster_r50_caffe_fpn_1x_coco + In Collection: Guided Anchoring + Config: configs/guided_anchoring/ga_faster_r50_caffe_fpn_1x_coco.py + Metadata: + Training Memory (GB): 5.5 + Epochs: 12 + Results: + - Task: Object Detection + Dataset: COCO + Metrics: + box AP: 39.6 + Weights: https://download.openmmlab.com/mmdetection/v2.0/guided_anchoring/ga_faster_r50_caffe_fpn_1x_coco/ga_faster_r50_caffe_fpn_1x_coco_20200702_000718-a11ccfe6.pth + + - Name: ga_faster_r101_caffe_fpn_1x_coco + In Collection: Guided Anchoring + Config: configs/guided_anchoring/ga_faster_r101_caffe_fpn_1x_coco.py + Metadata: + Training Memory (GB): 7.5 + Epochs: 12 + Results: + - Task: Object Detection + Dataset: COCO + Metrics: + box AP: 41.5 + Weights: https://download.openmmlab.com/mmdetection/v2.0/guided_anchoring/ga_faster_r101_caffe_fpn_1x_coco/ga_faster_r101_caffe_fpn_1x_coco_bbox_mAP-0.415_20200505_115528-fb82e499.pth + + - Name: ga_faster_x101_32x4d_fpn_1x_coco + In Collection: Guided Anchoring + Config: configs/guided_anchoring/ga_faster_x101_32x4d_fpn_1x_coco.py + Metadata: + Training Memory (GB): 8.7 + inference time (ms/im): + - value: 103.09 + hardware: V100 + backend: PyTorch + batch size: 1 + mode: FP32 + resolution: (800, 1333) + Epochs: 12 + Results: + - Task: Object Detection + Dataset: COCO + Metrics: + box AP: 43.0 + Weights: https://download.openmmlab.com/mmdetection/v2.0/guided_anchoring/ga_faster_x101_32x4d_fpn_1x_coco/ga_faster_x101_32x4d_fpn_1x_coco_20200215-1ded9da3.pth + + - Name: ga_faster_x101_64x4d_fpn_1x_coco + In Collection: Guided Anchoring + Config: configs/guided_anchoring/ga_faster_x101_64x4d_fpn_1x_coco.py + Metadata: + Training Memory (GB): 11.8 + inference time (ms/im): + - value: 136.99 + hardware: V100 + backend: PyTorch + batch size: 1 + mode: FP32 + resolution: (800, 1333) + Epochs: 12 + Results: + - Task: Object Detection + Dataset: COCO + Metrics: + box AP: 43.9 + Weights: https://download.openmmlab.com/mmdetection/v2.0/guided_anchoring/ga_faster_x101_64x4d_fpn_1x_coco/ga_faster_x101_64x4d_fpn_1x_coco_20200215-0fa7bde7.pth + + - Name: ga_retinanet_r50_caffe_fpn_1x_coco + In Collection: Guided Anchoring + Config: configs/guided_anchoring/ga_retinanet_r50_caffe_fpn_1x_coco.py + Metadata: + Training Memory (GB): 3.5 + inference time (ms/im): + - value: 59.52 + hardware: V100 + backend: PyTorch + batch size: 1 + mode: FP32 + resolution: (800, 1333) + Epochs: 12 + Results: + - Task: Object Detection + Dataset: COCO + Metrics: + box AP: 36.9 + Weights: https://download.openmmlab.com/mmdetection/v2.0/guided_anchoring/ga_retinanet_r50_caffe_fpn_1x_coco/ga_retinanet_r50_caffe_fpn_1x_coco_20201020-39581c6f.pth + + - Name: ga_retinanet_r101_caffe_fpn_1x_coco + In Collection: Guided Anchoring + Config: configs/guided_anchoring/ga_retinanet_r101_caffe_fpn_1x_coco.py + Metadata: + Training Memory (GB): 5.5 + inference time (ms/im): + - value: 77.52 + hardware: V100 + backend: PyTorch + batch size: 1 + mode: FP32 + resolution: (800, 1333) + Epochs: 12 + Results: + - Task: Object Detection + Dataset: COCO + Metrics: + box AP: 39.0 + Weights: https://download.openmmlab.com/mmdetection/v2.0/guided_anchoring/ga_retinanet_r101_caffe_fpn_1x_coco/ga_retinanet_r101_caffe_fpn_1x_coco_20200531-6266453c.pth + + - Name: ga_retinanet_x101_32x4d_fpn_1x_coco + In Collection: Guided Anchoring + Config: configs/guided_anchoring/ga_retinanet_x101_32x4d_fpn_1x_coco.py + Metadata: + Training Memory (GB): 6.9 + inference time (ms/im): + - value: 94.34 + hardware: V100 + backend: PyTorch + batch size: 1 + mode: FP32 + resolution: (800, 1333) + Epochs: 12 + Results: + - Task: Object Detection + Dataset: COCO + Metrics: + box AP: 40.5 + Weights: https://download.openmmlab.com/mmdetection/v2.0/guided_anchoring/ga_retinanet_x101_32x4d_fpn_1x_coco/ga_retinanet_x101_32x4d_fpn_1x_coco_20200219-40c56caa.pth + + - Name: ga_retinanet_x101_64x4d_fpn_1x_coco + In Collection: Guided Anchoring + Config: configs/guided_anchoring/ga_retinanet_x101_64x4d_fpn_1x_coco.py + Metadata: + Training Memory (GB): 9.9 + inference time (ms/im): + - value: 129.87 + hardware: V100 + backend: PyTorch + batch size: 1 + mode: FP32 + resolution: (800, 1333) + Epochs: 12 + Results: + - Task: Object Detection + Dataset: COCO + Metrics: + box AP: 41.3 + Weights: https://download.openmmlab.com/mmdetection/v2.0/guided_anchoring/ga_retinanet_x101_64x4d_fpn_1x_coco/ga_retinanet_x101_64x4d_fpn_1x_coco_20200226-ef9f7f1f.pth diff --git a/configs/hrnet/README.md b/configs/hrnet/README.md index 35b8878..bff5ce0 100644 --- a/configs/hrnet/README.md +++ b/configs/hrnet/README.md @@ -2,7 +2,7 @@ ## Introduction -[ALGORITHM] + ```latex @inproceedings{SunXLW19, @@ -28,59 +28,59 @@ | Backbone | Style | Lr schd | Mem (GB) | Inf time (fps) | box AP | Config | Download | | :-------------: | :-----: | :-----: | :------: | :-------------:|:------:| :------:| :--------:| -| HRNetV2p-W18 | pytorch | 1x | 6.6 | 13.4 | 36.9 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/hrnet/faster_rcnn_hrnetv2p_w18_1x_coco.py) | [model](http://download.openmmlab.com/mmdetection/v2.0/hrnet/faster_rcnn_hrnetv2p_w18_1x_coco/faster_rcnn_hrnetv2p_w18_1x_coco_20200130-56651a6d.pth) | [log](http://download.openmmlab.com/mmdetection/v2.0/hrnet/faster_rcnn_hrnetv2p_w18_1x_coco/faster_rcnn_hrnetv2p_w18_1x_coco_20200130_211246.log.json) | -| HRNetV2p-W18 | pytorch | 2x | 6.6 | | 38.9 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/hrnet/faster_rcnn_hrnetv2p_w18_2x_coco.py) | [model](http://download.openmmlab.com/mmdetection/v2.0/hrnet/faster_rcnn_hrnetv2p_w18_2x_coco/faster_rcnn_hrnetv2p_w18_2x_coco_20200702_085731-a4ec0611.pth) | [log](http://download.openmmlab.com/mmdetection/v2.0/hrnet/faster_rcnn_hrnetv2p_w18_2x_coco/faster_rcnn_hrnetv2p_w18_2x_coco_20200702_085731.log.json) | -| HRNetV2p-W32 | pytorch | 1x | 9.0 | 12.4 | 40.2 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/hrnet/faster_rcnn_hrnetv2p_w32_1x_coco.py) | [model](http://download.openmmlab.com/mmdetection/v2.0/hrnet/faster_rcnn_hrnetv2p_w32_1x_coco/faster_rcnn_hrnetv2p_w32_1x_coco_20200130-6e286425.pth) | [log](http://download.openmmlab.com/mmdetection/v2.0/hrnet/faster_rcnn_hrnetv2p_w32_1x_coco/faster_rcnn_hrnetv2p_w32_1x_coco_20200130_204442.log.json) | -| HRNetV2p-W32 | pytorch | 2x | 9.0 | | 41.4 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/hrnet/faster_rcnn_hrnetv2p_w32_2x_coco.py) | [model](http://download.openmmlab.com/mmdetection/v2.0/hrnet/faster_rcnn_hrnetv2p_w32_2x_coco/faster_rcnn_hrnetv2p_w32_2x_coco_20200529_015927-976a9c15.pth) | [log](http://download.openmmlab.com/mmdetection/v2.0/hrnet/faster_rcnn_hrnetv2p_w32_2x_coco/faster_rcnn_hrnetv2p_w32_2x_coco_20200529_015927.log.json) | -| HRNetV2p-W40 | pytorch | 1x | 10.4 | 10.5 | 41.2 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/hrnet/faster_rcnn_hrnetv2p_w40_1x_coco.py) | [model](http://download.openmmlab.com/mmdetection/v2.0/hrnet/faster_rcnn_hrnetv2p_w40_1x_coco/faster_rcnn_hrnetv2p_w40_1x_coco_20200210-95c1f5ce.pth) | [log](http://download.openmmlab.com/mmdetection/v2.0/hrnet/faster_rcnn_hrnetv2p_w40_1x_coco/faster_rcnn_hrnetv2p_w40_1x_coco_20200210_125315.log.json) | -| HRNetV2p-W40 | pytorch | 2x | 10.4 | | 42.1 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/hrnet/faster_rcnn_hrnetv2p_w40_2x_coco.py) | [model](http://download.openmmlab.com/mmdetection/v2.0/hrnet/faster_rcnn_hrnetv2p_w40_2x_coco/faster_rcnn_hrnetv2p_w40_2x_coco_20200512_161033-0f236ef4.pth) | [log](http://download.openmmlab.com/mmdetection/v2.0/hrnet/faster_rcnn_hrnetv2p_w40_2x_coco/faster_rcnn_hrnetv2p_w40_2x_coco_20200512_161033.log.json) | +| HRNetV2p-W18 | pytorch | 1x | 6.6 | 13.4 | 36.9 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/hrnet/faster_rcnn_hrnetv2p_w18_1x_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/hrnet/faster_rcnn_hrnetv2p_w18_1x_coco/faster_rcnn_hrnetv2p_w18_1x_coco_20200130-56651a6d.pth) | [log](https://download.openmmlab.com/mmdetection/v2.0/hrnet/faster_rcnn_hrnetv2p_w18_1x_coco/faster_rcnn_hrnetv2p_w18_1x_coco_20200130_211246.log.json) | +| HRNetV2p-W18 | pytorch | 2x | 6.6 | - | 38.9 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/hrnet/faster_rcnn_hrnetv2p_w18_2x_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/hrnet/faster_rcnn_hrnetv2p_w18_2x_coco/faster_rcnn_hrnetv2p_w18_2x_coco_20200702_085731-a4ec0611.pth) | [log](https://download.openmmlab.com/mmdetection/v2.0/hrnet/faster_rcnn_hrnetv2p_w18_2x_coco/faster_rcnn_hrnetv2p_w18_2x_coco_20200702_085731.log.json) | +| HRNetV2p-W32 | pytorch | 1x | 9.0 | 12.4 | 40.2 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/hrnet/faster_rcnn_hrnetv2p_w32_1x_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/hrnet/faster_rcnn_hrnetv2p_w32_1x_coco/faster_rcnn_hrnetv2p_w32_1x_coco_20200130-6e286425.pth) | [log](https://download.openmmlab.com/mmdetection/v2.0/hrnet/faster_rcnn_hrnetv2p_w32_1x_coco/faster_rcnn_hrnetv2p_w32_1x_coco_20200130_204442.log.json) | +| HRNetV2p-W32 | pytorch | 2x | 9.0 | - | 41.4 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/hrnet/faster_rcnn_hrnetv2p_w32_2x_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/hrnet/faster_rcnn_hrnetv2p_w32_2x_coco/faster_rcnn_hrnetv2p_w32_2x_coco_20200529_015927-976a9c15.pth) | [log](https://download.openmmlab.com/mmdetection/v2.0/hrnet/faster_rcnn_hrnetv2p_w32_2x_coco/faster_rcnn_hrnetv2p_w32_2x_coco_20200529_015927.log.json) | +| HRNetV2p-W40 | pytorch | 1x | 10.4 | 10.5 | 41.2 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/hrnet/faster_rcnn_hrnetv2p_w40_1x_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/hrnet/faster_rcnn_hrnetv2p_w40_1x_coco/faster_rcnn_hrnetv2p_w40_1x_coco_20200210-95c1f5ce.pth) | [log](https://download.openmmlab.com/mmdetection/v2.0/hrnet/faster_rcnn_hrnetv2p_w40_1x_coco/faster_rcnn_hrnetv2p_w40_1x_coco_20200210_125315.log.json) | +| HRNetV2p-W40 | pytorch | 2x | 10.4 | - | 42.1 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/hrnet/faster_rcnn_hrnetv2p_w40_2x_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/hrnet/faster_rcnn_hrnetv2p_w40_2x_coco/faster_rcnn_hrnetv2p_w40_2x_coco_20200512_161033-0f236ef4.pth) | [log](https://download.openmmlab.com/mmdetection/v2.0/hrnet/faster_rcnn_hrnetv2p_w40_2x_coco/faster_rcnn_hrnetv2p_w40_2x_coco_20200512_161033.log.json) | ### Mask R-CNN | Backbone | Style | Lr schd | Mem (GB) | Inf time (fps) | box AP | mask AP | Config | Download | | :-------------: | :-----: | :-----: | :------: | :-------------:|:------:| :------:|:------:|:--------:| -| HRNetV2p-W18 | pytorch | 1x | 7.0 | 11.7 | 37.7 | 34.2 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/hrnet/mask_rcnn_hrnetv2p_w18_1x_coco.py) | [model](http://download.openmmlab.com/mmdetection/v2.0/hrnet/mask_rcnn_hrnetv2p_w18_1x_coco/mask_rcnn_hrnetv2p_w18_1x_coco_20200205-1c3d78ed.pth) | [log](http://download.openmmlab.com/mmdetection/v2.0/hrnet/mask_rcnn_hrnetv2p_w18_1x_coco/mask_rcnn_hrnetv2p_w18_1x_coco_20200205_232523.log.json) | -| HRNetV2p-W18 | pytorch | 2x | 7.0 | - | 39.8 | 36.0 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/hrnet/mask_rcnn_hrnetv2p_w18_2x_coco.py) | [model](http://download.openmmlab.com/mmdetection/v2.0/hrnet/mask_rcnn_hrnetv2p_w18_2x_coco/mask_rcnn_hrnetv2p_w18_2x_coco_20200212-b3c825b1.pth) | [log](http://download.openmmlab.com/mmdetection/v2.0/hrnet/mask_rcnn_hrnetv2p_w18_2x_coco/mask_rcnn_hrnetv2p_w18_2x_coco_20200212_134222.log.json) | -| HRNetV2p-W32 | pytorch | 1x | 9.4 | 11.3 | 41.2 | 37.1 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/hrnet/mask_rcnn_hrnetv2p_w32_1x_coco.py) | [model](http://download.openmmlab.com/mmdetection/v2.0/hrnet/mask_rcnn_hrnetv2p_w32_1x_coco/mask_rcnn_hrnetv2p_w32_1x_coco_20200207-b29f616e.pth) | [log](http://download.openmmlab.com/mmdetection/v2.0/hrnet/mask_rcnn_hrnetv2p_w32_1x_coco/mask_rcnn_hrnetv2p_w32_1x_coco_20200207_055017.log.json) | -| HRNetV2p-W32 | pytorch | 2x | 9.4 | - | 42.5 | 37.8 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/hrnet/mask_rcnn_hrnetv2p_w32_2x_coco.py) | [model](http://download.openmmlab.com/mmdetection/v2.0/hrnet/mask_rcnn_hrnetv2p_w32_2x_coco/mask_rcnn_hrnetv2p_w32_2x_coco_20200213-45b75b4d.pth) | [log](http://download.openmmlab.com/mmdetection/v2.0/hrnet/mask_rcnn_hrnetv2p_w32_2x_coco/mask_rcnn_hrnetv2p_w32_2x_coco_20200213_150518.log.json) | -| HRNetV2p-W40 | pytorch | 1x | 10.9 | | 42.1 | 37.5 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/hrnet/mask_rcnn_hrnetv2p_w40_1x_coco.py) | [model](http://download.openmmlab.com/mmdetection/v2.0/hrnet/mask_rcnn_hrnetv2p_w40_1x_coco/mask_rcnn_hrnetv2p_w40_1x_coco_20200511_015646-66738b35.pth) | [log](http://download.openmmlab.com/mmdetection/v2.0/hrnet/mask_rcnn_hrnetv2p_w40_1x_coco/mask_rcnn_hrnetv2p_w40_1x_coco_20200511_015646.log.json) | -| HRNetV2p-W40 | pytorch | 2x | 10.9 | | 42.8 | 38.2 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/hrnet/mask_rcnn_hrnetv2p_w40_2x_coco.py) | [model](http://download.openmmlab.com/mmdetection/v2.0/hrnet/mask_rcnn_hrnetv2p_w40_2x_coco/mask_rcnn_hrnetv2p_w40_2x_coco_20200512_163732-aed5e4ab.pth) | [log](http://download.openmmlab.com/mmdetection/v2.0/hrnet/mask_rcnn_hrnetv2p_w40_2x_coco/mask_rcnn_hrnetv2p_w40_2x_coco_20200512_163732.log.json) | +| HRNetV2p-W18 | pytorch | 1x | 7.0 | 11.7 | 37.7 | 34.2 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/hrnet/mask_rcnn_hrnetv2p_w18_1x_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/hrnet/mask_rcnn_hrnetv2p_w18_1x_coco/mask_rcnn_hrnetv2p_w18_1x_coco_20200205-1c3d78ed.pth) | [log](https://download.openmmlab.com/mmdetection/v2.0/hrnet/mask_rcnn_hrnetv2p_w18_1x_coco/mask_rcnn_hrnetv2p_w18_1x_coco_20200205_232523.log.json) | +| HRNetV2p-W18 | pytorch | 2x | 7.0 | - | 39.8 | 36.0 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/hrnet/mask_rcnn_hrnetv2p_w18_2x_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/hrnet/mask_rcnn_hrnetv2p_w18_2x_coco/mask_rcnn_hrnetv2p_w18_2x_coco_20200212-b3c825b1.pth) | [log](https://download.openmmlab.com/mmdetection/v2.0/hrnet/mask_rcnn_hrnetv2p_w18_2x_coco/mask_rcnn_hrnetv2p_w18_2x_coco_20200212_134222.log.json) | +| HRNetV2p-W32 | pytorch | 1x | 9.4 | 11.3 | 41.2 | 37.1 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/hrnet/mask_rcnn_hrnetv2p_w32_1x_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/hrnet/mask_rcnn_hrnetv2p_w32_1x_coco/mask_rcnn_hrnetv2p_w32_1x_coco_20200207-b29f616e.pth) | [log](https://download.openmmlab.com/mmdetection/v2.0/hrnet/mask_rcnn_hrnetv2p_w32_1x_coco/mask_rcnn_hrnetv2p_w32_1x_coco_20200207_055017.log.json) | +| HRNetV2p-W32 | pytorch | 2x | 9.4 | - | 42.5 | 37.8 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/hrnet/mask_rcnn_hrnetv2p_w32_2x_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/hrnet/mask_rcnn_hrnetv2p_w32_2x_coco/mask_rcnn_hrnetv2p_w32_2x_coco_20200213-45b75b4d.pth) | [log](https://download.openmmlab.com/mmdetection/v2.0/hrnet/mask_rcnn_hrnetv2p_w32_2x_coco/mask_rcnn_hrnetv2p_w32_2x_coco_20200213_150518.log.json) | +| HRNetV2p-W40 | pytorch | 1x | 10.9 | | 42.1 | 37.5 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/hrnet/mask_rcnn_hrnetv2p_w40_1x_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/hrnet/mask_rcnn_hrnetv2p_w40_1x_coco/mask_rcnn_hrnetv2p_w40_1x_coco_20200511_015646-66738b35.pth) | [log](https://download.openmmlab.com/mmdetection/v2.0/hrnet/mask_rcnn_hrnetv2p_w40_1x_coco/mask_rcnn_hrnetv2p_w40_1x_coco_20200511_015646.log.json) | +| HRNetV2p-W40 | pytorch | 2x | 10.9 | | 42.8 | 38.2 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/hrnet/mask_rcnn_hrnetv2p_w40_2x_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/hrnet/mask_rcnn_hrnetv2p_w40_2x_coco/mask_rcnn_hrnetv2p_w40_2x_coco_20200512_163732-aed5e4ab.pth) | [log](https://download.openmmlab.com/mmdetection/v2.0/hrnet/mask_rcnn_hrnetv2p_w40_2x_coco/mask_rcnn_hrnetv2p_w40_2x_coco_20200512_163732.log.json) | ### Cascade R-CNN | Backbone | Style | Lr schd | Mem (GB) | Inf time (fps) | box AP | Config | Download | | :-------------: | :-----: | :-----: | :------: | :-------------:|:------:| :------: | :--------: | -| HRNetV2p-W18 | pytorch | 20e | 7.0 | 11.0 | 41.2 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/hrnet/cascade_rcnn_hrnetv2p_w18_20e_coco.py) | [model](http://download.openmmlab.com/mmdetection/v2.0/hrnet/cascade_rcnn_hrnetv2p_w18_20e_coco/cascade_rcnn_hrnetv2p_w18_20e_coco_20200210-434be9d7.pth) | [log](http://download.openmmlab.com/mmdetection/v2.0/hrnet/cascade_rcnn_hrnetv2p_w18_20e_coco/cascade_rcnn_hrnetv2p_w18_20e_coco_20200210_105632.log.json) | -| HRNetV2p-W32 | pytorch | 20e | 9.4 | 11.0 | 43.3 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/hrnet/cascade_rcnn_hrnetv2p_w32_20e_coco.py) | [model](http://download.openmmlab.com/mmdetection/v2.0/hrnet/cascade_rcnn_hrnetv2p_w32_20e_coco/cascade_rcnn_hrnetv2p_w32_20e_coco_20200208-928455a4.pth) | [log](http://download.openmmlab.com/mmdetection/v2.0/hrnet/cascade_rcnn_hrnetv2p_w32_20e_coco/cascade_rcnn_hrnetv2p_w32_20e_coco_20200208_160511.log.json) | -| HRNetV2p-W40 | pytorch | 20e | 10.8 | | 43.8 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/hrnet/cascade_rcnn_hrnetv2p_w40_20e_coco.py) | [model](http://download.openmmlab.com/mmdetection/v2.0/hrnet/cascade_rcnn_hrnetv2p_w40_20e_coco/cascade_rcnn_hrnetv2p_w40_20e_coco_20200512_161112-75e47b04.pth) | [log](http://download.openmmlab.com/mmdetection/v2.0/hrnet/cascade_rcnn_hrnetv2p_w40_20e_coco/cascade_rcnn_hrnetv2p_w40_20e_coco_20200512_161112.log.json) | +| HRNetV2p-W18 | pytorch | 20e | 7.0 | 11.0 | 41.2 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/hrnet/cascade_rcnn_hrnetv2p_w18_20e_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/hrnet/cascade_rcnn_hrnetv2p_w18_20e_coco/cascade_rcnn_hrnetv2p_w18_20e_coco_20200210-434be9d7.pth) | [log](https://download.openmmlab.com/mmdetection/v2.0/hrnet/cascade_rcnn_hrnetv2p_w18_20e_coco/cascade_rcnn_hrnetv2p_w18_20e_coco_20200210_105632.log.json) | +| HRNetV2p-W32 | pytorch | 20e | 9.4 | 11.0 | 43.3 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/hrnet/cascade_rcnn_hrnetv2p_w32_20e_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/hrnet/cascade_rcnn_hrnetv2p_w32_20e_coco/cascade_rcnn_hrnetv2p_w32_20e_coco_20200208-928455a4.pth) | [log](https://download.openmmlab.com/mmdetection/v2.0/hrnet/cascade_rcnn_hrnetv2p_w32_20e_coco/cascade_rcnn_hrnetv2p_w32_20e_coco_20200208_160511.log.json) | +| HRNetV2p-W40 | pytorch | 20e | 10.8 | | 43.8 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/hrnet/cascade_rcnn_hrnetv2p_w40_20e_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/hrnet/cascade_rcnn_hrnetv2p_w40_20e_coco/cascade_rcnn_hrnetv2p_w40_20e_coco_20200512_161112-75e47b04.pth) | [log](https://download.openmmlab.com/mmdetection/v2.0/hrnet/cascade_rcnn_hrnetv2p_w40_20e_coco/cascade_rcnn_hrnetv2p_w40_20e_coco_20200512_161112.log.json) | ### Cascade Mask R-CNN | Backbone | Style | Lr schd | Mem (GB) | Inf time (fps) | box AP | mask AP | Config | Download | | :-------------: | :-----: | :-----: | :------: | :-------------:|:------:| :------:|:------:|:--------:| -| HRNetV2p-W18 | pytorch | 20e | 8.5 | 8.5 |41.6 |36.4 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/hrnet/cascade_mask_rcnn_hrnetv2p_w18_20e_coco.py) | [model](http://download.openmmlab.com/mmdetection/v2.0/hrnet/cascade_mask_rcnn_hrnetv2p_w18_20e_coco/cascade_mask_rcnn_hrnetv2p_w18_20e_coco_20200210-b543cd2b.pth) | [log](http://download.openmmlab.com/mmdetection/v2.0/hrnet/cascade_mask_rcnn_hrnetv2p_w18_20e_coco/cascade_mask_rcnn_hrnetv2p_w18_20e_coco_20200210_093149.log.json) | -| HRNetV2p-W32 | pytorch | 20e | | 8.3 |44.3 |38.6 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/hrnet/cascade_mask_rcnn_hrnetv2p_w32_20e_coco.py) | [model](http://download.openmmlab.com/mmdetection/v2.0/hrnet/cascade_mask_rcnn_hrnetv2p_w32_20e_coco/cascade_mask_rcnn_hrnetv2p_w32_20e_coco_20200512_154043-39d9cf7b.pth) | [log](http://download.openmmlab.com/mmdetection/v2.0/hrnet/cascade_mask_rcnn_hrnetv2p_w32_20e_coco/cascade_mask_rcnn_hrnetv2p_w32_20e_coco_20200512_154043.log.json) | -| HRNetV2p-W40 | pytorch | 20e | 12.5 | |45.1 |39.3 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/hrnet/cascade_mask_rcnn_hrnetv2p_w40_20e_coco.py) | [model](http://download.openmmlab.com/mmdetection/v2.0/hrnet/cascade_mask_rcnn_hrnetv2p_w40_20e_coco/cascade_mask_rcnn_hrnetv2p_w40_20e_coco_20200527_204922-969c4610.pth) | [log](http://download.openmmlab.com/mmdetection/v2.0/hrnet/cascade_mask_rcnn_hrnetv2p_w40_20e_coco/cascade_mask_rcnn_hrnetv2p_w40_20e_coco_20200527_204922.log.json) | +| HRNetV2p-W18 | pytorch | 20e | 8.5 | 8.5 |41.6 |36.4 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/hrnet/cascade_mask_rcnn_hrnetv2p_w18_20e_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/hrnet/cascade_mask_rcnn_hrnetv2p_w18_20e_coco/cascade_mask_rcnn_hrnetv2p_w18_20e_coco_20200210-b543cd2b.pth) | [log](https://download.openmmlab.com/mmdetection/v2.0/hrnet/cascade_mask_rcnn_hrnetv2p_w18_20e_coco/cascade_mask_rcnn_hrnetv2p_w18_20e_coco_20200210_093149.log.json) | +| HRNetV2p-W32 | pytorch | 20e | | 8.3 |44.3 |38.6 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/hrnet/cascade_mask_rcnn_hrnetv2p_w32_20e_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/hrnet/cascade_mask_rcnn_hrnetv2p_w32_20e_coco/cascade_mask_rcnn_hrnetv2p_w32_20e_coco_20200512_154043-39d9cf7b.pth) | [log](https://download.openmmlab.com/mmdetection/v2.0/hrnet/cascade_mask_rcnn_hrnetv2p_w32_20e_coco/cascade_mask_rcnn_hrnetv2p_w32_20e_coco_20200512_154043.log.json) | +| HRNetV2p-W40 | pytorch | 20e | 12.5 | |45.1 |39.3 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/hrnet/cascade_mask_rcnn_hrnetv2p_w40_20e_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/hrnet/cascade_mask_rcnn_hrnetv2p_w40_20e_coco/cascade_mask_rcnn_hrnetv2p_w40_20e_coco_20200527_204922-969c4610.pth) | [log](https://download.openmmlab.com/mmdetection/v2.0/hrnet/cascade_mask_rcnn_hrnetv2p_w40_20e_coco/cascade_mask_rcnn_hrnetv2p_w40_20e_coco_20200527_204922.log.json) | ### Hybrid Task Cascade (HTC) | Backbone | Style | Lr schd | Mem (GB) | Inf time (fps) | box AP | mask AP | Config | Download | | :-------------: | :-----: | :-----: | :------: | :-------------:|:------:| :------:|:------:|:--------:| -| HRNetV2p-W18 | pytorch | 20e | 10.8 | 4.7 | 42.8 | 37.9 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/hrnet/htc_hrnetv2p_w18_20e_coco.py) | [model](http://download.openmmlab.com/mmdetection/v2.0/hrnet/htc_hrnetv2p_w18_20e_coco/htc_hrnetv2p_w18_20e_coco_20200210-b266988c.pth) | [log](http://download.openmmlab.com/mmdetection/v2.0/hrnet/htc_hrnetv2p_w18_20e_coco/htc_hrnetv2p_w18_20e_coco_20200210_182735.log.json) | -| HRNetV2p-W32 | pytorch | 20e | 13.1 | 4.9 | 45.4 | 39.9 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/hrnet/htc_hrnetv2p_w32_20e_coco.py) | [model](http://download.openmmlab.com/mmdetection/v2.0/hrnet/htc_hrnetv2p_w32_20e_coco/htc_hrnetv2p_w32_20e_coco_20200207-7639fa12.pth) | [log](http://download.openmmlab.com/mmdetection/v2.0/hrnet/htc_hrnetv2p_w32_20e_coco/htc_hrnetv2p_w32_20e_coco_20200207_193153.log.json) | -| HRNetV2p-W40 | pytorch | 20e | 14.6 | | 46.4 | 40.8 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/hrnet/htc_hrnetv2p_w40_20e_coco.py) | [model](http://download.openmmlab.com/mmdetection/v2.0/hrnet/htc_hrnetv2p_w40_20e_coco/htc_hrnetv2p_w40_20e_coco_20200529_183411-417c4d5b.pth) | [log](http://download.openmmlab.com/mmdetection/v2.0/hrnet/htc_hrnetv2p_w40_20e_coco/htc_hrnetv2p_w40_20e_coco_20200529_183411.log.json) | +| HRNetV2p-W18 | pytorch | 20e | 10.8 | 4.7 | 42.8 | 37.9 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/hrnet/htc_hrnetv2p_w18_20e_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/hrnet/htc_hrnetv2p_w18_20e_coco/htc_hrnetv2p_w18_20e_coco_20200210-b266988c.pth) | [log](https://download.openmmlab.com/mmdetection/v2.0/hrnet/htc_hrnetv2p_w18_20e_coco/htc_hrnetv2p_w18_20e_coco_20200210_182735.log.json) | +| HRNetV2p-W32 | pytorch | 20e | 13.1 | 4.9 | 45.4 | 39.9 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/hrnet/htc_hrnetv2p_w32_20e_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/hrnet/htc_hrnetv2p_w32_20e_coco/htc_hrnetv2p_w32_20e_coco_20200207-7639fa12.pth) | [log](https://download.openmmlab.com/mmdetection/v2.0/hrnet/htc_hrnetv2p_w32_20e_coco/htc_hrnetv2p_w32_20e_coco_20200207_193153.log.json) | +| HRNetV2p-W40 | pytorch | 20e | 14.6 | | 46.4 | 40.8 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/hrnet/htc_hrnetv2p_w40_20e_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/hrnet/htc_hrnetv2p_w40_20e_coco/htc_hrnetv2p_w40_20e_coco_20200529_183411-417c4d5b.pth) | [log](https://download.openmmlab.com/mmdetection/v2.0/hrnet/htc_hrnetv2p_w40_20e_coco/htc_hrnetv2p_w40_20e_coco_20200529_183411.log.json) | ### FCOS | Backbone | Style | GN | MS train | Lr schd | Mem (GB) | Inf time (fps) | box AP | Config | Download | |:---------:|:-------:|:-------:|:--------:|:-------:|:------:|:------:|:------:|:------:|:--------:| |HRNetV2p-W18| pytorch | Y | N | 1x | 13.0 | 12.9 | 35.3 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/hrnet/fcos_hrnetv2p_w18_gn-head_4x4_1x_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/hrnet/fcos_hrnetv2p_w18_gn-head_4x4_1x_coco/fcos_hrnetv2p_w18_gn-head_4x4_1x_coco_20201212_100710-4ad151de.pth) | [log](https://download.openmmlab.com/mmdetection/v2.0/hrnet/fcos_hrnetv2p_w18_gn-head_4x4_1x_coco/fcos_hrnetv2p_w18_gn-head_4x4_1x_coco_20201212_100710.log.json) | -|HRNetV2p-W18| pytorch | Y | N | 2x | 13.0 | - | 38.2 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/hrnet/fcos_hrnetv2p_w18_gn-head_4x4_2x_coco.py) | [model](http://download.openmmlab.com/mmdetection/v2.0/hrnet/fcos_hrnetv2p_w18_gn-head_4x4_2x_coco/fcos_hrnetv2p_w18_gn-head_4x4_2x_coco_20201212_101110-5c575fa5.pth) | [log](http://download.openmmlab.com/mmdetection/v2.0/hrnet/fcos_hrnetv2p_w18_gn-head_4x4_2x_coco/fcos_hrnetv2p_w18_gn-head_4x4_2x_coco_20201212_101110.log.json) | -|HRNetV2p-W32| pytorch | Y | N | 1x | 17.5 | 12.9 | 39.5 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/hrnet/fcos_hrnetv2p_w32_gn-head_4x4_1x_coco.py) | [model](http://download.openmmlab.com/mmdetection/v2.0/hrnet/fcos_hrnetv2p_w32_gn-head_4x4_1x_coco/fcos_hrnetv2p_w32_gn-head_4x4_1x_coco_20201211_134730-cb8055c0.pth) | [log](http://download.openmmlab.com/mmdetection/v2.0/hrnet/fcos_hrnetv2p_w32_gn-head_4x4_1x_coco/fcos_hrnetv2p_w32_gn-head_4x4_1x_coco_20201211_134730.log.json) | -|HRNetV2p-W32| pytorch | Y | N | 2x | 17.5 | - | 40.8 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/hrnet/fcos_hrnetv2p_w32_gn-head_4x4_2x_coco.py) | [model](http://download.openmmlab.com/mmdetection/v2.0/hrnet/fcos_hrnetv2p_w32_gn-head_4x4_2x_coco/fcos_hrnetv2p_w32_gn-head_4x4_2x_coco_20201212_112133-77b6b9bb.pth) | [log](http://download.openmmlab.com/mmdetection/v2.0/hrnet/fcos_hrnetv2p_w32_gn-head_4x4_2x_coco/fcos_hrnetv2p_w32_gn-head_4x4_2x_coco_20201212_112133.log.json) | -|HRNetV2p-W18| pytorch | Y | Y | 2x | 13.0 | 12.9 | 38.3 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/hrnet/fcos_hrnetv2p_w18_gn-head_mstrain_640-800_4x4_2x_coco.py) | [model](http://download.openmmlab.com/mmdetection/v2.0/hrnet/fcos_hrnetv2p_w18_gn-head_mstrain_640-800_4x4_2x_coco/fcos_hrnetv2p_w18_gn-head_mstrain_640-800_4x4_2x_coco_20201212_111651-441e9d9f.pth) | [log](http://download.openmmlab.com/mmdetection/v2.0/hrnet/fcos_hrnetv2p_w18_gn-head_mstrain_640-800_4x4_2x_coco/fcos_hrnetv2p_w18_gn-head_mstrain_640-800_4x4_2x_coco_20201212_111651.log.json) | -|HRNetV2p-W32| pytorch | Y | Y | 2x | 17.5 | 12.4 | 41.9 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/hrnet/fcos_hrnetv2p_w32_gn-head_mstrain_640-800_4x4_2x_coco.py) | [model](http://download.openmmlab.com/mmdetection/v2.0/hrnet/fcos_hrnetv2p_w32_gn-head_mstrain_640-800_4x4_2x_coco/fcos_hrnetv2p_w32_gn-head_mstrain_640-800_4x4_2x_coco_20201212_090846-b6f2b49f.pth) | [log](http://download.openmmlab.com/mmdetection/v2.0/hrnet/fcos_hrnetv2p_w32_gn-head_mstrain_640-800_4x4_2x_coco/fcos_hrnetv2p_w32_gn-head_mstrain_640-800_4x4_2x_coco_20201212_090846.log.json) | -|HRNetV2p-W48| pytorch | Y | Y | 2x | 20.3 | 10.8 | 42.7 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/hrnet/fcos_hrnetv2p_w40_gn-head_mstrain_640-800_4x4_2x_coco.py) | [model](http://download.openmmlab.com/mmdetection/v2.0/hrnet/fcos_hrnetv2p_w40_gn-head_mstrain_640-800_4x4_2x_coco/fcos_hrnetv2p_w40_gn-head_mstrain_640-800_4x4_2x_coco_20201212_124752-f22d2ce5.pth) | [log](http://download.openmmlab.com/mmdetection/v2.0/hrnet/fcos_hrnetv2p_w40_gn-head_mstrain_640-800_4x4_2x_coco/fcos_hrnetv2p_w40_gn-head_mstrain_640-800_4x4_2x_coco_20201212_124752.log.json) | +|HRNetV2p-W18| pytorch | Y | N | 2x | 13.0 | - | 38.2 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/hrnet/fcos_hrnetv2p_w18_gn-head_4x4_2x_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/hrnet/fcos_hrnetv2p_w18_gn-head_4x4_2x_coco/fcos_hrnetv2p_w18_gn-head_4x4_2x_coco_20201212_101110-5c575fa5.pth) | [log](https://download.openmmlab.com/mmdetection/v2.0/hrnet/fcos_hrnetv2p_w18_gn-head_4x4_2x_coco/fcos_hrnetv2p_w18_gn-head_4x4_2x_coco_20201212_101110.log.json) | +|HRNetV2p-W32| pytorch | Y | N | 1x | 17.5 | 12.9 | 39.5 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/hrnet/fcos_hrnetv2p_w32_gn-head_4x4_1x_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/hrnet/fcos_hrnetv2p_w32_gn-head_4x4_1x_coco/fcos_hrnetv2p_w32_gn-head_4x4_1x_coco_20201211_134730-cb8055c0.pth) | [log](https://download.openmmlab.com/mmdetection/v2.0/hrnet/fcos_hrnetv2p_w32_gn-head_4x4_1x_coco/fcos_hrnetv2p_w32_gn-head_4x4_1x_coco_20201211_134730.log.json) | +|HRNetV2p-W32| pytorch | Y | N | 2x | 17.5 | - | 40.8 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/hrnet/fcos_hrnetv2p_w32_gn-head_4x4_2x_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/hrnet/fcos_hrnetv2p_w32_gn-head_4x4_2x_coco/fcos_hrnetv2p_w32_gn-head_4x4_2x_coco_20201212_112133-77b6b9bb.pth) | [log](https://download.openmmlab.com/mmdetection/v2.0/hrnet/fcos_hrnetv2p_w32_gn-head_4x4_2x_coco/fcos_hrnetv2p_w32_gn-head_4x4_2x_coco_20201212_112133.log.json) | +|HRNetV2p-W18| pytorch | Y | Y | 2x | 13.0 | 12.9 | 38.3 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/hrnet/fcos_hrnetv2p_w18_gn-head_mstrain_640-800_4x4_2x_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/hrnet/fcos_hrnetv2p_w18_gn-head_mstrain_640-800_4x4_2x_coco/fcos_hrnetv2p_w18_gn-head_mstrain_640-800_4x4_2x_coco_20201212_111651-441e9d9f.pth) | [log](https://download.openmmlab.com/mmdetection/v2.0/hrnet/fcos_hrnetv2p_w18_gn-head_mstrain_640-800_4x4_2x_coco/fcos_hrnetv2p_w18_gn-head_mstrain_640-800_4x4_2x_coco_20201212_111651.log.json) | +|HRNetV2p-W32| pytorch | Y | Y | 2x | 17.5 | 12.4 | 41.9 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/hrnet/fcos_hrnetv2p_w32_gn-head_mstrain_640-800_4x4_2x_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/hrnet/fcos_hrnetv2p_w32_gn-head_mstrain_640-800_4x4_2x_coco/fcos_hrnetv2p_w32_gn-head_mstrain_640-800_4x4_2x_coco_20201212_090846-b6f2b49f.pth) | [log](https://download.openmmlab.com/mmdetection/v2.0/hrnet/fcos_hrnetv2p_w32_gn-head_mstrain_640-800_4x4_2x_coco/fcos_hrnetv2p_w32_gn-head_mstrain_640-800_4x4_2x_coco_20201212_090846.log.json) | +|HRNetV2p-W48| pytorch | Y | Y | 2x | 20.3 | 10.8 | 42.7 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/hrnet/fcos_hrnetv2p_w40_gn-head_mstrain_640-800_4x4_2x_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/hrnet/fcos_hrnetv2p_w40_gn-head_mstrain_640-800_4x4_2x_coco/fcos_hrnetv2p_w40_gn-head_mstrain_640-800_4x4_2x_coco_20201212_124752-f22d2ce5.pth) | [log](https://download.openmmlab.com/mmdetection/v2.0/hrnet/fcos_hrnetv2p_w40_gn-head_mstrain_640-800_4x4_2x_coco/fcos_hrnetv2p_w40_gn-head_mstrain_640-800_4x4_2x_coco_20201212_124752.log.json) | **Note:** diff --git a/configs/hrnet/cascade_mask_rcnn_hrnetv2p_w18_20e_coco.py b/configs/hrnet/cascade_mask_rcnn_hrnetv2p_w18_20e_coco.py index e8df265..839cf3e 100644 --- a/configs/hrnet/cascade_mask_rcnn_hrnetv2p_w18_20e_coco.py +++ b/configs/hrnet/cascade_mask_rcnn_hrnetv2p_w18_20e_coco.py @@ -1,10 +1,11 @@ _base_ = './cascade_mask_rcnn_hrnetv2p_w32_20e_coco.py' # model settings model = dict( - pretrained='open-mmlab://msra/hrnetv2_w18', backbone=dict( extra=dict( stage2=dict(num_channels=(18, 36)), stage3=dict(num_channels=(18, 36, 72)), - stage4=dict(num_channels=(18, 36, 72, 144)))), + stage4=dict(num_channels=(18, 36, 72, 144))), + init_cfg=dict( + type='Pretrained', checkpoint='open-mmlab://msra/hrnetv2_w18')), neck=dict(type='HRFPN', in_channels=[18, 36, 72, 144], out_channels=256)) diff --git a/configs/hrnet/cascade_mask_rcnn_hrnetv2p_w32_20e_coco.py b/configs/hrnet/cascade_mask_rcnn_hrnetv2p_w32_20e_coco.py index d410f23..9942602 100644 --- a/configs/hrnet/cascade_mask_rcnn_hrnetv2p_w32_20e_coco.py +++ b/configs/hrnet/cascade_mask_rcnn_hrnetv2p_w32_20e_coco.py @@ -1,6 +1,5 @@ _base_ = '../cascade_rcnn/cascade_mask_rcnn_r50_fpn_1x_coco.py' model = dict( - pretrained='open-mmlab://msra/hrnetv2_w32', backbone=dict( _delete_=True, type='HRNet', @@ -28,7 +27,9 @@ num_branches=4, block='BASIC', num_blocks=(4, 4, 4, 4), - num_channels=(32, 64, 128, 256)))), + num_channels=(32, 64, 128, 256))), + init_cfg=dict( + type='Pretrained', checkpoint='open-mmlab://msra/hrnetv2_w32')), neck=dict( _delete_=True, type='HRFPN', diff --git a/configs/hrnet/cascade_mask_rcnn_hrnetv2p_w40_20e_coco.py b/configs/hrnet/cascade_mask_rcnn_hrnetv2p_w40_20e_coco.py index 29b1469..10d5e83 100644 --- a/configs/hrnet/cascade_mask_rcnn_hrnetv2p_w40_20e_coco.py +++ b/configs/hrnet/cascade_mask_rcnn_hrnetv2p_w40_20e_coco.py @@ -1,11 +1,12 @@ _base_ = './cascade_mask_rcnn_hrnetv2p_w32_20e_coco.py' # model settings model = dict( - pretrained='open-mmlab://msra/hrnetv2_w40', backbone=dict( type='HRNet', extra=dict( stage2=dict(num_channels=(40, 80)), stage3=dict(num_channels=(40, 80, 160)), - stage4=dict(num_channels=(40, 80, 160, 320)))), + stage4=dict(num_channels=(40, 80, 160, 320))), + init_cfg=dict( + type='Pretrained', checkpoint='open-mmlab://msra/hrnetv2_w40')), neck=dict(type='HRFPN', in_channels=[40, 80, 160, 320], out_channels=256)) diff --git a/configs/hrnet/cascade_rcnn_hrnetv2p_w18_20e_coco.py b/configs/hrnet/cascade_rcnn_hrnetv2p_w18_20e_coco.py index 9585a4f..ebd5e20 100644 --- a/configs/hrnet/cascade_rcnn_hrnetv2p_w18_20e_coco.py +++ b/configs/hrnet/cascade_rcnn_hrnetv2p_w18_20e_coco.py @@ -1,10 +1,11 @@ _base_ = './cascade_rcnn_hrnetv2p_w32_20e_coco.py' # model settings model = dict( - pretrained='open-mmlab://msra/hrnetv2_w18', backbone=dict( extra=dict( stage2=dict(num_channels=(18, 36)), stage3=dict(num_channels=(18, 36, 72)), - stage4=dict(num_channels=(18, 36, 72, 144)))), + stage4=dict(num_channels=(18, 36, 72, 144))), + init_cfg=dict( + type='Pretrained', checkpoint='open-mmlab://msra/hrnetv2_w18')), neck=dict(type='HRFPN', in_channels=[18, 36, 72, 144], out_channels=256)) diff --git a/configs/hrnet/cascade_rcnn_hrnetv2p_w32_20e_coco.py b/configs/hrnet/cascade_rcnn_hrnetv2p_w32_20e_coco.py index ec1bb76..e7f89a9 100644 --- a/configs/hrnet/cascade_rcnn_hrnetv2p_w32_20e_coco.py +++ b/configs/hrnet/cascade_rcnn_hrnetv2p_w32_20e_coco.py @@ -1,6 +1,5 @@ _base_ = '../cascade_rcnn/cascade_rcnn_r50_fpn_1x_coco.py' model = dict( - pretrained='open-mmlab://msra/hrnetv2_w32', backbone=dict( _delete_=True, type='HRNet', @@ -28,7 +27,9 @@ num_branches=4, block='BASIC', num_blocks=(4, 4, 4, 4), - num_channels=(32, 64, 128, 256)))), + num_channels=(32, 64, 128, 256))), + init_cfg=dict( + type='Pretrained', checkpoint='open-mmlab://msra/hrnetv2_w32')), neck=dict( _delete_=True, type='HRFPN', diff --git a/configs/hrnet/cascade_rcnn_hrnetv2p_w40_20e_coco.py b/configs/hrnet/cascade_rcnn_hrnetv2p_w40_20e_coco.py index bd43e47..265e8d6 100644 --- a/configs/hrnet/cascade_rcnn_hrnetv2p_w40_20e_coco.py +++ b/configs/hrnet/cascade_rcnn_hrnetv2p_w40_20e_coco.py @@ -1,11 +1,12 @@ _base_ = './cascade_rcnn_hrnetv2p_w32_20e_coco.py' # model settings model = dict( - pretrained='open-mmlab://msra/hrnetv2_w40', backbone=dict( type='HRNet', extra=dict( stage2=dict(num_channels=(40, 80)), stage3=dict(num_channels=(40, 80, 160)), - stage4=dict(num_channels=(40, 80, 160, 320)))), + stage4=dict(num_channels=(40, 80, 160, 320))), + init_cfg=dict( + type='Pretrained', checkpoint='open-mmlab://msra/hrnetv2_w40')), neck=dict(type='HRFPN', in_channels=[40, 80, 160, 320], out_channels=256)) diff --git a/configs/hrnet/faster_rcnn_hrnetv2p_w18_1x_coco.py b/configs/hrnet/faster_rcnn_hrnetv2p_w18_1x_coco.py index 9907bcb..1df2c3d 100644 --- a/configs/hrnet/faster_rcnn_hrnetv2p_w18_1x_coco.py +++ b/configs/hrnet/faster_rcnn_hrnetv2p_w18_1x_coco.py @@ -1,10 +1,11 @@ _base_ = './faster_rcnn_hrnetv2p_w32_1x_coco.py' # model settings model = dict( - pretrained='open-mmlab://msra/hrnetv2_w18', backbone=dict( extra=dict( stage2=dict(num_channels=(18, 36)), stage3=dict(num_channels=(18, 36, 72)), - stage4=dict(num_channels=(18, 36, 72, 144)))), + stage4=dict(num_channels=(18, 36, 72, 144))), + init_cfg=dict( + type='Pretrained', checkpoint='open-mmlab://msra/hrnetv2_w18')), neck=dict(type='HRFPN', in_channels=[18, 36, 72, 144], out_channels=256)) diff --git a/configs/hrnet/faster_rcnn_hrnetv2p_w32_1x_coco.py b/configs/hrnet/faster_rcnn_hrnetv2p_w32_1x_coco.py index 190e81c..be05809 100644 --- a/configs/hrnet/faster_rcnn_hrnetv2p_w32_1x_coco.py +++ b/configs/hrnet/faster_rcnn_hrnetv2p_w32_1x_coco.py @@ -1,6 +1,5 @@ _base_ = '../faster_rcnn/faster_rcnn_r50_fpn_1x_coco.py' model = dict( - pretrained='open-mmlab://msra/hrnetv2_w32', backbone=dict( _delete_=True, type='HRNet', @@ -28,7 +27,9 @@ num_branches=4, block='BASIC', num_blocks=(4, 4, 4, 4), - num_channels=(32, 64, 128, 256)))), + num_channels=(32, 64, 128, 256))), + init_cfg=dict( + type='Pretrained', checkpoint='open-mmlab://msra/hrnetv2_w32')), neck=dict( _delete_=True, type='HRFPN', diff --git a/configs/hrnet/faster_rcnn_hrnetv2p_w40_1x_coco.py b/configs/hrnet/faster_rcnn_hrnetv2p_w40_1x_coco.py index d0fd9fa..886a7c9 100644 --- a/configs/hrnet/faster_rcnn_hrnetv2p_w40_1x_coco.py +++ b/configs/hrnet/faster_rcnn_hrnetv2p_w40_1x_coco.py @@ -1,10 +1,11 @@ _base_ = './faster_rcnn_hrnetv2p_w32_1x_coco.py' model = dict( - pretrained='open-mmlab://msra/hrnetv2_w40', backbone=dict( type='HRNet', extra=dict( stage2=dict(num_channels=(40, 80)), stage3=dict(num_channels=(40, 80, 160)), - stage4=dict(num_channels=(40, 80, 160, 320)))), + stage4=dict(num_channels=(40, 80, 160, 320))), + init_cfg=dict( + type='Pretrained', checkpoint='open-mmlab://msra/hrnetv2_w40')), neck=dict(type='HRFPN', in_channels=[40, 80, 160, 320], out_channels=256)) diff --git a/configs/hrnet/fcos_hrnetv2p_w18_gn-head_4x4_1x_coco.py b/configs/hrnet/fcos_hrnetv2p_w18_gn-head_4x4_1x_coco.py index 20bffb9..fd662bd 100644 --- a/configs/hrnet/fcos_hrnetv2p_w18_gn-head_4x4_1x_coco.py +++ b/configs/hrnet/fcos_hrnetv2p_w18_gn-head_4x4_1x_coco.py @@ -1,9 +1,10 @@ _base_ = './fcos_hrnetv2p_w32_gn-head_4x4_1x_coco.py' model = dict( - pretrained='open-mmlab://msra/hrnetv2_w18', backbone=dict( extra=dict( stage2=dict(num_channels=(18, 36)), stage3=dict(num_channels=(18, 36, 72)), - stage4=dict(num_channels=(18, 36, 72, 144)))), + stage4=dict(num_channels=(18, 36, 72, 144))), + init_cfg=dict( + type='Pretrained', checkpoint='open-mmlab://msra/hrnetv2_w18')), neck=dict(type='HRFPN', in_channels=[18, 36, 72, 144], out_channels=256)) diff --git a/configs/hrnet/fcos_hrnetv2p_w18_gn-head_mstrain_640-800_4x4_2x_coco.py b/configs/hrnet/fcos_hrnetv2p_w18_gn-head_mstrain_640-800_4x4_2x_coco.py index b845128..37bfdae 100644 --- a/configs/hrnet/fcos_hrnetv2p_w18_gn-head_mstrain_640-800_4x4_2x_coco.py +++ b/configs/hrnet/fcos_hrnetv2p_w18_gn-head_mstrain_640-800_4x4_2x_coco.py @@ -1,9 +1,10 @@ _base_ = './fcos_hrnetv2p_w32_gn-head_mstrain_640-800_4x4_2x_coco.py' model = dict( - pretrained='open-mmlab://msra/hrnetv2_w18', backbone=dict( extra=dict( stage2=dict(num_channels=(18, 36)), stage3=dict(num_channels=(18, 36, 72)), - stage4=dict(num_channels=(18, 36, 72, 144)))), + stage4=dict(num_channels=(18, 36, 72, 144))), + init_cfg=dict( + type='Pretrained', checkpoint='open-mmlab://msra/hrnetv2_w18')), neck=dict(type='HRFPN', in_channels=[18, 36, 72, 144], out_channels=256)) diff --git a/configs/hrnet/fcos_hrnetv2p_w32_gn-head_4x4_1x_coco.py b/configs/hrnet/fcos_hrnetv2p_w32_gn-head_4x4_1x_coco.py index 98f1cb7..10617f2 100644 --- a/configs/hrnet/fcos_hrnetv2p_w32_gn-head_4x4_1x_coco.py +++ b/configs/hrnet/fcos_hrnetv2p_w32_gn-head_4x4_1x_coco.py @@ -1,6 +1,5 @@ _base_ = '../fcos/fcos_r50_caffe_fpn_gn-head_4x4_1x_coco.py' model = dict( - pretrained='open-mmlab://msra/hrnetv2_w32', backbone=dict( _delete_=True, type='HRNet', @@ -28,7 +27,9 @@ num_branches=4, block='BASIC', num_blocks=(4, 4, 4, 4), - num_channels=(32, 64, 128, 256)))), + num_channels=(32, 64, 128, 256))), + init_cfg=dict( + type='Pretrained', checkpoint='open-mmlab://msra/hrnetv2_w32')), neck=dict( _delete_=True, type='HRFPN', diff --git a/configs/hrnet/fcos_hrnetv2p_w40_gn-head_mstrain_640-800_4x4_2x_coco.py b/configs/hrnet/fcos_hrnetv2p_w40_gn-head_mstrain_640-800_4x4_2x_coco.py index 452b0fe..0ae9dbe 100644 --- a/configs/hrnet/fcos_hrnetv2p_w40_gn-head_mstrain_640-800_4x4_2x_coco.py +++ b/configs/hrnet/fcos_hrnetv2p_w40_gn-head_mstrain_640-800_4x4_2x_coco.py @@ -1,10 +1,11 @@ _base_ = './fcos_hrnetv2p_w32_gn-head_mstrain_640-800_4x4_2x_coco.py' model = dict( - pretrained='open-mmlab://msra/hrnetv2_w40', backbone=dict( type='HRNet', extra=dict( stage2=dict(num_channels=(40, 80)), stage3=dict(num_channels=(40, 80, 160)), - stage4=dict(num_channels=(40, 80, 160, 320)))), + stage4=dict(num_channels=(40, 80, 160, 320))), + init_cfg=dict( + type='Pretrained', checkpoint='open-mmlab://msra/hrnetv2_w40')), neck=dict(type='HRFPN', in_channels=[40, 80, 160, 320], out_channels=256)) diff --git a/configs/hrnet/htc_hrnetv2p_w18_20e_coco.py b/configs/hrnet/htc_hrnetv2p_w18_20e_coco.py index 391636f..3c2eb1d 100644 --- a/configs/hrnet/htc_hrnetv2p_w18_20e_coco.py +++ b/configs/hrnet/htc_hrnetv2p_w18_20e_coco.py @@ -1,9 +1,10 @@ _base_ = './htc_hrnetv2p_w32_20e_coco.py' model = dict( - pretrained='open-mmlab://msra/hrnetv2_w18', backbone=dict( extra=dict( stage2=dict(num_channels=(18, 36)), stage3=dict(num_channels=(18, 36, 72)), - stage4=dict(num_channels=(18, 36, 72, 144)))), + stage4=dict(num_channels=(18, 36, 72, 144))), + init_cfg=dict( + type='Pretrained', checkpoint='open-mmlab://msra/hrnetv2_w18')), neck=dict(type='HRFPN', in_channels=[18, 36, 72, 144], out_channels=256)) diff --git a/configs/hrnet/htc_hrnetv2p_w32_20e_coco.py b/configs/hrnet/htc_hrnetv2p_w32_20e_coco.py index aee7808..545cb83 100644 --- a/configs/hrnet/htc_hrnetv2p_w32_20e_coco.py +++ b/configs/hrnet/htc_hrnetv2p_w32_20e_coco.py @@ -1,6 +1,5 @@ _base_ = '../htc/htc_r50_fpn_20e_coco.py' model = dict( - pretrained='open-mmlab://msra/hrnetv2_w32', backbone=dict( _delete_=True, type='HRNet', @@ -28,7 +27,9 @@ num_branches=4, block='BASIC', num_blocks=(4, 4, 4, 4), - num_channels=(32, 64, 128, 256)))), + num_channels=(32, 64, 128, 256))), + init_cfg=dict( + type='Pretrained', checkpoint='open-mmlab://msra/hrnetv2_w32')), neck=dict( _delete_=True, type='HRFPN', diff --git a/configs/hrnet/htc_hrnetv2p_w40_20e_coco.py b/configs/hrnet/htc_hrnetv2p_w40_20e_coco.py index abf6fb5..94bff1b 100644 --- a/configs/hrnet/htc_hrnetv2p_w40_20e_coco.py +++ b/configs/hrnet/htc_hrnetv2p_w40_20e_coco.py @@ -1,10 +1,11 @@ _base_ = './htc_hrnetv2p_w32_20e_coco.py' model = dict( - pretrained='open-mmlab://msra/hrnetv2_w40', backbone=dict( type='HRNet', extra=dict( stage2=dict(num_channels=(40, 80)), stage3=dict(num_channels=(40, 80, 160)), - stage4=dict(num_channels=(40, 80, 160, 320)))), + stage4=dict(num_channels=(40, 80, 160, 320))), + init_cfg=dict( + type='Pretrained', checkpoint='open-mmlab://msra/hrnetv2_w40')), neck=dict(type='HRFPN', in_channels=[40, 80, 160, 320], out_channels=256)) diff --git a/configs/hrnet/mask_rcnn_hrnetv2p_w18_1x_coco.py b/configs/hrnet/mask_rcnn_hrnetv2p_w18_1x_coco.py index 82a5f46..cb12200 100644 --- a/configs/hrnet/mask_rcnn_hrnetv2p_w18_1x_coco.py +++ b/configs/hrnet/mask_rcnn_hrnetv2p_w18_1x_coco.py @@ -1,9 +1,10 @@ _base_ = './mask_rcnn_hrnetv2p_w32_1x_coco.py' model = dict( - pretrained='open-mmlab://msra/hrnetv2_w18', backbone=dict( extra=dict( stage2=dict(num_channels=(18, 36)), stage3=dict(num_channels=(18, 36, 72)), - stage4=dict(num_channels=(18, 36, 72, 144)))), + stage4=dict(num_channels=(18, 36, 72, 144))), + init_cfg=dict( + type='Pretrained', checkpoint='open-mmlab://msra/hrnetv2_w18')), neck=dict(type='HRFPN', in_channels=[18, 36, 72, 144], out_channels=256)) diff --git a/configs/hrnet/mask_rcnn_hrnetv2p_w32_1x_coco.py b/configs/hrnet/mask_rcnn_hrnetv2p_w32_1x_coco.py index f533af6..d5f0eb5 100644 --- a/configs/hrnet/mask_rcnn_hrnetv2p_w32_1x_coco.py +++ b/configs/hrnet/mask_rcnn_hrnetv2p_w32_1x_coco.py @@ -1,6 +1,5 @@ _base_ = '../mask_rcnn/mask_rcnn_r50_fpn_1x_coco.py' model = dict( - pretrained='open-mmlab://msra/hrnetv2_w32', backbone=dict( _delete_=True, type='HRNet', @@ -28,7 +27,9 @@ num_branches=4, block='BASIC', num_blocks=(4, 4, 4, 4), - num_channels=(32, 64, 128, 256)))), + num_channels=(32, 64, 128, 256))), + init_cfg=dict( + type='Pretrained', checkpoint='open-mmlab://msra/hrnetv2_w32')), neck=dict( _delete_=True, type='HRFPN', diff --git a/configs/hrnet/mask_rcnn_hrnetv2p_w40_1x_coco.py b/configs/hrnet/mask_rcnn_hrnetv2p_w40_1x_coco.py index 5b10c16..5a76f4b 100644 --- a/configs/hrnet/mask_rcnn_hrnetv2p_w40_1x_coco.py +++ b/configs/hrnet/mask_rcnn_hrnetv2p_w40_1x_coco.py @@ -1,10 +1,11 @@ _base_ = './mask_rcnn_hrnetv2p_w18_1x_coco.py' model = dict( - pretrained='open-mmlab://msra/hrnetv2_w40', backbone=dict( type='HRNet', extra=dict( stage2=dict(num_channels=(40, 80)), stage3=dict(num_channels=(40, 80, 160)), - stage4=dict(num_channels=(40, 80, 160, 320)))), + stage4=dict(num_channels=(40, 80, 160, 320))), + init_cfg=dict( + type='Pretrained', checkpoint='open-mmlab://msra/hrnetv2_w40')), neck=dict(type='HRFPN', in_channels=[40, 80, 160, 320], out_channels=256)) diff --git a/configs/hrnet/metafile.yml b/configs/hrnet/metafile.yml new file mode 100644 index 0000000..37703aa --- /dev/null +++ b/configs/hrnet/metafile.yml @@ -0,0 +1,604 @@ +Collections: + - Name: HRNet + Metadata: + Training Data: COCO + Training Techniques: + - SGD with Momentum + - Weight Decay + Training Resources: 8x V100 GPUs + Architecture: + - HRNet + Paper: + URL: https://arxiv.org/abs/1904.04514 + Title: 'Deep High-Resolution Representation Learning for Visual Recognition' + README: configs/hrnet/README.md + Code: + URL: https://github.com/open-mmlab/mmdetection/blob/v2.0.0/mmdet/models/backbones/hrnet.py#L195 + Version: v2.0.0 + +Models: + - Name: faster_rcnn_hrnetv2p_w18_1x_coco + In Collection: HRNet + Config: configs/hrnet/faster_rcnn_hrnetv2p_w18_1x_coco.py + Metadata: + Training Memory (GB): 6.6 + inference time (ms/im): + - value: 74.63 + hardware: V100 + backend: PyTorch + batch size: 1 + mode: FP32 + resolution: (800, 1333) + Epochs: 12 + Results: + - Task: Object Detection + Dataset: COCO + Metrics: + box AP: 36.9 + Weights: https://download.openmmlab.com/mmdetection/v2.0/hrnet/faster_rcnn_hrnetv2p_w18_1x_coco/faster_rcnn_hrnetv2p_w18_1x_coco_20200130-56651a6d.pth + + - Name: faster_rcnn_hrnetv2p_w18_2x_coco + In Collection: HRNet + Config: configs/hrnet/faster_rcnn_hrnetv2p_w18_2x_coco.py + Metadata: + Training Memory (GB): 6.6 + inference time (ms/im): + - value: 74.63 + hardware: V100 + backend: PyTorch + batch size: 1 + mode: FP32 + resolution: (800, 1333) + Epochs: 24 + Results: + - Task: Object Detection + Dataset: COCO + Metrics: + box AP: 38.9 + Weights: https://download.openmmlab.com/mmdetection/v2.0/hrnet/faster_rcnn_hrnetv2p_w18_2x_coco/faster_rcnn_hrnetv2p_w18_2x_coco_20200702_085731-a4ec0611.pth + + - Name: faster_rcnn_hrnetv2p_w32_1x_coco + In Collection: HRNet + Config: configs/hrnet/faster_rcnn_hrnetv2p_w32_1x_coco.py + Metadata: + Training Memory (GB): 9.0 + inference time (ms/im): + - value: 80.65 + hardware: V100 + backend: PyTorch + batch size: 1 + mode: FP32 + resolution: (800, 1333) + Epochs: 12 + Results: + - Task: Object Detection + Dataset: COCO + Metrics: + box AP: 40.2 + Weights: https://download.openmmlab.com/mmdetection/v2.0/hrnet/faster_rcnn_hrnetv2p_w32_1x_coco/faster_rcnn_hrnetv2p_w32_1x_coco_20200130-6e286425.pth + + - Name: faster_rcnn_hrnetv2p_w32_2x_coco + In Collection: HRNet + Config: configs/hrnet/faster_rcnn_hrnetv2p_w32_2x_coco.py + Metadata: + Training Memory (GB): 9.0 + inference time (ms/im): + - value: 80.65 + hardware: V100 + backend: PyTorch + batch size: 1 + mode: FP32 + resolution: (800, 1333) + Epochs: 24 + Results: + - Task: Object Detection + Dataset: COCO + Metrics: + box AP: 41.4 + Weights: https://download.openmmlab.com/mmdetection/v2.0/hrnet/faster_rcnn_hrnetv2p_w32_2x_coco/faster_rcnn_hrnetv2p_w32_2x_coco_20200529_015927-976a9c15.pth + + - Name: faster_rcnn_hrnetv2p_w40_1x_coco + In Collection: HRNet + Config: configs/hrnet/faster_rcnn_hrnetv2p_w40_1x_coco.py + Metadata: + Training Memory (GB): 10.4 + inference time (ms/im): + - value: 95.24 + hardware: V100 + backend: PyTorch + batch size: 1 + mode: FP32 + resolution: (800, 1333) + Epochs: 12 + Results: + - Task: Object Detection + Dataset: COCO + Metrics: + box AP: 41.2 + Weights: https://download.openmmlab.com/mmdetection/v2.0/hrnet/faster_rcnn_hrnetv2p_w40_1x_coco/faster_rcnn_hrnetv2p_w40_1x_coco_20200210-95c1f5ce.pth + + - Name: faster_rcnn_hrnetv2p_w40_2x_coco + In Collection: HRNet + Config: configs/hrnet/faster_rcnn_hrnetv2p_w40_2x_coco.py + Metadata: + Training Memory (GB): 10.4 + inference time (ms/im): + - value: 95.24 + hardware: V100 + backend: PyTorch + batch size: 1 + mode: FP32 + resolution: (800, 1333) + Epochs: 24 + Results: + - Task: Object Detection + Dataset: COCO + Metrics: + box AP: 42.1 + Weights: https://download.openmmlab.com/mmdetection/v2.0/hrnet/faster_rcnn_hrnetv2p_w40_2x_coco/faster_rcnn_hrnetv2p_w40_2x_coco_20200512_161033-0f236ef4.pth + + - Name: mask_rcnn_hrnetv2p_w18_1x_coco + In Collection: HRNet + Config: configs/hrnet/mask_rcnn_hrnetv2p_w18_1x_coco.py + Metadata: + Training Memory (GB): 7.0 + inference time (ms/im): + - value: 85.47 + hardware: V100 + backend: PyTorch + batch size: 1 + mode: FP32 + resolution: (800, 1333) + Epochs: 12 + Results: + - Task: Object Detection + Dataset: COCO + Metrics: + box AP: 37.7 + - Task: Instance Segmentation + Dataset: COCO + Metrics: + mask AP: 34.2 + Weights: https://download.openmmlab.com/mmdetection/v2.0/hrnet/mask_rcnn_hrnetv2p_w18_1x_coco/mask_rcnn_hrnetv2p_w18_1x_coco_20200205-1c3d78ed.pth + + - Name: mask_rcnn_hrnetv2p_w18_2x_coco + In Collection: HRNet + Config: configs/hrnet/mask_rcnn_hrnetv2p_w18_2x_coco.py + Metadata: + Training Memory (GB): 7.0 + inference time (ms/im): + - value: 85.47 + hardware: V100 + backend: PyTorch + batch size: 1 + mode: FP32 + resolution: (800, 1333) + Epochs: 24 + Results: + - Task: Object Detection + Dataset: COCO + Metrics: + box AP: 39.8 + - Task: Instance Segmentation + Dataset: COCO + Metrics: + mask AP: 36.0 + Weights: https://download.openmmlab.com/mmdetection/v2.0/hrnet/mask_rcnn_hrnetv2p_w18_2x_coco/mask_rcnn_hrnetv2p_w18_2x_coco_20200212-b3c825b1.pth + + - Name: mask_rcnn_hrnetv2p_w32_1x_coco + In Collection: HRNet + Config: configs/hrnet/mask_rcnn_hrnetv2p_w32_1x_coco.py + Metadata: + Training Memory (GB): 9.4 + inference time (ms/im): + - value: 88.5 + hardware: V100 + backend: PyTorch + batch size: 1 + mode: FP32 + resolution: (800, 1333) + Epochs: 12 + Results: + - Task: Object Detection + Dataset: COCO + Metrics: + box AP: 41.2 + - Task: Instance Segmentation + Dataset: COCO + Metrics: + mask AP: 37.1 + Weights: https://download.openmmlab.com/mmdetection/v2.0/hrnet/mask_rcnn_hrnetv2p_w32_1x_coco/mask_rcnn_hrnetv2p_w32_1x_coco_20200207-b29f616e.pth + + - Name: mask_rcnn_hrnetv2p_w32_2x_coco + In Collection: HRNet + Config: configs/hrnet/mask_rcnn_hrnetv2p_w32_2x_coco.py + Metadata: + Training Memory (GB): 9.4 + inference time (ms/im): + - value: 88.5 + hardware: V100 + backend: PyTorch + batch size: 1 + mode: FP32 + resolution: (800, 1333) + Epochs: 24 + Results: + - Task: Object Detection + Dataset: COCO + Metrics: + box AP: 42.5 + - Task: Instance Segmentation + Dataset: COCO + Metrics: + mask AP: 37.8 + Weights: https://download.openmmlab.com/mmdetection/v2.0/hrnet/mask_rcnn_hrnetv2p_w32_2x_coco/mask_rcnn_hrnetv2p_w32_2x_coco_20200213-45b75b4d.pth + + - Name: mask_rcnn_hrnetv2p_w40_1x_coco + In Collection: HRNet + Config: configs/hrnet/mask_rcnn_hrnetv2p_w40_1x_coco.py + Metadata: + Training Memory (GB): 10.9 + Epochs: 12 + Results: + - Task: Object Detection + Dataset: COCO + Metrics: + box AP: 42.1 + - Task: Instance Segmentation + Dataset: COCO + Metrics: + mask AP: 37.5 + Weights: https://download.openmmlab.com/mmdetection/v2.0/hrnet/mask_rcnn_hrnetv2p_w40_1x_coco/mask_rcnn_hrnetv2p_w40_1x_coco_20200511_015646-66738b35.pth + + - Name: mask_rcnn_hrnetv2p_w40_2x_coco + In Collection: HRNet + Config: configs/hrnet/mask_rcnn_hrnetv2p_w40_2x_coco.py + Metadata: + Training Memory (GB): 10.9 + Epochs: 24 + Results: + - Task: Object Detection + Dataset: COCO + Metrics: + box AP: 42.8 + - Task: Instance Segmentation + Dataset: COCO + Metrics: + mask AP: 38.2 + Weights: https://download.openmmlab.com/mmdetection/v2.0/hrnet/mask_rcnn_hrnetv2p_w40_2x_coco/mask_rcnn_hrnetv2p_w40_2x_coco_20200512_163732-aed5e4ab.pth + + - Name: cascade_rcnn_hrnetv2p_w18_20e_coco + In Collection: HRNet + Config: configs/hrnet/cascade_rcnn_hrnetv2p_w18_20e_coco.py + Metadata: + Training Memory (GB): 7.0 + inference time (ms/im): + - value: 90.91 + hardware: V100 + backend: PyTorch + batch size: 1 + mode: FP32 + resolution: (800, 1333) + Epochs: 20 + Results: + - Task: Object Detection + Dataset: COCO + Metrics: + box AP: 41.2 + Weights: https://download.openmmlab.com/mmdetection/v2.0/hrnet/cascade_rcnn_hrnetv2p_w18_20e_coco/cascade_rcnn_hrnetv2p_w18_20e_coco_20200210-434be9d7.pth + + - Name: cascade_rcnn_hrnetv2p_w32_20e_coco + In Collection: HRNet + Config: configs/hrnet/cascade_rcnn_hrnetv2p_w32_20e_coco.py + Metadata: + Training Memory (GB): 9.4 + inference time (ms/im): + - value: 90.91 + hardware: V100 + backend: PyTorch + batch size: 1 + mode: FP32 + resolution: (800, 1333) + Epochs: 20 + Results: + - Task: Object Detection + Dataset: COCO + Metrics: + box AP: 43.3 + Weights: https://download.openmmlab.com/mmdetection/v2.0/hrnet/cascade_rcnn_hrnetv2p_w32_20e_coco/cascade_rcnn_hrnetv2p_w32_20e_coco_20200208-928455a4.pth + + - Name: cascade_rcnn_hrnetv2p_w40_20e_coco + In Collection: HRNet + Config: configs/hrnet/cascade_rcnn_hrnetv2p_w40_20e_coco.py + Metadata: + Training Memory (GB): 10.8 + Epochs: 20 + Results: + - Task: Object Detection + Dataset: COCO + Metrics: + box AP: 43.8 + Weights: https://download.openmmlab.com/mmdetection/v2.0/hrnet/cascade_rcnn_hrnetv2p_w40_20e_coco/cascade_rcnn_hrnetv2p_w40_20e_coco_20200512_161112-75e47b04.pth + + - Name: cascade_mask_rcnn_hrnetv2p_w18_20e_coco + In Collection: HRNet + Config: configs/hrnet/cascade_mask_rcnn_hrnetv2p_w18_20e_coco.py + Metadata: + Training Memory (GB): 8.5 + inference time (ms/im): + - value: 117.65 + hardware: V100 + backend: PyTorch + batch size: 1 + mode: FP32 + resolution: (800, 1333) + Epochs: 20 + Results: + - Task: Object Detection + Dataset: COCO + Metrics: + box AP: 41.6 + - Task: Instance Segmentation + Dataset: COCO + Metrics: + mask AP: 36.4 + Weights: https://download.openmmlab.com/mmdetection/v2.0/hrnet/cascade_mask_rcnn_hrnetv2p_w18_20e_coco/cascade_mask_rcnn_hrnetv2p_w18_20e_coco_20200210-b543cd2b.pth + + - Name: cascade_mask_rcnn_hrnetv2p_w32_20e_coco + In Collection: HRNet + Config: configs/hrnet/cascade_mask_rcnn_hrnetv2p_w32_20e_coco.py + Metadata: + inference time (ms/im): + - value: 120.48 + hardware: V100 + backend: PyTorch + batch size: 1 + mode: FP32 + resolution: (800, 1333) + Epochs: 20 + Results: + - Task: Object Detection + Dataset: COCO + Metrics: + box AP: 44.3 + - Task: Instance Segmentation + Dataset: COCO + Metrics: + mask AP: 38.6 + Weights: https://download.openmmlab.com/mmdetection/v2.0/hrnet/cascade_mask_rcnn_hrnetv2p_w32_20e_coco/cascade_mask_rcnn_hrnetv2p_w32_20e_coco_20200512_154043-39d9cf7b.pth + + - Name: cascade_mask_rcnn_hrnetv2p_w40_20e_coco + In Collection: HRNet + Config: configs/hrnet/cascade_mask_rcnn_hrnetv2p_w40_20e_coco.py + Metadata: + Training Memory (GB): 12.5 + Epochs: 20 + Results: + - Task: Object Detection + Dataset: COCO + Metrics: + box AP: 45.1 + - Task: Instance Segmentation + Dataset: COCO + Metrics: + mask AP: 39.3 + Weights: https://download.openmmlab.com/mmdetection/v2.0/hrnet/cascade_mask_rcnn_hrnetv2p_w40_20e_coco/cascade_mask_rcnn_hrnetv2p_w40_20e_coco_20200527_204922-969c4610.pth + + - Name: htc_hrnetv2p_w18_20e_coco + In Collection: HRNet + Config: configs/hrnet/htc_hrnetv2p_w18_20e_coco.py + Metadata: + Training Memory (GB): 10.8 + inference time (ms/im): + - value: 212.77 + hardware: V100 + backend: PyTorch + batch size: 1 + mode: FP32 + resolution: (800, 1333) + Epochs: 20 + Results: + - Task: Object Detection + Dataset: COCO + Metrics: + box AP: 42.8 + - Task: Instance Segmentation + Dataset: COCO + Metrics: + mask AP: 37.9 + Weights: https://download.openmmlab.com/mmdetection/v2.0/hrnet/htc_hrnetv2p_w18_20e_coco/htc_hrnetv2p_w18_20e_coco_20200210-b266988c.pth + + - Name: htc_hrnetv2p_w32_20e_coco + In Collection: HRNet + Config: configs/hrnet/htc_hrnetv2p_w32_20e_coco.py + Metadata: + Training Memory (GB): 13.1 + inference time (ms/im): + - value: 204.08 + hardware: V100 + backend: PyTorch + batch size: 1 + mode: FP32 + resolution: (800, 1333) + Epochs: 20 + Results: + - Task: Object Detection + Dataset: COCO + Metrics: + box AP: 45.4 + - Task: Instance Segmentation + Dataset: COCO + Metrics: + mask AP: 39.9 + Weights: https://download.openmmlab.com/mmdetection/v2.0/hrnet/htc_hrnetv2p_w32_20e_coco/htc_hrnetv2p_w32_20e_coco_20200207-7639fa12.pth + + - Name: htc_hrnetv2p_w40_20e_coco + In Collection: HRNet + Config: configs/hrnet/htc_hrnetv2p_w40_20e_coco.py + Metadata: + Training Memory (GB): 14.6 + Epochs: 20 + Results: + - Task: Object Detection + Dataset: COCO + Metrics: + box AP: 46.4 + - Task: Instance Segmentation + Dataset: COCO + Metrics: + mask AP: 40.8 + Weights: https://download.openmmlab.com/mmdetection/v2.0/hrnet/htc_hrnetv2p_w40_20e_coco/htc_hrnetv2p_w40_20e_coco_20200529_183411-417c4d5b.pth + + - Name: fcos_hrnetv2p_w18_gn-head_4x4_1x_coco + In Collection: HRNet + Config: configs/hrnet/fcos_hrnetv2p_w18_gn-head_4x4_1x_coco.py + Metadata: + Training Resources: 4x V100 GPUs + Batch Size: 16 + Training Memory (GB): 13.0 + inference time (ms/im): + - value: 77.52 + hardware: V100 + backend: PyTorch + batch size: 1 + mode: FP32 + resolution: (800, 1333) + Epochs: 12 + Results: + - Task: Object Detection + Dataset: COCO + Metrics: + box AP: 35.3 + Weights: https://download.openmmlab.com/mmdetection/v2.0/hrnet/fcos_hrnetv2p_w18_gn-head_4x4_1x_coco/fcos_hrnetv2p_w18_gn-head_4x4_1x_coco_20201212_100710-4ad151de.pth + + - Name: fcos_hrnetv2p_w18_gn-head_4x4_2x_coco + In Collection: HRNet + Config: configs/hrnet/fcos_hrnetv2p_w18_gn-head_4x4_2x_coco.py + Metadata: + Training Resources: 4x V100 GPUs + Batch Size: 16 + Training Memory (GB): 13.0 + inference time (ms/im): + - value: 77.52 + hardware: V100 + backend: PyTorch + batch size: 1 + mode: FP32 + resolution: (800, 1333) + Epochs: 24 + Results: + - Task: Object Detection + Dataset: COCO + Metrics: + box AP: 38.2 + Weights: https://download.openmmlab.com/mmdetection/v2.0/hrnet/fcos_hrnetv2p_w18_gn-head_4x4_2x_coco/fcos_hrnetv2p_w18_gn-head_4x4_2x_coco_20201212_101110-5c575fa5.pth + + - Name: fcos_hrnetv2p_w32_gn-head_4x4_1x_coco + In Collection: HRNet + Config: configs/hrnet/fcos_hrnetv2p_w32_gn-head_4x4_1x_coco.py + Metadata: + Training Resources: 4x V100 GPUs + Batch Size: 16 + Training Memory (GB): 17.5 + inference time (ms/im): + - value: 77.52 + hardware: V100 + backend: PyTorch + batch size: 1 + mode: FP32 + resolution: (800, 1333) + Epochs: 12 + Results: + - Task: Object Detection + Dataset: COCO + Metrics: + box AP: 39.5 + Weights: https://download.openmmlab.com/mmdetection/v2.0/hrnet/fcos_hrnetv2p_w32_gn-head_4x4_1x_coco/fcos_hrnetv2p_w32_gn-head_4x4_1x_coco_20201211_134730-cb8055c0.pth + + - Name: fcos_hrnetv2p_w32_gn-head_4x4_2x_coco + In Collection: HRNet + Config: configs/hrnet/fcos_hrnetv2p_w32_gn-head_4x4_2x_coco.py + Metadata: + Training Resources: 4x V100 GPUs + Batch Size: 16 + Training Memory (GB): 17.5 + inference time (ms/im): + - value: 77.52 + hardware: V100 + backend: PyTorch + batch size: 1 + mode: FP32 + resolution: (800, 1333) + Epochs: 24 + Results: + - Task: Object Detection + Dataset: COCO + Metrics: + box AP: 40.8 + Weights: https://download.openmmlab.com/mmdetection/v2.0/hrnet/fcos_hrnetv2p_w32_gn-head_4x4_2x_coco/fcos_hrnetv2p_w32_gn-head_4x4_2x_coco_20201212_112133-77b6b9bb.pth + + - Name: fcos_hrnetv2p_w18_gn-head_mstrain_640-800_4x4_2x_coco + In Collection: HRNet + Config: configs/hrnet/fcos_hrnetv2p_w18_gn-head_mstrain_640-800_4x4_2x_coco.py + Metadata: + Training Resources: 4x V100 GPUs + Batch Size: 16 + Training Memory (GB): 13.0 + inference time (ms/im): + - value: 77.52 + hardware: V100 + backend: PyTorch + batch size: 1 + mode: FP32 + resolution: (800, 1333) + Epochs: 24 + Results: + - Task: Object Detection + Dataset: COCO + Metrics: + box AP: 38.3 + Weights: https://download.openmmlab.com/mmdetection/v2.0/hrnet/fcos_hrnetv2p_w18_gn-head_mstrain_640-800_4x4_2x_coco/fcos_hrnetv2p_w18_gn-head_mstrain_640-800_4x4_2x_coco_20201212_111651-441e9d9f.pth + + - Name: fcos_hrnetv2p_w32_gn-head_mstrain_640-800_4x4_2x_coco + In Collection: HRNet + Config: configs/hrnet/fcos_hrnetv2p_w32_gn-head_mstrain_640-800_4x4_2x_coco.py + Metadata: + Training Resources: 4x V100 GPUs + Batch Size: 16 + Training Memory (GB): 17.5 + inference time (ms/im): + - value: 80.65 + hardware: V100 + backend: PyTorch + batch size: 1 + mode: FP32 + resolution: (800, 1333) + Epochs: 24 + Results: + - Task: Object Detection + Dataset: COCO + Metrics: + box AP: 41.9 + Weights: https://download.openmmlab.com/mmdetection/v2.0/hrnet/fcos_hrnetv2p_w32_gn-head_mstrain_640-800_4x4_2x_coco/fcos_hrnetv2p_w32_gn-head_mstrain_640-800_4x4_2x_coco_20201212_090846-b6f2b49f.pth + + - Name: fcos_hrnetv2p_w40_gn-head_mstrain_640-800_4x4_2x_coco + In Collection: HRNet + Config: configs/hrnet/fcos_hrnetv2p_w40_gn-head_mstrain_640-800_4x4_2x_coco.py + Metadata: + Training Resources: 4x V100 GPUs + Batch Size: 16 + Training Memory (GB): 20.3 + inference time (ms/im): + - value: 92.59 + hardware: V100 + backend: PyTorch + batch size: 1 + mode: FP32 + resolution: (800, 1333) + Epochs: 24 + Results: + - Task: Object Detection + Dataset: COCO + Metrics: + box AP: 42.7 + Weights: https://download.openmmlab.com/mmdetection/v2.0/hrnet/fcos_hrnetv2p_w40_gn-head_mstrain_640-800_4x4_2x_coco/fcos_hrnetv2p_w40_gn-head_mstrain_640-800_4x4_2x_coco_20201212_124752-f22d2ce5.pth diff --git a/configs/htc/README.md b/configs/htc/README.md index 6af02da..1625827 100644 --- a/configs/htc/README.md +++ b/configs/htc/README.md @@ -2,7 +2,7 @@ ## Introduction -[ALGORITHM] + We provide config files to reproduce the results in the CVPR 2019 paper for [Hybrid Task Cascade](https://arxiv.org/abs/1901.07518). @@ -40,11 +40,11 @@ The results on COCO 2017val are shown in the below table. (results on test-dev a | Backbone | Style | Lr schd | Mem (GB) | Inf time (fps) | box AP | mask AP | Config | Download | |:---------:|:-------:|:-------:|:--------:|:--------------:|:------:|:-------:|:------:|:--------:| -| R-50-FPN | pytorch | 1x | 8.2 | 5.8 | 42.3 | 37.4 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/htc/htc_r50_fpn_1x_coco.py) | [model](http://download.openmmlab.com/mmdetection/v2.0/htc/htc_r50_fpn_1x_coco/htc_r50_fpn_1x_coco_20200317-7332cf16.pth) | [log](http://download.openmmlab.com/mmdetection/v2.0/htc/htc_r50_fpn_1x_coco/htc_r50_fpn_1x_coco_20200317_070435.log.json) | -| R-50-FPN | pytorch | 20e | 8.2 | - | 43.3 | 38.3 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/htc/htc_r50_fpn_20e_coco.py) | [model](http://download.openmmlab.com/mmdetection/v2.0/htc/htc_r50_fpn_20e_coco/htc_r50_fpn_20e_coco_20200319-fe28c577.pth) | [log](http://download.openmmlab.com/mmdetection/v2.0/htc/htc_r50_fpn_20e_coco/htc_r50_fpn_20e_coco_20200319_070313.log.json) | -| R-101-FPN | pytorch | 20e | 10.2 | 5.5 | 44.8 | 39.6 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/htc/htc_r101_fpn_20e_coco.py) | [model](http://download.openmmlab.com/mmdetection/v2.0/htc/htc_r101_fpn_20e_coco/htc_r101_fpn_20e_coco_20200317-9b41b48f.pth) | [log](http://download.openmmlab.com/mmdetection/v2.0/htc/htc_r101_fpn_20e_coco/htc_r101_fpn_20e_coco_20200317_153107.log.json) | -| X-101-32x4d-FPN | pytorch |20e| 11.4 | 5.0 | 46.1 | 40.5 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/htc/htc_x101_32x4d_fpn_16x1_20e_coco.py) | [model](http://download.openmmlab.com/mmdetection/v2.0/htc/htc_x101_32x4d_fpn_16x1_20e_coco/htc_x101_32x4d_fpn_16x1_20e_coco_20200318-de97ae01.pth) | [log](http://download.openmmlab.com/mmdetection/v2.0/htc/htc_x101_32x4d_fpn_16x1_20e_coco/htc_x101_32x4d_fpn_16x1_20e_coco_20200318_034519.log.json) | -| X-101-64x4d-FPN | pytorch |20e| 14.5 | 4.4 | 47.0 | 41.4 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/htc/htc_x101_64x4d_fpn_16x1_20e_coco.py) | [model](http://download.openmmlab.com/mmdetection/v2.0/htc/htc_x101_64x4d_fpn_16x1_20e_coco/htc_x101_64x4d_fpn_16x1_20e_coco_20200318-b181fd7a.pth) | [log](http://download.openmmlab.com/mmdetection/v2.0/htc/htc_x101_64x4d_fpn_16x1_20e_coco/htc_x101_64x4d_fpn_16x1_20e_coco_20200318_081711.log.json) | +| R-50-FPN | pytorch | 1x | 8.2 | 5.8 | 42.3 | 37.4 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/htc/htc_r50_fpn_1x_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/htc/htc_r50_fpn_1x_coco/htc_r50_fpn_1x_coco_20200317-7332cf16.pth) | [log](https://download.openmmlab.com/mmdetection/v2.0/htc/htc_r50_fpn_1x_coco/htc_r50_fpn_1x_coco_20200317_070435.log.json) | +| R-50-FPN | pytorch | 20e | 8.2 | - | 43.3 | 38.3 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/htc/htc_r50_fpn_20e_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/htc/htc_r50_fpn_20e_coco/htc_r50_fpn_20e_coco_20200319-fe28c577.pth) | [log](https://download.openmmlab.com/mmdetection/v2.0/htc/htc_r50_fpn_20e_coco/htc_r50_fpn_20e_coco_20200319_070313.log.json) | +| R-101-FPN | pytorch | 20e | 10.2 | 5.5 | 44.8 | 39.6 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/htc/htc_r101_fpn_20e_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/htc/htc_r101_fpn_20e_coco/htc_r101_fpn_20e_coco_20200317-9b41b48f.pth) | [log](https://download.openmmlab.com/mmdetection/v2.0/htc/htc_r101_fpn_20e_coco/htc_r101_fpn_20e_coco_20200317_153107.log.json) | +| X-101-32x4d-FPN | pytorch |20e| 11.4 | 5.0 | 46.1 | 40.5 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/htc/htc_x101_32x4d_fpn_16x1_20e_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/htc/htc_x101_32x4d_fpn_16x1_20e_coco/htc_x101_32x4d_fpn_16x1_20e_coco_20200318-de97ae01.pth) | [log](https://download.openmmlab.com/mmdetection/v2.0/htc/htc_x101_32x4d_fpn_16x1_20e_coco/htc_x101_32x4d_fpn_16x1_20e_coco_20200318_034519.log.json) | +| X-101-64x4d-FPN | pytorch |20e| 14.5 | 4.4 | 47.0 | 41.4 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/htc/htc_x101_64x4d_fpn_16x1_20e_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/htc/htc_x101_64x4d_fpn_16x1_20e_coco/htc_x101_64x4d_fpn_16x1_20e_coco_20200318-b181fd7a.pth) | [log](https://download.openmmlab.com/mmdetection/v2.0/htc/htc_x101_64x4d_fpn_16x1_20e_coco/htc_x101_64x4d_fpn_16x1_20e_coco_20200318_081711.log.json) | - In the HTC paper and COCO 2018 Challenge, `score_thr` is set to 0.001 for both baselines and HTC. - We use 8 GPUs with 2 images/GPU for R-50 and R-101 models, and 16 GPUs with 1 image/GPU for X-101 models. @@ -54,4 +54,4 @@ We also provide a powerful HTC with DCN and multi-scale training model. No testi | Backbone | Style | DCN | training scales | Lr schd | box AP | mask AP | Config | Download | |:----------------:|:-------:|:-----:|:---------------:|:-------:|:------:|:-------:|:------:|:--------:| -| X-101-64x4d-FPN | pytorch | c3-c5 | 400~1400 | 20e | 50.4 | 43.8 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/htc/htc_x101_64x4d_fpn_dconv_c3-c5_mstrain_400_1400_16x1_20e_coco.py) | [model](http://download.openmmlab.com/mmdetection/v2.0/htc/htc_x101_64x4d_fpn_dconv_c3-c5_mstrain_400_1400_16x1_20e_coco/htc_x101_64x4d_fpn_dconv_c3-c5_mstrain_400_1400_16x1_20e_coco_20200312-946fd751.pth) | [log](http://download.openmmlab.com/mmdetection/v2.0/htc/htc_x101_64x4d_fpn_dconv_c3-c5_mstrain_400_1400_16x1_20e_coco/htc_x101_64x4d_fpn_dconv_c3-c5_mstrain_400_1400_16x1_20e_coco_20200312_203410.log.json) | +| X-101-64x4d-FPN | pytorch | c3-c5 | 400~1400 | 20e | 50.4 | 43.8 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/htc/htc_x101_64x4d_fpn_dconv_c3-c5_mstrain_400_1400_16x1_20e_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/htc/htc_x101_64x4d_fpn_dconv_c3-c5_mstrain_400_1400_16x1_20e_coco/htc_x101_64x4d_fpn_dconv_c3-c5_mstrain_400_1400_16x1_20e_coco_20200312-946fd751.pth) | [log](https://download.openmmlab.com/mmdetection/v2.0/htc/htc_x101_64x4d_fpn_dconv_c3-c5_mstrain_400_1400_16x1_20e_coco/htc_x101_64x4d_fpn_dconv_c3-c5_mstrain_400_1400_16x1_20e_coco_20200312_203410.log.json) | diff --git a/configs/htc/htc_r101_fpn_20e_coco.py b/configs/htc/htc_r101_fpn_20e_coco.py index de3d5b7..b42297b 100644 --- a/configs/htc/htc_r101_fpn_20e_coco.py +++ b/configs/htc/htc_r101_fpn_20e_coco.py @@ -1,5 +1,9 @@ _base_ = './htc_r50_fpn_1x_coco.py' -model = dict(pretrained='torchvision://resnet101', backbone=dict(depth=101)) +model = dict( + backbone=dict( + depth=101, + init_cfg=dict(type='Pretrained', + checkpoint='torchvision://resnet101'))) # learning policy lr_config = dict(step=[16, 19]) runner = dict(type='EpochBasedRunner', max_epochs=20) diff --git a/configs/htc/htc_r50_fpn_1x_coco.py b/configs/htc/htc_r50_fpn_1x_coco.py index 929cf46..1e8e18a 100644 --- a/configs/htc/htc_r50_fpn_1x_coco.py +++ b/configs/htc/htc_r50_fpn_1x_coco.py @@ -14,8 +14,8 @@ in_channels=256, conv_out_channels=256, num_classes=183, - ignore_label=255, - loss_weight=0.2))) + loss_seg=dict( + type='CrossEntropyLoss', ignore_index=255, loss_weight=0.2)))) data_root = 'data/coco/' img_norm_cfg = dict( mean=[123.675, 116.28, 103.53], std=[58.395, 57.12, 57.375], to_rgb=True) diff --git a/configs/htc/htc_without_semantic_r50_fpn_1x_coco.py b/configs/htc/htc_without_semantic_r50_fpn_1x_coco.py index d028d98..565104f 100644 --- a/configs/htc/htc_without_semantic_r50_fpn_1x_coco.py +++ b/configs/htc/htc_without_semantic_r50_fpn_1x_coco.py @@ -5,7 +5,6 @@ # model settings model = dict( type='HybridTaskCascade', - pretrained='torchvision://resnet50', backbone=dict( type='ResNet', depth=50, @@ -14,7 +13,8 @@ frozen_stages=1, norm_cfg=dict(type='BN', requires_grad=True), norm_eval=True, - style='pytorch'), + style='pytorch', + init_cfg=dict(type='Pretrained', checkpoint='torchvision://resnet50')), neck=dict( type='FPN', in_channels=[256, 512, 1024, 2048], diff --git a/configs/htc/htc_x101_32x4d_fpn_16x1_20e_coco.py b/configs/htc/htc_x101_32x4d_fpn_16x1_20e_coco.py index b9e5524..0c834f2 100644 --- a/configs/htc/htc_x101_32x4d_fpn_16x1_20e_coco.py +++ b/configs/htc/htc_x101_32x4d_fpn_16x1_20e_coco.py @@ -1,6 +1,5 @@ _base_ = './htc_r50_fpn_1x_coco.py' model = dict( - pretrained='open-mmlab://resnext101_32x4d', backbone=dict( type='ResNeXt', depth=101, @@ -11,7 +10,9 @@ frozen_stages=1, norm_cfg=dict(type='BN', requires_grad=True), norm_eval=True, - style='pytorch')) + style='pytorch', + init_cfg=dict( + type='Pretrained', checkpoint='open-mmlab://resnext101_32x4d'))) data = dict(samples_per_gpu=1, workers_per_gpu=1) # learning policy lr_config = dict(step=[16, 19]) diff --git a/configs/htc/htc_x101_64x4d_fpn_16x1_20e_coco.py b/configs/htc/htc_x101_64x4d_fpn_16x1_20e_coco.py index b140f75..8b0d962 100644 --- a/configs/htc/htc_x101_64x4d_fpn_16x1_20e_coco.py +++ b/configs/htc/htc_x101_64x4d_fpn_16x1_20e_coco.py @@ -1,6 +1,5 @@ _base_ = './htc_r50_fpn_1x_coco.py' model = dict( - pretrained='open-mmlab://resnext101_64x4d', backbone=dict( type='ResNeXt', depth=101, @@ -11,7 +10,9 @@ frozen_stages=1, norm_cfg=dict(type='BN', requires_grad=True), norm_eval=True, - style='pytorch')) + style='pytorch', + init_cfg=dict( + type='Pretrained', checkpoint='open-mmlab://resnext101_64x4d'))) data = dict(samples_per_gpu=1, workers_per_gpu=1) # learning policy lr_config = dict(step=[16, 19]) diff --git a/configs/htc/htc_x101_64x4d_fpn_dconv_c3-c5_mstrain_400_1400_16x1_20e_coco.py b/configs/htc/htc_x101_64x4d_fpn_dconv_c3-c5_mstrain_400_1400_16x1_20e_coco.py index da89e09..c8d8703 100644 --- a/configs/htc/htc_x101_64x4d_fpn_dconv_c3-c5_mstrain_400_1400_16x1_20e_coco.py +++ b/configs/htc/htc_x101_64x4d_fpn_dconv_c3-c5_mstrain_400_1400_16x1_20e_coco.py @@ -1,6 +1,5 @@ _base_ = './htc_r50_fpn_1x_coco.py' model = dict( - pretrained='open-mmlab://resnext101_64x4d', backbone=dict( type='ResNeXt', depth=101, @@ -13,7 +12,9 @@ norm_eval=True, style='pytorch', dcn=dict(type='DCN', deform_groups=1, fallback_on_stride=False), - stage_with_dcn=(False, True, True, True))) + stage_with_dcn=(False, True, True, True), + init_cfg=dict( + type='Pretrained', checkpoint='open-mmlab://resnext101_64x4d'))) # dataset settings img_norm_cfg = dict( mean=[123.675, 116.28, 103.53], std=[58.395, 57.12, 57.375], to_rgb=True) diff --git a/configs/htc/metafile.yml b/configs/htc/metafile.yml new file mode 100644 index 0000000..acd038c --- /dev/null +++ b/configs/htc/metafile.yml @@ -0,0 +1,165 @@ +Collections: + - Name: HTC + Metadata: + Training Data: COCO + Training Techniques: + - SGD with Momentum + - Weight Decay + Training Resources: 8x V100 GPUs + Architecture: + - FPN + - HTC + - RPN + - ResNet + - ResNeXt + - RoIAlign + Paper: + URL: https://arxiv.org/abs/1901.07518 + Title: 'Hybrid Task Cascade for Instance Segmentation' + README: configs/htc/README.md + Code: + URL: https://github.com/open-mmlab/mmdetection/blob/v2.0.0/mmdet/models/detectors/htc.py#L6 + Version: v2.0.0 + +Models: + - Name: htc_r50_fpn_1x_coco + In Collection: HTC + Config: configs/htc/htc_r50_fpn_1x_coco.py + Metadata: + Training Memory (GB): 8.2 + inference time (ms/im): + - value: 172.41 + hardware: V100 + backend: PyTorch + batch size: 1 + mode: FP32 + resolution: (800, 1333) + Epochs: 12 + Results: + - Task: Object Detection + Dataset: COCO + Metrics: + box AP: 42.3 + - Task: Instance Segmentation + Dataset: COCO + Metrics: + mask AP: 37.4 + Weights: https://download.openmmlab.com/mmdetection/v2.0/htc/htc_r50_fpn_1x_coco/htc_r50_fpn_1x_coco_20200317-7332cf16.pth + + - Name: htc_r50_fpn_20e_coco + In Collection: HTC + Config: configs/htc/htc_r50_fpn_20e_coco.py + Metadata: + Training Memory (GB): 8.2 + inference time (ms/im): + - value: 172.41 + hardware: V100 + backend: PyTorch + batch size: 1 + mode: FP32 + resolution: (800, 1333) + Epochs: 20 + Results: + - Task: Object Detection + Dataset: COCO + Metrics: + box AP: 43.3 + - Task: Instance Segmentation + Dataset: COCO + Metrics: + mask AP: 38.3 + Weights: https://download.openmmlab.com/mmdetection/v2.0/htc/htc_r50_fpn_20e_coco/htc_r50_fpn_20e_coco_20200319-fe28c577.pth + + - Name: htc_r101_fpn_20e_coco + In Collection: HTC + Config: configs/htc/htc_r101_fpn_20e_coco.py + Metadata: + Training Memory (GB): 10.2 + inference time (ms/im): + - value: 181.82 + hardware: V100 + backend: PyTorch + batch size: 1 + mode: FP32 + resolution: (800, 1333) + Epochs: 20 + Results: + - Task: Object Detection + Dataset: COCO + Metrics: + box AP: 44.8 + - Task: Instance Segmentation + Dataset: COCO + Metrics: + mask AP: 39.6 + Weights: https://download.openmmlab.com/mmdetection/v2.0/htc/htc_r101_fpn_20e_coco/htc_r101_fpn_20e_coco_20200317-9b41b48f.pth + + - Name: htc_x101_32x4d_fpn_16x1_20e_coco + In Collection: HTC + Config: configs/htc/htc_x101_32x4d_fpn_16x1_20e_coco.py + Metadata: + Training Resources: 16x V100 GPUs + Batch Size: 16 + Training Memory (GB): 11.4 + inference time (ms/im): + - value: 200 + hardware: V100 + backend: PyTorch + batch size: 1 + mode: FP32 + resolution: (800, 1333) + Epochs: 20 + Results: + - Task: Object Detection + Dataset: COCO + Metrics: + box AP: 46.1 + - Task: Instance Segmentation + Dataset: COCO + Metrics: + mask AP: 40.5 + Weights: https://download.openmmlab.com/mmdetection/v2.0/htc/htc_x101_32x4d_fpn_16x1_20e_coco/htc_x101_32x4d_fpn_16x1_20e_coco_20200318-de97ae01.pth + + - Name: htc_x101_64x4d_fpn_16x1_20e_coco + In Collection: HTC + Config: configs/htc/htc_x101_64x4d_fpn_16x1_20e_coco.py + Metadata: + Training Resources: 16x V100 GPUs + Batch Size: 16 + Training Memory (GB): 14.5 + inference time (ms/im): + - value: 227.27 + hardware: V100 + backend: PyTorch + batch size: 1 + mode: FP32 + resolution: (800, 1333) + Epochs: 20 + Results: + - Task: Object Detection + Dataset: COCO + Metrics: + box AP: 47.0 + - Task: Instance Segmentation + Dataset: COCO + Metrics: + mask AP: 41.4 + Weights: https://download.openmmlab.com/mmdetection/v2.0/htc/htc_x101_64x4d_fpn_16x1_20e_coco/htc_x101_64x4d_fpn_16x1_20e_coco_20200318-b181fd7a.pth + + - Name: htc_x101_64x4d_fpn_dconv_c3-c5_mstrain_400_1400_16x1_20e_coco + In Collection: HTC + Config: configs/htc/htc_x101_64x4d_fpn_dconv_c3-c5_mstrain_400_1400_16x1_20e_coco.py + Metadata: + Training Resources: 16x V100 GPUs + Batch Size: 16 + Epochs: 20 + Results: + - Task: Object Detection + Dataset: COCO + Metrics: + box AP: 50.4 + - Task: Instance Segmentation + Dataset: COCO + Metrics: + mask AP: 43.8 + Weights: https://download.openmmlab.com/mmdetection/v2.0/htc/htc_x101_64x4d_fpn_dconv_c3-c5_mstrain_400_1400_16x1_20e_coco/htc_x101_64x4d_fpn_dconv_c3-c5_mstrain_400_1400_16x1_20e_coco_20200312-946fd751.pth diff --git a/configs/instaboost/README.md b/configs/instaboost/README.md index 5ab74a1..02e9676 100644 --- a/configs/instaboost/README.md +++ b/configs/instaboost/README.md @@ -1,6 +1,6 @@ # InstaBoost for MMDetection -[ALGORITHM] + Configs in this directory is the implementation for ICCV2019 paper "InstaBoost: Boosting Instance Segmentation Via Probability Map Guided Copy-Pasting" and provided by the authors of the paper. InstaBoost is a data augmentation method for object detection and instance segmentation. The paper has been released on [`arXiv`](https://arxiv.org/abs/1908.07801). @@ -38,7 +38,7 @@ InstaBoost have been already integrated in the data pipeline, thus all you need | Network | Backbone | Lr schd | Mem (GB) | Inf time (fps) | box AP | mask AP | Config | Download | | :-------------: | :--------: | :-----: | :------: | :------------: | :------:| :-----: | :------: | :-----------------: | -| Mask R-CNN | R-50-FPN | 4x | 4.4 | 17.5 | 40.6 | 36.6 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/instaboost/mask_rcnn_r50_fpn_instaboost_4x_coco.py) | [model](http://download.openmmlab.com/mmdetection/v2.0/instaboost/mask_rcnn_r50_fpn_instaboost_4x_coco/mask_rcnn_r50_fpn_instaboost_4x_coco_20200307-d025f83a.pth) | [log](http://download.openmmlab.com/mmdetection/v2.0/instaboost/mask_rcnn_r50_fpn_instaboost_4x_coco/mask_rcnn_r50_fpn_instaboost_4x_coco_20200307_223635.log.json) | -| Mask R-CNN | R-101-FPN | 4x | 6.4 | | 42.5 | 38.0 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/instaboost/mask_rcnn_r101_fpn_instaboost_4x_coco.py) | [model](http://download.openmmlab.com/mmdetection/v2.0/instaboost/mask_rcnn_r101_fpn_instaboost_4x_coco/mask_rcnn_r101_fpn_instaboost_4x_coco_20200703_235738-f23f3a5f.pth) | [log](http://download.openmmlab.com/mmdetection/v2.0/instaboost/mask_rcnn_r101_fpn_instaboost_4x_coco/mask_rcnn_r101_fpn_instaboost_4x_coco_20200703_235738.log.json) | -| Mask R-CNN | X-101-64x4d-FPN | 4x | 10.7 | | 44.7 | 39.7 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/instaboost/mask_rcnn_x101_64x4d_fpn_instaboost_4x_coco.py) | [model](http://download.openmmlab.com/mmdetection/v2.0/instaboost/mask_rcnn_x101_64x4d_fpn_instaboost_4x_coco/mask_rcnn_x101_64x4d_fpn_instaboost_4x_coco_20200515_080947-8ed58c1b.pth) | [log](http://download.openmmlab.com/mmdetection/v2.0/instaboost/mask_rcnn_x101_64x4d_fpn_instaboost_4x_coco/mask_rcnn_x101_64x4d_fpn_instaboost_4x_coco_20200515_080947.log.json) | -| Cascade R-CNN | R-101-FPN | 4x | 6.0 | 12.0 | 43.7 | 38.0 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/instaboost/cascade_mask_rcnn_r50_fpn_instaboost_4x_coco.py) | [model](http://download.openmmlab.com/mmdetection/v2.0/instaboost/cascade_mask_rcnn_r50_fpn_instaboost_4x_coco/cascade_mask_rcnn_r50_fpn_instaboost_4x_coco_20200307-c19d98d9.pth) | [log](http://download.openmmlab.com/mmdetection/v2.0/instaboost/cascade_mask_rcnn_r50_fpn_instaboost_4x_coco/cascade_mask_rcnn_r50_fpn_instaboost_4x_coco_20200307_223646.log.json) | +| Mask R-CNN | R-50-FPN | 4x | 4.4 | 17.5 | 40.6 | 36.6 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/instaboost/mask_rcnn_r50_fpn_instaboost_4x_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/instaboost/mask_rcnn_r50_fpn_instaboost_4x_coco/mask_rcnn_r50_fpn_instaboost_4x_coco_20200307-d025f83a.pth) | [log](https://download.openmmlab.com/mmdetection/v2.0/instaboost/mask_rcnn_r50_fpn_instaboost_4x_coco/mask_rcnn_r50_fpn_instaboost_4x_coco_20200307_223635.log.json) | +| Mask R-CNN | R-101-FPN | 4x | 6.4 | | 42.5 | 38.0 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/instaboost/mask_rcnn_r101_fpn_instaboost_4x_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/instaboost/mask_rcnn_r101_fpn_instaboost_4x_coco/mask_rcnn_r101_fpn_instaboost_4x_coco_20200703_235738-f23f3a5f.pth) | [log](https://download.openmmlab.com/mmdetection/v2.0/instaboost/mask_rcnn_r101_fpn_instaboost_4x_coco/mask_rcnn_r101_fpn_instaboost_4x_coco_20200703_235738.log.json) | +| Mask R-CNN | X-101-64x4d-FPN | 4x | 10.7 | | 44.7 | 39.7 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/instaboost/mask_rcnn_x101_64x4d_fpn_instaboost_4x_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/instaboost/mask_rcnn_x101_64x4d_fpn_instaboost_4x_coco/mask_rcnn_x101_64x4d_fpn_instaboost_4x_coco_20200515_080947-8ed58c1b.pth) | [log](https://download.openmmlab.com/mmdetection/v2.0/instaboost/mask_rcnn_x101_64x4d_fpn_instaboost_4x_coco/mask_rcnn_x101_64x4d_fpn_instaboost_4x_coco_20200515_080947.log.json) | +| Cascade R-CNN | R-101-FPN | 4x | 6.0 | 12.0 | 43.7 | 38.0 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/instaboost/cascade_mask_rcnn_r50_fpn_instaboost_4x_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/instaboost/cascade_mask_rcnn_r50_fpn_instaboost_4x_coco/cascade_mask_rcnn_r50_fpn_instaboost_4x_coco_20200307-c19d98d9.pth) | [log](https://download.openmmlab.com/mmdetection/v2.0/instaboost/cascade_mask_rcnn_r50_fpn_instaboost_4x_coco/cascade_mask_rcnn_r50_fpn_instaboost_4x_coco_20200307_223646.log.json) | diff --git a/configs/instaboost/cascade_mask_rcnn_r101_fpn_instaboost_4x_coco.py b/configs/instaboost/cascade_mask_rcnn_r101_fpn_instaboost_4x_coco.py index 723ab02..9d0515d 100644 --- a/configs/instaboost/cascade_mask_rcnn_r101_fpn_instaboost_4x_coco.py +++ b/configs/instaboost/cascade_mask_rcnn_r101_fpn_instaboost_4x_coco.py @@ -1,3 +1,7 @@ _base_ = './cascade_mask_rcnn_r50_fpn_instaboost_4x_coco.py' -model = dict(pretrained='torchvision://resnet101', backbone=dict(depth=101)) +model = dict( + backbone=dict( + depth=101, + init_cfg=dict(type='Pretrained', + checkpoint='torchvision://resnet101'))) diff --git a/configs/instaboost/cascade_mask_rcnn_x101_64x4d_fpn_instaboost_4x_coco.py b/configs/instaboost/cascade_mask_rcnn_x101_64x4d_fpn_instaboost_4x_coco.py index 7cf5f30..d67b799 100644 --- a/configs/instaboost/cascade_mask_rcnn_x101_64x4d_fpn_instaboost_4x_coco.py +++ b/configs/instaboost/cascade_mask_rcnn_x101_64x4d_fpn_instaboost_4x_coco.py @@ -1,6 +1,5 @@ _base_ = './cascade_mask_rcnn_r50_fpn_instaboost_4x_coco.py' model = dict( - pretrained='open-mmlab://resnext101_64x4d', backbone=dict( type='ResNeXt', depth=101, @@ -10,4 +9,6 @@ out_indices=(0, 1, 2, 3), frozen_stages=1, norm_cfg=dict(type='BN', requires_grad=True), - style='pytorch')) + style='pytorch', + init_cfg=dict( + type='Pretrained', checkpoint='open-mmlab://resnext101_64x4d'))) diff --git a/configs/instaboost/mask_rcnn_r101_fpn_instaboost_4x_coco.py b/configs/instaboost/mask_rcnn_r101_fpn_instaboost_4x_coco.py index c281947..ebbb43e 100644 --- a/configs/instaboost/mask_rcnn_r101_fpn_instaboost_4x_coco.py +++ b/configs/instaboost/mask_rcnn_r101_fpn_instaboost_4x_coco.py @@ -1,2 +1,6 @@ _base_ = './mask_rcnn_r50_fpn_instaboost_4x_coco.py' -model = dict(pretrained='torchvision://resnet101', backbone=dict(depth=101)) +model = dict( + backbone=dict( + depth=101, + init_cfg=dict(type='Pretrained', + checkpoint='torchvision://resnet101'))) diff --git a/configs/instaboost/mask_rcnn_x101_64x4d_fpn_instaboost_4x_coco.py b/configs/instaboost/mask_rcnn_x101_64x4d_fpn_instaboost_4x_coco.py index 0acd088..2010f44 100644 --- a/configs/instaboost/mask_rcnn_x101_64x4d_fpn_instaboost_4x_coco.py +++ b/configs/instaboost/mask_rcnn_x101_64x4d_fpn_instaboost_4x_coco.py @@ -1,6 +1,5 @@ _base_ = './mask_rcnn_r50_fpn_instaboost_4x_coco.py' model = dict( - pretrained='open-mmlab://resnext101_64x4d', backbone=dict( type='ResNeXt', depth=101, @@ -10,4 +9,6 @@ out_indices=(0, 1, 2, 3), frozen_stages=1, norm_cfg=dict(type='BN', requires_grad=True), - style='pytorch')) + style='pytorch', + init_cfg=dict( + type='Pretrained', checkpoint='open-mmlab://resnext101_64x4d'))) diff --git a/configs/instaboost/metafile.yml b/configs/instaboost/metafile.yml new file mode 100644 index 0000000..325283d --- /dev/null +++ b/configs/instaboost/metafile.yml @@ -0,0 +1,99 @@ +Collections: + - Name: InstaBoost + Metadata: + Training Data: COCO + Training Techniques: + - InstaBoost + - SGD with Momentum + - Weight Decay + Training Resources: 8x V100 GPUs + Paper: + URL: https://arxiv.org/abs/1908.07801 + Title: 'Instaboost: Boosting instance segmentation via probability map guided copy-pasting' + README: configs/instaboost/README.md + Code: + URL: https://github.com/open-mmlab/mmdetection/blob/v2.0.0/mmdet/datasets/pipelines/instaboost.py#L7 + Version: v2.0.0 + +Models: + - Name: mask_rcnn_r50_fpn_instaboost_4x_coco + In Collection: InstaBoost + Config: configs/instaboost/mask_rcnn_r50_fpn_instaboost_4x_coco.py + Metadata: + Training Memory (GB): 4.4 + inference time (ms/im): + - value: 57.14 + hardware: V100 + backend: PyTorch + batch size: 1 + mode: FP32 + resolution: (800, 1333) + Epochs: 48 + Results: + - Task: Object Detection + Dataset: COCO + Metrics: + box AP: 40.6 + - Task: Instance Segmentation + Dataset: COCO + Metrics: + mask AP: 36.6 + Weights: https://download.openmmlab.com/mmdetection/v2.0/instaboost/mask_rcnn_r50_fpn_instaboost_4x_coco/mask_rcnn_r50_fpn_instaboost_4x_coco_20200307-d025f83a.pth + + - Name: mask_rcnn_r101_fpn_instaboost_4x_coco + In Collection: InstaBoost + Config: configs/instaboost/mask_rcnn_r101_fpn_instaboost_4x_coco.py + Metadata: + Training Memory (GB): 6.4 + Epochs: 48 + Results: + - Task: Object Detection + Dataset: COCO + Metrics: + box AP: 42.5 + - Task: Instance Segmentation + Dataset: COCO + Metrics: + mask AP: 38.0 + Weights: https://download.openmmlab.com/mmdetection/v2.0/instaboost/mask_rcnn_r101_fpn_instaboost_4x_coco/mask_rcnn_r101_fpn_instaboost_4x_coco_20200703_235738-f23f3a5f.pth + + - Name: mask_rcnn_x101_64x4d_fpn_instaboost_4x_coco + In Collection: InstaBoost + Config: configs/instaboost/mask_rcnn_x101_64x4d_fpn_instaboost_4x_coco.py + Metadata: + Training Memory (GB): 10.7 + Epochs: 48 + Results: + - Task: Object Detection + Dataset: COCO + Metrics: + box AP: 44.7 + - Task: Instance Segmentation + Dataset: COCO + Metrics: + mask AP: 39.7 + Weights: https://download.openmmlab.com/mmdetection/v2.0/instaboost/mask_rcnn_x101_64x4d_fpn_instaboost_4x_coco/mask_rcnn_x101_64x4d_fpn_instaboost_4x_coco_20200515_080947-8ed58c1b.pth + + - Name: cascade_mask_rcnn_r50_fpn_instaboost_4x_coco + In Collection: InstaBoost + Config: configs/instaboost/cascade_mask_rcnn_r50_fpn_instaboost_4x_coco.py + Metadata: + Training Memory (GB): 6.0 + inference time (ms/im): + - value: 83.33 + hardware: V100 + backend: PyTorch + batch size: 1 + mode: FP32 + resolution: (800, 1333) + Epochs: 48 + Results: + - Task: Object Detection + Dataset: COCO + Metrics: + box AP: 43.7 + - Task: Instance Segmentation + Dataset: COCO + Metrics: + mask AP: 38.0 + Weights: https://download.openmmlab.com/mmdetection/v2.0/instaboost/cascade_mask_rcnn_r50_fpn_instaboost_4x_coco/cascade_mask_rcnn_r50_fpn_instaboost_4x_coco_20200307-c19d98d9.pth diff --git a/configs/ld/README.md b/configs/ld/README.md new file mode 100644 index 0000000..0177f1e --- /dev/null +++ b/configs/ld/README.md @@ -0,0 +1,31 @@ +# Localization Distillation for Object Detection + +## Introduction + + + +```latex +@Article{zheng2021LD, + title={Localization Distillation for Object Detection}, + author= {Zhaohui Zheng, Rongguang Ye, Ping Wang, Jun Wang, Dongwei Ren, Wangmeng Zuo}, + journal={arXiv:2102.12252}, + year={2021} +} +``` + +### GFocalV1 with LD + +| Teacher | Student | Training schedule | Mini-batch size | AP (val) | AP50 (val) | AP75 (val) | Config | +| :-------: | :-----: | :---------------: | :-------------: | :------: | :--------: | :--------: | :--------------: | +| -- | R-18 | 1x | 6 | 35.8 | 53.1 | 38.2 | | +| R-101 | R-18 | 1x | 6 | 36.5 | 52.9 | 39.3 | [config](https://github.com/open-mmlab/mmdetection/blob/master/configs/ld/ld_r18_gflv1_r101_fpn_coco_1x.py) | +| -- | R-34 | 1x | 6 | 38.9 | 56.6 | 42.2 | | +| R-101 | R-34 | 1x | 6 | 39.8 | 56.6 | 43.1 | [config](https://github.com/open-mmlab/mmdetection/blob/master/configs/ld/ld_r34_gflv1_r101_fpn_coco_1x.py) | +| -- | R-50 | 1x | 6 | 40.1 | 58.2 | 43.1 | | +| R-101 | R-50 | 1x | 6 | 41.1 | 58.7 | 44.9 | [config](https://github.com/open-mmlab/mmdetection/blob/master/configs/ld/ld_r50_gflv1_r101_fpn_coco_1x.py) | +| -- | R-101 | 2x | 6 | 44.6 | 62.9 | 48.4 | | +| R-101-DCN | R-101 | 2x | 6 | 45.4 | 63.1 | 49.5 | [config](https://github.com/open-mmlab/mmdetection/blob/master/configs/ld/ld_r101_gflv1_r101dcn_fpn_coco_1x.py) | + +## Note + +- Meaning of Config name: ld_r18(student model)_gflv1(based on gflv1)_r101(teacher model)_fpn(neck)_coco(dataset)_1x(12 epoch).py diff --git a/configs/ld/ld_r101_gflv1_r101dcn_fpn_coco_2x.py b/configs/ld/ld_r101_gflv1_r101dcn_fpn_coco_2x.py new file mode 100644 index 0000000..1cbdb4c --- /dev/null +++ b/configs/ld/ld_r101_gflv1_r101dcn_fpn_coco_2x.py @@ -0,0 +1,44 @@ +_base_ = ['./ld_r18_gflv1_r101_fpn_coco_1x.py'] +teacher_ckpt = 'https://download.openmmlab.com/mmdetection/v2.0/gfl/gfl_r101_fpn_dconv_c3-c5_mstrain_2x_coco/gfl_r101_fpn_dconv_c3-c5_mstrain_2x_coco_20200630_102002-134b07df.pth' # noqa +model = dict( + teacher_config='configs/gfl/gfl_r101_fpn_dconv_c3-c5_mstrain_2x_coco.py', + teacher_ckpt=teacher_ckpt, + backbone=dict( + type='ResNet', + depth=101, + num_stages=4, + out_indices=(0, 1, 2, 3), + frozen_stages=1, + norm_cfg=dict(type='BN', requires_grad=True), + norm_eval=True, + style='pytorch', + init_cfg=dict(type='Pretrained', + checkpoint='torchvision://resnet101')), + neck=dict( + type='FPN', + in_channels=[256, 512, 1024, 2048], + out_channels=256, + start_level=1, + add_extra_convs='on_output', + num_outs=5)) + +lr_config = dict(step=[16, 22]) +runner = dict(type='EpochBasedRunner', max_epochs=24) +# multi-scale training +img_norm_cfg = dict( + mean=[123.675, 116.28, 103.53], std=[58.395, 57.12, 57.375], to_rgb=True) +train_pipeline = [ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations', with_bbox=True), + dict( + type='Resize', + img_scale=[(1333, 480), (1333, 800)], + multiscale_mode='range', + keep_ratio=True), + dict(type='RandomFlip', flip_ratio=0.5), + dict(type='Normalize', **img_norm_cfg), + dict(type='Pad', size_divisor=32), + dict(type='DefaultFormatBundle'), + dict(type='Collect', keys=['img', 'gt_bboxes', 'gt_labels']), +] +data = dict(train=dict(pipeline=train_pipeline)) diff --git a/configs/ld/ld_r18_gflv1_r101_fpn_coco_1x.py b/configs/ld/ld_r18_gflv1_r101_fpn_coco_1x.py new file mode 100644 index 0000000..18dce81 --- /dev/null +++ b/configs/ld/ld_r18_gflv1_r101_fpn_coco_1x.py @@ -0,0 +1,62 @@ +_base_ = [ + '../_base_/datasets/coco_detection.py', + '../_base_/schedules/schedule_1x.py', '../_base_/default_runtime.py' +] +teacher_ckpt = 'https://download.openmmlab.com/mmdetection/v2.0/gfl/gfl_r101_fpn_mstrain_2x_coco/gfl_r101_fpn_mstrain_2x_coco_20200629_200126-dd12f847.pth' # noqa +model = dict( + type='KnowledgeDistillationSingleStageDetector', + teacher_config='configs/gfl/gfl_r101_fpn_mstrain_2x_coco.py', + teacher_ckpt=teacher_ckpt, + backbone=dict( + type='ResNet', + depth=18, + num_stages=4, + out_indices=(0, 1, 2, 3), + frozen_stages=1, + norm_cfg=dict(type='BN', requires_grad=True), + norm_eval=True, + style='pytorch', + init_cfg=dict(type='Pretrained', checkpoint='torchvision://resnet18')), + neck=dict( + type='FPN', + in_channels=[64, 128, 256, 512], + out_channels=256, + start_level=1, + add_extra_convs='on_output', + num_outs=5), + bbox_head=dict( + type='LDHead', + num_classes=80, + in_channels=256, + stacked_convs=4, + feat_channels=256, + anchor_generator=dict( + type='AnchorGenerator', + ratios=[1.0], + octave_base_scale=8, + scales_per_octave=1, + strides=[8, 16, 32, 64, 128]), + loss_cls=dict( + type='QualityFocalLoss', + use_sigmoid=True, + beta=2.0, + loss_weight=1.0), + loss_dfl=dict(type='DistributionFocalLoss', loss_weight=0.25), + loss_ld=dict( + type='KnowledgeDistillationKLDivLoss', loss_weight=0.25, T=10), + reg_max=16, + loss_bbox=dict(type='GIoULoss', loss_weight=2.0)), + # training and testing settings + train_cfg=dict( + assigner=dict(type='ATSSAssigner', topk=9), + allowed_border=-1, + pos_weight=-1, + debug=False), + test_cfg=dict( + nms_pre=1000, + min_bbox_size=0, + score_thr=0.05, + nms=dict(type='nms', iou_threshold=0.6), + max_per_img=100)) + +optimizer = dict(type='SGD', lr=0.01, momentum=0.9, weight_decay=0.0001) diff --git a/configs/ld/ld_r34_gflv1_r101_fpn_coco_1x.py b/configs/ld/ld_r34_gflv1_r101_fpn_coco_1x.py new file mode 100644 index 0000000..3b6996d --- /dev/null +++ b/configs/ld/ld_r34_gflv1_r101_fpn_coco_1x.py @@ -0,0 +1,19 @@ +_base_ = ['./ld_r18_gflv1_r101_fpn_coco_1x.py'] +model = dict( + backbone=dict( + type='ResNet', + depth=34, + num_stages=4, + out_indices=(0, 1, 2, 3), + frozen_stages=1, + norm_cfg=dict(type='BN', requires_grad=True), + norm_eval=True, + style='pytorch', + init_cfg=dict(type='Pretrained', checkpoint='torchvision://resnet34')), + neck=dict( + type='FPN', + in_channels=[64, 128, 256, 512], + out_channels=256, + start_level=1, + add_extra_convs='on_output', + num_outs=5)) diff --git a/configs/ld/ld_r50_gflv1_r101_fpn_coco_1x.py b/configs/ld/ld_r50_gflv1_r101_fpn_coco_1x.py new file mode 100644 index 0000000..2b18785 --- /dev/null +++ b/configs/ld/ld_r50_gflv1_r101_fpn_coco_1x.py @@ -0,0 +1,19 @@ +_base_ = ['./ld_r18_gflv1_r101_fpn_coco_1x.py'] +model = dict( + backbone=dict( + type='ResNet', + depth=50, + num_stages=4, + out_indices=(0, 1, 2, 3), + frozen_stages=1, + norm_cfg=dict(type='BN', requires_grad=True), + norm_eval=True, + style='pytorch', + init_cfg=dict(type='Pretrained', checkpoint='torchvision://resnet50')), + neck=dict( + type='FPN', + in_channels=[256, 512, 1024, 2048], + out_channels=256, + start_level=1, + add_extra_convs='on_output', + num_outs=5)) diff --git a/configs/ld/metafile.yml b/configs/ld/metafile.yml new file mode 100644 index 0000000..cd833bf --- /dev/null +++ b/configs/ld/metafile.yml @@ -0,0 +1,72 @@ +Collections: + - Name: Localization Distillation + Metadata: + Training Data: COCO + Training Techniques: + - Localization Distillation + - SGD with Momentum + - Weight Decay + Training Resources: 8x V100 GPUs + Architecture: + - FPN + - ResNet + Paper: + URL: https://arxiv.org/abs/2102.12252 + Title: 'Localization Distillation for Object Detection' + README: configs/ld/README.md + Code: + URL: https://github.com/open-mmlab/mmdetection/blob/v2.11.0/mmdet/models/dense_heads/ld_head.py#L11 + Version: v2.11.0 + +Models: + - Name: ld_r18_gflv1_r101_fpn_coco_1x + In Collection: Localization Distillation + Config: configs/ld/ld_r18_gflv1_r101_fpn_coco_1x.py + Metadata: + Teacher: R-101 + Results: + - Task: Object Detection + Dataset: COCO + Metrics: + box AP: 36.5 + box AP@0.5: 52.9 + box AP@0.75: 39.3 + + - Name: ld_r34_gflv1_r101_fpn_coco_1x + In Collection: Localization Distillation + Config: configs/ld/ld_r34_gflv1_r101_fpn_coco_1x.py + Metadata: + Teacher: R-101 + Results: + - Task: Object Detection + Dataset: COCO + Metrics: + box AP: 39.8 + box AP@0.5: 56.6 + box AP@0.75: 43.1 + + - Name: ld_r50_gflv1_r101_fpn_coco_1x + In Collection: Localization Distillation + Config: configs/ld/ld_r50_gflv1_r101_fpn_coco_1x.py + Metadata: + Teacher: R-101 + Results: + - Task: Object Detection + Dataset: COCO + Metrics: + box AP: 41.1 + box AP@0.5: 58.7 + box AP@0.75: 44.9 + + - Name: ld_r101_gflv1_r101dcn_fpn_coco_1x + In Collection: Localization Distillation + Config: configs/ld/ld_r101_gflv1_r101dcn_fpn_coco_1x.py + Metadata: + Teacher: R-101-DCN + Results: + - Task: Object Detection + Dataset: COCO + Metrics: + box AP: 45.4 + box AP@0.5: 63.1 + box AP@0.75: 49.5 diff --git a/configs/legacy_1.x/README.md b/configs/legacy_1.x/README.md index 61b2aa8..38a2a0e 100644 --- a/configs/legacy_1.x/README.md +++ b/configs/legacy_1.x/README.md @@ -1,6 +1,6 @@ # Legacy Configs in MMDetection V1.x -[OTHERS] + Configs in this directory implement the legacy configs used by MMDetection V1.x and its model zoos. diff --git a/configs/legacy_1.x/cascade_mask_rcnn_r50_fpn_1x_coco_v1.py b/configs/legacy_1.x/cascade_mask_rcnn_r50_fpn_1x_coco_v1.py index 5899444..fc9d004 100644 --- a/configs/legacy_1.x/cascade_mask_rcnn_r50_fpn_1x_coco_v1.py +++ b/configs/legacy_1.x/cascade_mask_rcnn_r50_fpn_1x_coco_v1.py @@ -5,7 +5,6 @@ ] model = dict( type='CascadeRCNN', - pretrained='torchvision://resnet50', backbone=dict( type='ResNet', depth=50, @@ -14,7 +13,8 @@ frozen_stages=1, norm_cfg=dict(type='BN', requires_grad=True), norm_eval=True, - style='pytorch'), + style='pytorch', + init_cfg=dict(type='Pretrained', checkpoint='torchvision://resnet50')), neck=dict( type='FPN', in_channels=[256, 512, 1024, 2048], diff --git a/configs/legacy_1.x/faster_rcnn_r50_fpn_1x_coco_v1.py b/configs/legacy_1.x/faster_rcnn_r50_fpn_1x_coco_v1.py index fb2f2d1..8c573be 100644 --- a/configs/legacy_1.x/faster_rcnn_r50_fpn_1x_coco_v1.py +++ b/configs/legacy_1.x/faster_rcnn_r50_fpn_1x_coco_v1.py @@ -6,7 +6,8 @@ model = dict( type='FasterRCNN', - pretrained='torchvision://resnet50', + backbone=dict( + init_cfg=dict(type='Pretrained', checkpoint='torchvision://resnet50')), rpn_head=dict( type='RPNHead', anchor_generator=dict( diff --git a/configs/legacy_1.x/retinanet_r50_caffe_fpn_1x_coco_v1.py b/configs/legacy_1.x/retinanet_r50_caffe_fpn_1x_coco_v1.py index ef9392f..a63d248 100644 --- a/configs/legacy_1.x/retinanet_r50_caffe_fpn_1x_coco_v1.py +++ b/configs/legacy_1.x/retinanet_r50_caffe_fpn_1x_coco_v1.py @@ -1,8 +1,12 @@ _base_ = './retinanet_r50_fpn_1x_coco_v1.py' model = dict( - pretrained='open-mmlab://detectron/resnet50_caffe', backbone=dict( - norm_cfg=dict(requires_grad=False), norm_eval=True, style='caffe')) + norm_cfg=dict(requires_grad=False), + norm_eval=True, + style='caffe', + init_cfg=dict( + type='Pretrained', + checkpoint='open-mmlab://detectron/resnet50_caffe'))) # use caffe img_norm img_norm_cfg = dict( mean=[102.9801, 115.9465, 122.7717], std=[1.0, 1.0, 1.0], to_rgb=False) diff --git a/configs/libra_rcnn/README.md b/configs/libra_rcnn/README.md index 1f28087..8b78af4 100644 --- a/configs/libra_rcnn/README.md +++ b/configs/libra_rcnn/README.md @@ -2,10 +2,12 @@ ## Introduction -[ALGORITHM] + We provide config files to reproduce the results in the CVPR 2019 paper [Libra R-CNN](https://arxiv.org/pdf/1904.02701.pdf). +The extended version of [Libra R-CNN](https://arxiv.org/pdf/2108.10175.pdf) is accpeted by IJCV. + ``` @inproceedings{pang2019libra, title={Libra R-CNN: Towards Balanced Learning for Object Detection}, @@ -13,6 +15,17 @@ We provide config files to reproduce the results in the CVPR 2019 paper [Libra R booktitle={IEEE Conference on Computer Vision and Pattern Recognition}, year={2019} } + +@article{pang2021towards, + title={Towards Balanced Learning for Instance Recognition}, + author={Pang, Jiangmiao and Chen, Kai and Li, Qi and Xu, Zhihai and Feng, Huajun and Shi, Jianping and Ouyang, Wanli and Lin, Dahua}, + journal={International Journal of Computer Vision}, + volume={129}, + number={5}, + pages={1376--1393}, + year={2021}, + publisher={Springer} +} ``` ## Results and models @@ -21,8 +34,8 @@ The results on COCO 2017val are shown in the below table. (results on test-dev a | Architecture | Backbone | Style | Lr schd | Mem (GB) | Inf time (fps) | box AP | Config | Download | |:------------:|:---------------:|:-------:|:-------:|:--------:|:--------------:|:------:|:------:|:--------:| -| Faster R-CNN | R-50-FPN | pytorch | 1x | 4.6 | 19.0 | 38.3 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/libra_rcnn/libra_faster_rcnn_r50_fpn_1x_coco.py) | [model](http://download.openmmlab.com/mmdetection/v2.0/libra_rcnn/libra_faster_rcnn_r50_fpn_1x_coco/libra_faster_rcnn_r50_fpn_1x_coco_20200130-3afee3a9.pth) | [log](http://download.openmmlab.com/mmdetection/v2.0/libra_rcnn/libra_faster_rcnn_r50_fpn_1x_coco/libra_faster_rcnn_r50_fpn_1x_coco_20200130_204655.log.json) | +| Faster R-CNN | R-50-FPN | pytorch | 1x | 4.6 | 19.0 | 38.3 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/libra_rcnn/libra_faster_rcnn_r50_fpn_1x_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/libra_rcnn/libra_faster_rcnn_r50_fpn_1x_coco/libra_faster_rcnn_r50_fpn_1x_coco_20200130-3afee3a9.pth) | [log](https://download.openmmlab.com/mmdetection/v2.0/libra_rcnn/libra_faster_rcnn_r50_fpn_1x_coco/libra_faster_rcnn_r50_fpn_1x_coco_20200130_204655.log.json) | | Fast R-CNN | R-50-FPN | pytorch | 1x | | | | | -| Faster R-CNN | R-101-FPN | pytorch | 1x | 6.5 | 14.4 | 40.1 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/libra_rcnn/libra_faster_rcnn_r101_fpn_1x_coco.py) | [model](http://download.openmmlab.com/mmdetection/v2.0/libra_rcnn/libra_faster_rcnn_r101_fpn_1x_coco/libra_faster_rcnn_r101_fpn_1x_coco_20200203-8dba6a5a.pth) | [log](http://download.openmmlab.com/mmdetection/v2.0/libra_rcnn/libra_faster_rcnn_r101_fpn_1x_coco/libra_faster_rcnn_r101_fpn_1x_coco_20200203_001405.log.json) | -| Faster R-CNN | X-101-64x4d-FPN | pytorch | 1x | 10.8 | 8.5 | 42.7 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/libra_rcnn/libra_faster_rcnn_x101_64x4d_fpn_1x_coco.py) | [model](http://download.openmmlab.com/mmdetection/v2.0/libra_rcnn/libra_faster_rcnn_x101_64x4d_fpn_1x_coco/libra_faster_rcnn_x101_64x4d_fpn_1x_coco_20200315-3a7d0488.pth) | [log](http://download.openmmlab.com/mmdetection/v2.0/libra_rcnn/libra_faster_rcnn_x101_64x4d_fpn_1x_coco/libra_faster_rcnn_x101_64x4d_fpn_1x_coco_20200315_231625.log.json) | -| RetinaNet | R-50-FPN | pytorch | 1x | 4.2 | 17.7 | 37.6 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/libra_rcnn/libra_retinanet_r50_fpn_1x_coco.py) | [model](http://download.openmmlab.com/mmdetection/v2.0/libra_rcnn/libra_retinanet_r50_fpn_1x_coco/libra_retinanet_r50_fpn_1x_coco_20200205-804d94ce.pth) | [log](http://download.openmmlab.com/mmdetection/v2.0/libra_rcnn/libra_retinanet_r50_fpn_1x_coco/libra_retinanet_r50_fpn_1x_coco_20200205_112757.log.json) | +| Faster R-CNN | R-101-FPN | pytorch | 1x | 6.5 | 14.4 | 40.1 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/libra_rcnn/libra_faster_rcnn_r101_fpn_1x_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/libra_rcnn/libra_faster_rcnn_r101_fpn_1x_coco/libra_faster_rcnn_r101_fpn_1x_coco_20200203-8dba6a5a.pth) | [log](https://download.openmmlab.com/mmdetection/v2.0/libra_rcnn/libra_faster_rcnn_r101_fpn_1x_coco/libra_faster_rcnn_r101_fpn_1x_coco_20200203_001405.log.json) | +| Faster R-CNN | X-101-64x4d-FPN | pytorch | 1x | 10.8 | 8.5 | 42.7 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/libra_rcnn/libra_faster_rcnn_x101_64x4d_fpn_1x_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/libra_rcnn/libra_faster_rcnn_x101_64x4d_fpn_1x_coco/libra_faster_rcnn_x101_64x4d_fpn_1x_coco_20200315-3a7d0488.pth) | [log](https://download.openmmlab.com/mmdetection/v2.0/libra_rcnn/libra_faster_rcnn_x101_64x4d_fpn_1x_coco/libra_faster_rcnn_x101_64x4d_fpn_1x_coco_20200315_231625.log.json) | +| RetinaNet | R-50-FPN | pytorch | 1x | 4.2 | 17.7 | 37.6 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/libra_rcnn/libra_retinanet_r50_fpn_1x_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/libra_rcnn/libra_retinanet_r50_fpn_1x_coco/libra_retinanet_r50_fpn_1x_coco_20200205-804d94ce.pth) | [log](https://download.openmmlab.com/mmdetection/v2.0/libra_rcnn/libra_retinanet_r50_fpn_1x_coco/libra_retinanet_r50_fpn_1x_coco_20200205_112757.log.json) | diff --git a/configs/libra_rcnn/libra_faster_rcnn_r101_fpn_1x_coco.py b/configs/libra_rcnn/libra_faster_rcnn_r101_fpn_1x_coco.py index 8e36c9b..e899706 100644 --- a/configs/libra_rcnn/libra_faster_rcnn_r101_fpn_1x_coco.py +++ b/configs/libra_rcnn/libra_faster_rcnn_r101_fpn_1x_coco.py @@ -1,2 +1,6 @@ _base_ = './libra_faster_rcnn_r50_fpn_1x_coco.py' -model = dict(pretrained='torchvision://resnet101', backbone=dict(depth=101)) +model = dict( + backbone=dict( + depth=101, + init_cfg=dict(type='Pretrained', + checkpoint='torchvision://resnet101'))) diff --git a/configs/libra_rcnn/libra_faster_rcnn_x101_64x4d_fpn_1x_coco.py b/configs/libra_rcnn/libra_faster_rcnn_x101_64x4d_fpn_1x_coco.py index e945532..06740a7 100644 --- a/configs/libra_rcnn/libra_faster_rcnn_x101_64x4d_fpn_1x_coco.py +++ b/configs/libra_rcnn/libra_faster_rcnn_x101_64x4d_fpn_1x_coco.py @@ -1,6 +1,5 @@ _base_ = './libra_faster_rcnn_r50_fpn_1x_coco.py' model = dict( - pretrained='open-mmlab://resnext101_64x4d', backbone=dict( type='ResNeXt', depth=101, @@ -10,4 +9,6 @@ out_indices=(0, 1, 2, 3), frozen_stages=1, norm_cfg=dict(type='BN', requires_grad=True), - style='pytorch')) + style='pytorch', + init_cfg=dict( + type='Pretrained', checkpoint='open-mmlab://resnext101_64x4d'))) diff --git a/configs/libra_rcnn/metafile.yml b/configs/libra_rcnn/metafile.yml new file mode 100644 index 0000000..8c32795 --- /dev/null +++ b/configs/libra_rcnn/metafile.yml @@ -0,0 +1,99 @@ +Collections: + - Name: Libra R-CNN + Metadata: + Training Data: COCO + Training Techniques: + - IoU-Balanced Sampling + - SGD with Momentum + - Weight Decay + Training Resources: 8x V100 GPUs + Architecture: + - Balanced Feature Pyramid + Paper: + URL: https://arxiv.org/abs/1904.02701 + Title: 'Libra R-CNN: Towards Balanced Learning for Object Detection' + README: configs/libra_rcnn/README.md + Code: + URL: https://github.com/open-mmlab/mmdetection/blob/v2.0.0/mmdet/models/necks/bfp.py#L10 + Version: v2.0.0 + +Models: + - Name: libra_faster_rcnn_r50_fpn_1x_coco + In Collection: Libra R-CNN + Config: configs/libra_rcnn/libra_faster_rcnn_r50_fpn_1x_coco.py + Metadata: + Training Memory (GB): 4.6 + inference time (ms/im): + - value: 52.63 + hardware: V100 + backend: PyTorch + batch size: 1 + mode: FP32 + resolution: (800, 1333) + Epochs: 12 + Results: + - Task: Object Detection + Dataset: COCO + Metrics: + box AP: 38.3 + Weights: https://download.openmmlab.com/mmdetection/v2.0/libra_rcnn/libra_faster_rcnn_r50_fpn_1x_coco/libra_faster_rcnn_r50_fpn_1x_coco_20200130-3afee3a9.pth + + - Name: libra_faster_rcnn_r101_fpn_1x_coco + In Collection: Libra R-CNN + Config: configs/libra_rcnn/libra_faster_rcnn_r101_fpn_1x_coco.py + Metadata: + Training Memory (GB): 6.5 + inference time (ms/im): + - value: 69.44 + hardware: V100 + backend: PyTorch + batch size: 1 + mode: FP32 + resolution: (800, 1333) + Epochs: 12 + Results: + - Task: Object Detection + Dataset: COCO + Metrics: + box AP: 40.1 + Weights: https://download.openmmlab.com/mmdetection/v2.0/libra_rcnn/libra_faster_rcnn_r101_fpn_1x_coco/libra_faster_rcnn_r101_fpn_1x_coco_20200203-8dba6a5a.pth + + - Name: libra_faster_rcnn_x101_64x4d_fpn_1x_coco + In Collection: Libra R-CNN + Config: configs/libra_rcnn/libra_faster_rcnn_x101_64x4d_fpn_1x_coco.py + Metadata: + Training Memory (GB): 10.8 + inference time (ms/im): + - value: 117.65 + hardware: V100 + backend: PyTorch + batch size: 1 + mode: FP32 + resolution: (800, 1333) + Epochs: 12 + Results: + - Task: Object Detection + Dataset: COCO + Metrics: + box AP: 42.7 + Weights: https://download.openmmlab.com/mmdetection/v2.0/libra_rcnn/libra_faster_rcnn_x101_64x4d_fpn_1x_coco/libra_faster_rcnn_x101_64x4d_fpn_1x_coco_20200315-3a7d0488.pth + + - Name: libra_retinanet_r50_fpn_1x_coco + In Collection: Libra R-CNN + Config: configs/libra_rcnn/libra_retinanet_r50_fpn_1x_coco.py + Metadata: + Training Memory (GB): 4.2 + inference time (ms/im): + - value: 56.5 + hardware: V100 + backend: PyTorch + batch size: 1 + mode: FP32 + resolution: (800, 1333) + Epochs: 12 + Results: + - Task: Object Detection + Dataset: COCO + Metrics: + box AP: 37.6 + Weights: https://download.openmmlab.com/mmdetection/v2.0/libra_rcnn/libra_retinanet_r50_fpn_1x_coco/libra_retinanet_r50_fpn_1x_coco_20200205-804d94ce.pth diff --git a/configs/lvis/README.md b/configs/lvis/README.md index 3276803..157e872 100644 --- a/configs/lvis/README.md +++ b/configs/lvis/README.md @@ -2,7 +2,7 @@ ## Introduction -[DATASET] + ```latex @inproceedings{gupta2019lvis, @@ -19,14 +19,7 @@ * Run following scripts to install our forked lvis-api. ```shell - # mmlvis is fully compatible with official lvis - pip install mmlvis - ``` - - or - - ```shell - pip install -r requirements/optional.txt + pip install git+https://github.com/lvis-dataset/lvis-api.git ``` * All experiments use oversample strategy [here](../../docs/tutorials/new_dataset.md#class-balanced-dataset) with oversample threshold `1e-3`. @@ -36,16 +29,16 @@ | Backbone | Style | Lr schd | Mem (GB) | Inf time (fps) | box AP | mask AP | Config | Download | | :-------------: | :-----: | :-----: | :------: | :------------: | :----: | :-----: | :------: |:--------: | -| R-50-FPN | pytorch | 2x | - | - | 26.1 | 25.9 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/lvis/mask_rcnn_r50_fpn_sample1e-3_mstrain_2x_lvis_v0.5.py) | [model](http://download.openmmlab.com/mmdetection/v2.0/lvis/mask_rcnn_r50_fpn_sample1e-3_mstrain_2x_lvis/mask_rcnn_r50_fpn_sample1e-3_mstrain_2x_lvis-dbd06831.pth) | [log](http://download.openmmlab.com/mmdetection/v2.0/lvis/mask_rcnn_r50_fpn_sample1e-3_mstrain_2x_lvis/mask_rcnn_r50_fpn_sample1e-3_mstrain_2x_lvis_20200531_160435.log.json) | -| R-101-FPN | pytorch | 2x | - | - | 27.1 | 27.0 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/lvis/mask_rcnn_r101_fpn_sample1e-3_mstrain_2x_lvis_v0.5.py) | [model](http://download.openmmlab.com/mmdetection/v2.0/lvis/mask_rcnn_r101_fpn_sample1e-3_mstrain_2x_lvis/mask_rcnn_r101_fpn_sample1e-3_mstrain_2x_lvis-54582ee2.pth) | [log](http://download.openmmlab.com/mmdetection/v2.0/lvis/mask_rcnn_r101_fpn_sample1e-3_mstrain_2x_lvis/mask_rcnn_r101_fpn_sample1e-3_mstrain_2x_lvis_20200601_134748.log.json) | -| X-101-32x4d-FPN | pytorch | 2x | - | - | 26.7 | 26.9 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/lvis/mask_rcnn_x101_32x4d_fpn_sample1e-3_mstrain_2x_lvis_v0.5.py) | [model](http://download.openmmlab.com/mmdetection/v2.0/lvis/mask_rcnn_x101_32x4d_fpn_sample1e-3_mstrain_2x_lvis/mask_rcnn_x101_32x4d_fpn_sample1e-3_mstrain_2x_lvis-3cf55ea2.pth) | [log](http://download.openmmlab.com/mmdetection/v2.0/lvis/mask_rcnn_x101_32x4d_fpn_sample1e-3_mstrain_2x_lvis/mask_rcnn_x101_32x4d_fpn_sample1e-3_mstrain_2x_lvis_20200531_221749.log.json) | -| X-101-64x4d-FPN | pytorch | 2x | - | - | 26.4 | 26.0 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/lvis/mask_rcnn_x101_64x4d_fpn_sample1e-3_mstrain_2x_lvis_v0.5.py) | [model](http://download.openmmlab.com/mmdetection/v2.0/lvis/mask_rcnn_x101_64x4d_fpn_sample1e-3_mstrain_2x_lvis/mask_rcnn_x101_64x4d_fpn_sample1e-3_mstrain_2x_lvis-1c99a5ad.pth) | [log](http://download.openmmlab.com/mmdetection/v2.0/lvis/mask_rcnn_x101_64x4d_fpn_sample1e-3_mstrain_2x_lvis/mask_rcnn_x101_64x4d_fpn_sample1e-3_mstrain_2x_lvis_20200601_194651.log.json) | +| R-50-FPN | pytorch | 2x | - | - | 26.1 | 25.9 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/lvis/mask_rcnn_r50_fpn_sample1e-3_mstrain_2x_lvis_v0.5.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/lvis/mask_rcnn_r50_fpn_sample1e-3_mstrain_2x_lvis/mask_rcnn_r50_fpn_sample1e-3_mstrain_2x_lvis-dbd06831.pth) | [log](https://download.openmmlab.com/mmdetection/v2.0/lvis/mask_rcnn_r50_fpn_sample1e-3_mstrain_2x_lvis/mask_rcnn_r50_fpn_sample1e-3_mstrain_2x_lvis_20200531_160435.log.json) | +| R-101-FPN | pytorch | 2x | - | - | 27.1 | 27.0 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/lvis/mask_rcnn_r101_fpn_sample1e-3_mstrain_2x_lvis_v0.5.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/lvis/mask_rcnn_r101_fpn_sample1e-3_mstrain_2x_lvis/mask_rcnn_r101_fpn_sample1e-3_mstrain_2x_lvis-54582ee2.pth) | [log](https://download.openmmlab.com/mmdetection/v2.0/lvis/mask_rcnn_r101_fpn_sample1e-3_mstrain_2x_lvis/mask_rcnn_r101_fpn_sample1e-3_mstrain_2x_lvis_20200601_134748.log.json) | +| X-101-32x4d-FPN | pytorch | 2x | - | - | 26.7 | 26.9 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/lvis/mask_rcnn_x101_32x4d_fpn_sample1e-3_mstrain_2x_lvis_v0.5.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/lvis/mask_rcnn_x101_32x4d_fpn_sample1e-3_mstrain_2x_lvis/mask_rcnn_x101_32x4d_fpn_sample1e-3_mstrain_2x_lvis-3cf55ea2.pth) | [log](https://download.openmmlab.com/mmdetection/v2.0/lvis/mask_rcnn_x101_32x4d_fpn_sample1e-3_mstrain_2x_lvis/mask_rcnn_x101_32x4d_fpn_sample1e-3_mstrain_2x_lvis_20200531_221749.log.json) | +| X-101-64x4d-FPN | pytorch | 2x | - | - | 26.4 | 26.0 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/lvis/mask_rcnn_x101_64x4d_fpn_sample1e-3_mstrain_2x_lvis_v0.5.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/lvis/mask_rcnn_x101_64x4d_fpn_sample1e-3_mstrain_2x_lvis/mask_rcnn_x101_64x4d_fpn_sample1e-3_mstrain_2x_lvis-1c99a5ad.pth) | [log](https://download.openmmlab.com/mmdetection/v2.0/lvis/mask_rcnn_x101_64x4d_fpn_sample1e-3_mstrain_2x_lvis/mask_rcnn_x101_64x4d_fpn_sample1e-3_mstrain_2x_lvis_20200601_194651.log.json) | ## Results and models of LVIS v1 | Backbone | Style | Lr schd | Mem (GB) | Inf time (fps) | box AP | mask AP | Config | Download | | :-------------: | :-----: | :-----: | :------: | :------------: | :----: | :-----: | :------: | :--------: | -| R-50-FPN | pytorch | 1x | 9.1 | - | 22.5 | 21.7 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/lvis/mask_rcnn_r50_fpn_sample1e-3_mstrain_1x_lvis_v1.py) | [model](http://download.openmmlab.com/mmdetection/v2.0/lvis/mask_rcnn_r50_fpn_sample1e-3_mstrain_1x_lvis_v1/mask_rcnn_r50_fpn_sample1e-3_mstrain_1x_lvis_v1-aa78ac3d.pth) | [log](http://download.openmmlab.com/mmdetection/v2.0/lvis/mask_rcnn_r50_fpn_sample1e-3_mstrain_1x_lvis_v1/mask_rcnn_r50_fpn_sample1e-3_mstrain_1x_lvis_v1-20200829_061305.log.json) | -| R-101-FPN | pytorch | 1x | 10.8 | - | 24.6 | 23.6 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/lvis/mask_rcnn_r101_fpn_sample1e-3_mstrain_1x_lvis_v1.py) | [model](http://download.openmmlab.com/mmdetection/v2.0/lvis/mask_rcnn_r101_fpn_sample1e-3_mstrain_1x_lvis_v1/mask_rcnn_r101_fpn_sample1e-3_mstrain_1x_lvis_v1-ec55ce32.pth) | [log](http://download.openmmlab.com/mmdetection/v2.0/lvis/mask_rcnn_r101_fpn_sample1e-3_mstrain_1x_lvis_v1/mask_rcnn_r101_fpn_sample1e-3_mstrain_1x_lvis_v1-20200829_070959.log.json) | -| X-101-32x4d-FPN | pytorch | 1x | 11.8 | - | 26.7 | 25.5 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/lvis/mask_rcnn_x101_32x4d_fpn_sample1e-3_mstrain_1x_lvis_v1.py) | [model](http://download.openmmlab.com/mmdetection/v2.0/lvis/mask_rcnn_x101_32x4d_fpn_sample1e-3_mstrain_1x_lvis_v1/mask_rcnn_x101_32x4d_fpn_sample1e-3_mstrain_1x_lvis_v1-ebbc5c81.pth) | [log](http://download.openmmlab.com/mmdetection/v2.0/lvis/mask_rcnn_x101_32x4d_fpn_sample1e-3_mstrain_1x_lvis_v1/mask_rcnn_x101_32x4d_fpn_sample1e-3_mstrain_1x_lvis_v1-20200829_071317.log.json) | -| X-101-64x4d-FPN | pytorch | 1x | 14.6 | - | 27.2 | 25.8 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/lvis/mask_rcnn_x101_64x4d_fpn_sample1e-3_mstrain_1x_lvis_v1.py) | [model](http://download.openmmlab.com/mmdetection/v2.0/lvis/mask_rcnn_x101_64x4d_fpn_sample1e-3_mstrain_1x_lvis_v1/mask_rcnn_x101_64x4d_fpn_sample1e-3_mstrain_1x_lvis_v1-43d9edfe.pth) | [log](http://download.openmmlab.com/mmdetection/v2.0/lvis/mask_rcnn_x101_64x4d_fpn_sample1e-3_mstrain_1x_lvis_v1/mask_rcnn_x101_64x4d_fpn_sample1e-3_mstrain_1x_lvis_v1-20200830_060206.log.json) | +| R-50-FPN | pytorch | 1x | 9.1 | - | 22.5 | 21.7 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/lvis/mask_rcnn_r50_fpn_sample1e-3_mstrain_1x_lvis_v1.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/lvis/mask_rcnn_r50_fpn_sample1e-3_mstrain_1x_lvis_v1/mask_rcnn_r50_fpn_sample1e-3_mstrain_1x_lvis_v1-aa78ac3d.pth) | [log](https://download.openmmlab.com/mmdetection/v2.0/lvis/mask_rcnn_r50_fpn_sample1e-3_mstrain_1x_lvis_v1/mask_rcnn_r50_fpn_sample1e-3_mstrain_1x_lvis_v1-20200829_061305.log.json) | +| R-101-FPN | pytorch | 1x | 10.8 | - | 24.6 | 23.6 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/lvis/mask_rcnn_r101_fpn_sample1e-3_mstrain_1x_lvis_v1.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/lvis/mask_rcnn_r101_fpn_sample1e-3_mstrain_1x_lvis_v1/mask_rcnn_r101_fpn_sample1e-3_mstrain_1x_lvis_v1-ec55ce32.pth) | [log](https://download.openmmlab.com/mmdetection/v2.0/lvis/mask_rcnn_r101_fpn_sample1e-3_mstrain_1x_lvis_v1/mask_rcnn_r101_fpn_sample1e-3_mstrain_1x_lvis_v1-20200829_070959.log.json) | +| X-101-32x4d-FPN | pytorch | 1x | 11.8 | - | 26.7 | 25.5 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/lvis/mask_rcnn_x101_32x4d_fpn_sample1e-3_mstrain_1x_lvis_v1.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/lvis/mask_rcnn_x101_32x4d_fpn_sample1e-3_mstrain_1x_lvis_v1/mask_rcnn_x101_32x4d_fpn_sample1e-3_mstrain_1x_lvis_v1-ebbc5c81.pth) | [log](https://download.openmmlab.com/mmdetection/v2.0/lvis/mask_rcnn_x101_32x4d_fpn_sample1e-3_mstrain_1x_lvis_v1/mask_rcnn_x101_32x4d_fpn_sample1e-3_mstrain_1x_lvis_v1-20200829_071317.log.json) | +| X-101-64x4d-FPN | pytorch | 1x | 14.6 | - | 27.2 | 25.8 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/lvis/mask_rcnn_x101_64x4d_fpn_sample1e-3_mstrain_1x_lvis_v1.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/lvis/mask_rcnn_x101_64x4d_fpn_sample1e-3_mstrain_1x_lvis_v1/mask_rcnn_x101_64x4d_fpn_sample1e-3_mstrain_1x_lvis_v1-43d9edfe.pth) | [log](https://download.openmmlab.com/mmdetection/v2.0/lvis/mask_rcnn_x101_64x4d_fpn_sample1e-3_mstrain_1x_lvis_v1/mask_rcnn_x101_64x4d_fpn_sample1e-3_mstrain_1x_lvis_v1-20200830_060206.log.json) | diff --git a/configs/lvis/mask_rcnn_r101_fpn_sample1e-3_mstrain_1x_lvis_v1.py b/configs/lvis/mask_rcnn_r101_fpn_sample1e-3_mstrain_1x_lvis_v1.py index 1881865..0f017f5 100644 --- a/configs/lvis/mask_rcnn_r101_fpn_sample1e-3_mstrain_1x_lvis_v1.py +++ b/configs/lvis/mask_rcnn_r101_fpn_sample1e-3_mstrain_1x_lvis_v1.py @@ -1,2 +1,6 @@ _base_ = './mask_rcnn_r50_fpn_sample1e-3_mstrain_1x_lvis_v1.py' -model = dict(pretrained='torchvision://resnet101', backbone=dict(depth=101)) +model = dict( + backbone=dict( + depth=101, + init_cfg=dict(type='Pretrained', + checkpoint='torchvision://resnet101'))) diff --git a/configs/lvis/mask_rcnn_r101_fpn_sample1e-3_mstrain_2x_lvis_v0.5.py b/configs/lvis/mask_rcnn_r101_fpn_sample1e-3_mstrain_2x_lvis_v0.5.py index 2d2816c..637f4a6 100644 --- a/configs/lvis/mask_rcnn_r101_fpn_sample1e-3_mstrain_2x_lvis_v0.5.py +++ b/configs/lvis/mask_rcnn_r101_fpn_sample1e-3_mstrain_2x_lvis_v0.5.py @@ -1,2 +1,6 @@ _base_ = './mask_rcnn_r50_fpn_sample1e-3_mstrain_2x_lvis_v0.5.py' -model = dict(pretrained='torchvision://resnet101', backbone=dict(depth=101)) +model = dict( + backbone=dict( + depth=101, + init_cfg=dict(type='Pretrained', + checkpoint='torchvision://resnet101'))) diff --git a/configs/lvis/mask_rcnn_x101_32x4d_fpn_sample1e-3_mstrain_1x_lvis_v1.py b/configs/lvis/mask_rcnn_x101_32x4d_fpn_sample1e-3_mstrain_1x_lvis_v1.py index 5abcc2e..a6115c1 100644 --- a/configs/lvis/mask_rcnn_x101_32x4d_fpn_sample1e-3_mstrain_1x_lvis_v1.py +++ b/configs/lvis/mask_rcnn_x101_32x4d_fpn_sample1e-3_mstrain_1x_lvis_v1.py @@ -1,6 +1,5 @@ _base_ = './mask_rcnn_r50_fpn_sample1e-3_mstrain_1x_lvis_v1.py' model = dict( - pretrained='open-mmlab://resnext101_32x4d', backbone=dict( type='ResNeXt', depth=101, @@ -10,4 +9,6 @@ out_indices=(0, 1, 2, 3), frozen_stages=1, norm_cfg=dict(type='BN', requires_grad=True), - style='pytorch')) + style='pytorch', + init_cfg=dict( + type='Pretrained', checkpoint='open-mmlab://resnext101_32x4d'))) diff --git a/configs/lvis/mask_rcnn_x101_32x4d_fpn_sample1e-3_mstrain_2x_lvis_v0.5.py b/configs/lvis/mask_rcnn_x101_32x4d_fpn_sample1e-3_mstrain_2x_lvis_v0.5.py index 439c39a..96b6252 100644 --- a/configs/lvis/mask_rcnn_x101_32x4d_fpn_sample1e-3_mstrain_2x_lvis_v0.5.py +++ b/configs/lvis/mask_rcnn_x101_32x4d_fpn_sample1e-3_mstrain_2x_lvis_v0.5.py @@ -1,6 +1,5 @@ _base_ = './mask_rcnn_r50_fpn_sample1e-3_mstrain_2x_lvis_v0.5.py' model = dict( - pretrained='open-mmlab://resnext101_32x4d', backbone=dict( type='ResNeXt', depth=101, @@ -10,4 +9,6 @@ out_indices=(0, 1, 2, 3), frozen_stages=1, norm_cfg=dict(type='BN', requires_grad=True), - style='pytorch')) + style='pytorch', + init_cfg=dict( + type='Pretrained', checkpoint='open-mmlab://resnext101_32x4d'))) diff --git a/configs/lvis/mask_rcnn_x101_64x4d_fpn_sample1e-3_mstrain_1x_lvis_v1.py b/configs/lvis/mask_rcnn_x101_64x4d_fpn_sample1e-3_mstrain_1x_lvis_v1.py index f77adba..0f95a73 100644 --- a/configs/lvis/mask_rcnn_x101_64x4d_fpn_sample1e-3_mstrain_1x_lvis_v1.py +++ b/configs/lvis/mask_rcnn_x101_64x4d_fpn_sample1e-3_mstrain_1x_lvis_v1.py @@ -1,6 +1,5 @@ _base_ = './mask_rcnn_r50_fpn_sample1e-3_mstrain_1x_lvis_v1.py' model = dict( - pretrained='open-mmlab://resnext101_64x4d', backbone=dict( type='ResNeXt', depth=101, @@ -10,4 +9,6 @@ out_indices=(0, 1, 2, 3), frozen_stages=1, norm_cfg=dict(type='BN', requires_grad=True), - style='pytorch')) + style='pytorch', + init_cfg=dict( + type='Pretrained', checkpoint='open-mmlab://resnext101_64x4d'))) diff --git a/configs/lvis/mask_rcnn_x101_64x4d_fpn_sample1e-3_mstrain_2x_lvis_v0.5.py b/configs/lvis/mask_rcnn_x101_64x4d_fpn_sample1e-3_mstrain_2x_lvis_v0.5.py index 2136255..986acda 100644 --- a/configs/lvis/mask_rcnn_x101_64x4d_fpn_sample1e-3_mstrain_2x_lvis_v0.5.py +++ b/configs/lvis/mask_rcnn_x101_64x4d_fpn_sample1e-3_mstrain_2x_lvis_v0.5.py @@ -1,6 +1,5 @@ _base_ = './mask_rcnn_r50_fpn_sample1e-3_mstrain_2x_lvis_v0.5.py' model = dict( - pretrained='open-mmlab://resnext101_64x4d', backbone=dict( type='ResNeXt', depth=101, @@ -10,4 +9,6 @@ out_indices=(0, 1, 2, 3), frozen_stages=1, norm_cfg=dict(type='BN', requires_grad=True), - style='pytorch')) + style='pytorch', + init_cfg=dict( + type='Pretrained', checkpoint='open-mmlab://resnext101_64x4d'))) diff --git a/configs/mask_rcnn/README.md b/configs/mask_rcnn/README.md index fd1dc5b..77cfabe 100644 --- a/configs/mask_rcnn/README.md +++ b/configs/mask_rcnn/README.md @@ -2,7 +2,7 @@ ## Introduction -[ALGORITHM] + ```latex @article{He_2017, @@ -19,16 +19,16 @@ | Backbone | Style | Lr schd | Mem (GB) | Inf time (fps) | box AP | mask AP | Config | Download | | :-------------: | :-----: | :-----: | :------: | :------------: | :----: | :-----: | :------: | :--------: | -| R-50-FPN | caffe | 1x | 4.3 | | 38.0 | 34.4 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/mask_rcnn/mask_rcnn_r50_caffe_fpn_1x_coco.py) | [model](http://download.openmmlab.com/mmdetection/v2.0/mask_rcnn/mask_rcnn_r50_caffe_fpn_1x_coco/mask_rcnn_r50_caffe_fpn_1x_coco_bbox_mAP-0.38__segm_mAP-0.344_20200504_231812-0ebd1859.pth) | [log](http://download.openmmlab.com/mmdetection/v2.0/mask_rcnn/mask_rcnn_r50_caffe_fpn_1x_coco/mask_rcnn_r50_caffe_fpn_1x_coco_20200504_231812.log.json) | -| R-50-FPN | pytorch | 1x | 4.4 | 16.1 | 38.2 | 34.7 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/mask_rcnn/mask_rcnn_r50_fpn_1x_coco.py) | [model](http://download.openmmlab.com/mmdetection/v2.0/mask_rcnn/mask_rcnn_r50_fpn_1x_coco/mask_rcnn_r50_fpn_1x_coco_20200205-d4b0c5d6.pth) | [log](http://download.openmmlab.com/mmdetection/v2.0/mask_rcnn/mask_rcnn_r50_fpn_1x_coco/mask_rcnn_r50_fpn_1x_coco_20200205_050542.log.json) | -| R-50-FPN | pytorch | 2x | - | - | 39.2 | 35.4 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/mask_rcnn/mask_rcnn_r50_fpn_2x_coco.py) | [model](http://download.openmmlab.com/mmdetection/v2.0/mask_rcnn/mask_rcnn_r50_fpn_2x_coco/mask_rcnn_r50_fpn_2x_coco_bbox_mAP-0.392__segm_mAP-0.354_20200505_003907-3e542a40.pth) | [log](http://download.openmmlab.com/mmdetection/v2.0/mask_rcnn/mask_rcnn_r50_fpn_2x_coco/mask_rcnn_r50_fpn_2x_coco_20200505_003907.log.json) | -| R-101-FPN | caffe | 1x | | | 40.4 | 36.4 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/mask_rcnn/mask_rcnn_r101_caffe_fpn_1x_coco.py) | [model](http://download.openmmlab.com/mmdetection/v2.0/mask_rcnn/mask_rcnn_r101_caffe_fpn_1x_coco/mask_rcnn_r101_caffe_fpn_1x_coco_20200601_095758-805e06c1.pth) | [log](http://download.openmmlab.com/mmdetection/v2.0/mask_rcnn/mask_rcnn_r101_caffe_fpn_1x_coco/mask_rcnn_r101_caffe_fpn_1x_coco_20200601_095758.log.json)| -| R-101-FPN | pytorch | 1x | 6.4 | 13.5 | 40.0 | 36.1 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/mask_rcnn/mask_rcnn_r101_fpn_1x_coco.py) | [model](http://download.openmmlab.com/mmdetection/v2.0/mask_rcnn/mask_rcnn_r101_fpn_1x_coco/mask_rcnn_r101_fpn_1x_coco_20200204-1efe0ed5.pth) | [log](http://download.openmmlab.com/mmdetection/v2.0/mask_rcnn/mask_rcnn_r101_fpn_1x_coco/mask_rcnn_r101_fpn_1x_coco_20200204_144809.log.json) | -| R-101-FPN | pytorch | 2x | - | - | 40.8 | 36.6 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/mask_rcnn/mask_rcnn_r101_fpn_2x_coco.py) | [model](http://download.openmmlab.com/mmdetection/v2.0/mask_rcnn/mask_rcnn_r101_fpn_2x_coco/mask_rcnn_r101_fpn_2x_coco_bbox_mAP-0.408__segm_mAP-0.366_20200505_071027-14b391c7.pth) | [log](http://download.openmmlab.com/mmdetection/v2.0/mask_rcnn/mask_rcnn_r101_fpn_2x_coco/mask_rcnn_r101_fpn_2x_coco_20200505_071027.log.json) | -| X-101-32x4d-FPN | pytorch | 1x | 7.6 | 11.3 | 41.9 | 37.5 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/mask_rcnn/mask_rcnn_x101_32x4d_fpn_1x_coco.py) | [model](http://download.openmmlab.com/mmdetection/v2.0/mask_rcnn/mask_rcnn_x101_32x4d_fpn_1x_coco/mask_rcnn_x101_32x4d_fpn_1x_coco_20200205-478d0b67.pth) | [log](http://download.openmmlab.com/mmdetection/v2.0/mask_rcnn/mask_rcnn_x101_32x4d_fpn_1x_coco/mask_rcnn_x101_32x4d_fpn_1x_coco_20200205_034906.log.json) | -| X-101-32x4d-FPN | pytorch | 2x | - | - | 42.2 | 37.8 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/mask_rcnn/mask_rcnn_x101_32x4d_fpn_2x_coco.py) | [model](http://download.openmmlab.com/mmdetection/v2.0/mask_rcnn/mask_rcnn_x101_32x4d_fpn_2x_coco/mask_rcnn_x101_32x4d_fpn_2x_coco_bbox_mAP-0.422__segm_mAP-0.378_20200506_004702-faef898c.pth) | [log](http://download.openmmlab.com/mmdetection/v2.0/mask_rcnn/mask_rcnn_x101_32x4d_fpn_2x_coco/mask_rcnn_x101_32x4d_fpn_2x_coco_20200506_004702.log.json) | -| X-101-64x4d-FPN | pytorch | 1x | 10.7 | 8.0 | 42.8 | 38.4 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/mask_rcnn/mask_rcnn_x101_64x4d_fpn_1x_coco.py) | [model](http://download.openmmlab.com/mmdetection/v2.0/mask_rcnn/mask_rcnn_x101_64x4d_fpn_1x_coco/mask_rcnn_x101_64x4d_fpn_1x_coco_20200201-9352eb0d.pth) | [log](http://download.openmmlab.com/mmdetection/v2.0/mask_rcnn/mask_rcnn_x101_64x4d_fpn_1x_coco/mask_rcnn_x101_64x4d_fpn_1x_coco_20200201_124310.log.json) | -| X-101-64x4d-FPN | pytorch | 2x | - | - | 42.7 | 38.1 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/mask_rcnn/mask_rcnn_x101_64x4d_fpn_2x_coco.py) | [model](http://download.openmmlab.com/mmdetection/v2.0/mask_rcnn/mask_rcnn_x101_64x4d_fpn_2x_coco/mask_rcnn_x101_64x4d_fpn_2x_coco_20200509_224208-39d6f70c.pth) | [log](http://download.openmmlab.com/mmdetection/v2.0/mask_rcnn/mask_rcnn_x101_64x4d_fpn_2x_coco/mask_rcnn_x101_64x4d_fpn_2x_coco_20200509_224208.log.json)| +| R-50-FPN | caffe | 1x | 4.3 | | 38.0 | 34.4 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/mask_rcnn/mask_rcnn_r50_caffe_fpn_1x_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/mask_rcnn/mask_rcnn_r50_caffe_fpn_1x_coco/mask_rcnn_r50_caffe_fpn_1x_coco_bbox_mAP-0.38__segm_mAP-0.344_20200504_231812-0ebd1859.pth) | [log](https://download.openmmlab.com/mmdetection/v2.0/mask_rcnn/mask_rcnn_r50_caffe_fpn_1x_coco/mask_rcnn_r50_caffe_fpn_1x_coco_20200504_231812.log.json) | +| R-50-FPN | pytorch | 1x | 4.4 | 16.1 | 38.2 | 34.7 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/mask_rcnn/mask_rcnn_r50_fpn_1x_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/mask_rcnn/mask_rcnn_r50_fpn_1x_coco/mask_rcnn_r50_fpn_1x_coco_20200205-d4b0c5d6.pth) | [log](https://download.openmmlab.com/mmdetection/v2.0/mask_rcnn/mask_rcnn_r50_fpn_1x_coco/mask_rcnn_r50_fpn_1x_coco_20200205_050542.log.json) | +| R-50-FPN | pytorch | 2x | - | - | 39.2 | 35.4 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/mask_rcnn/mask_rcnn_r50_fpn_2x_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/mask_rcnn/mask_rcnn_r50_fpn_2x_coco/mask_rcnn_r50_fpn_2x_coco_bbox_mAP-0.392__segm_mAP-0.354_20200505_003907-3e542a40.pth) | [log](https://download.openmmlab.com/mmdetection/v2.0/mask_rcnn/mask_rcnn_r50_fpn_2x_coco/mask_rcnn_r50_fpn_2x_coco_20200505_003907.log.json) | +| R-101-FPN | caffe | 1x | | | 40.4 | 36.4 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/mask_rcnn/mask_rcnn_r101_caffe_fpn_1x_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/mask_rcnn/mask_rcnn_r101_caffe_fpn_1x_coco/mask_rcnn_r101_caffe_fpn_1x_coco_20200601_095758-805e06c1.pth) | [log](https://download.openmmlab.com/mmdetection/v2.0/mask_rcnn/mask_rcnn_r101_caffe_fpn_1x_coco/mask_rcnn_r101_caffe_fpn_1x_coco_20200601_095758.log.json)| +| R-101-FPN | pytorch | 1x | 6.4 | 13.5 | 40.0 | 36.1 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/mask_rcnn/mask_rcnn_r101_fpn_1x_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/mask_rcnn/mask_rcnn_r101_fpn_1x_coco/mask_rcnn_r101_fpn_1x_coco_20200204-1efe0ed5.pth) | [log](https://download.openmmlab.com/mmdetection/v2.0/mask_rcnn/mask_rcnn_r101_fpn_1x_coco/mask_rcnn_r101_fpn_1x_coco_20200204_144809.log.json) | +| R-101-FPN | pytorch | 2x | - | - | 40.8 | 36.6 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/mask_rcnn/mask_rcnn_r101_fpn_2x_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/mask_rcnn/mask_rcnn_r101_fpn_2x_coco/mask_rcnn_r101_fpn_2x_coco_bbox_mAP-0.408__segm_mAP-0.366_20200505_071027-14b391c7.pth) | [log](https://download.openmmlab.com/mmdetection/v2.0/mask_rcnn/mask_rcnn_r101_fpn_2x_coco/mask_rcnn_r101_fpn_2x_coco_20200505_071027.log.json) | +| X-101-32x4d-FPN | pytorch | 1x | 7.6 | 11.3 | 41.9 | 37.5 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/mask_rcnn/mask_rcnn_x101_32x4d_fpn_1x_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/mask_rcnn/mask_rcnn_x101_32x4d_fpn_1x_coco/mask_rcnn_x101_32x4d_fpn_1x_coco_20200205-478d0b67.pth) | [log](https://download.openmmlab.com/mmdetection/v2.0/mask_rcnn/mask_rcnn_x101_32x4d_fpn_1x_coco/mask_rcnn_x101_32x4d_fpn_1x_coco_20200205_034906.log.json) | +| X-101-32x4d-FPN | pytorch | 2x | - | - | 42.2 | 37.8 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/mask_rcnn/mask_rcnn_x101_32x4d_fpn_2x_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/mask_rcnn/mask_rcnn_x101_32x4d_fpn_2x_coco/mask_rcnn_x101_32x4d_fpn_2x_coco_bbox_mAP-0.422__segm_mAP-0.378_20200506_004702-faef898c.pth) | [log](https://download.openmmlab.com/mmdetection/v2.0/mask_rcnn/mask_rcnn_x101_32x4d_fpn_2x_coco/mask_rcnn_x101_32x4d_fpn_2x_coco_20200506_004702.log.json) | +| X-101-64x4d-FPN | pytorch | 1x | 10.7 | 8.0 | 42.8 | 38.4 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/mask_rcnn/mask_rcnn_x101_64x4d_fpn_1x_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/mask_rcnn/mask_rcnn_x101_64x4d_fpn_1x_coco/mask_rcnn_x101_64x4d_fpn_1x_coco_20200201-9352eb0d.pth) | [log](https://download.openmmlab.com/mmdetection/v2.0/mask_rcnn/mask_rcnn_x101_64x4d_fpn_1x_coco/mask_rcnn_x101_64x4d_fpn_1x_coco_20200201_124310.log.json) | +| X-101-64x4d-FPN | pytorch | 2x | - | - | 42.7 | 38.1 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/mask_rcnn/mask_rcnn_x101_64x4d_fpn_2x_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/mask_rcnn/mask_rcnn_x101_64x4d_fpn_2x_coco/mask_rcnn_x101_64x4d_fpn_2x_coco_20200509_224208-39d6f70c.pth) | [log](https://download.openmmlab.com/mmdetection/v2.0/mask_rcnn/mask_rcnn_x101_64x4d_fpn_2x_coco/mask_rcnn_x101_64x4d_fpn_2x_coco_20200509_224208.log.json)| | X-101-32x8d-FPN | pytorch | 1x | - | - | 42.8 | 38.3 | | ## Pre-trained Models @@ -37,7 +37,12 @@ We also train some models with longer schedules and multi-scale training. The us | Backbone | Style | Lr schd | Mem (GB) | Inf time (fps) | box AP | mask AP | Config | Download | | :-------------: | :-----: | :-----: | :------: | :------------: | :----: | :-----: | :------: | :--------: | -| [R-50-FPN](./mask_rcnn_r50_caffe_fpn_mstrain-poly_2x_coco.py) | caffe | 2x | 4.3 | | 40.3 | 36.5 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/mask_rcnn/mask_rcnn_r50_caffe_fpn_mstrain-poly_2x_coco.py) | [model](http://download.openmmlab.com/mmdetection/v2.0/mask_rcnn/mask_rcnn_r50_caffe_fpn_mstrain-poly_2x_coco/mask_rcnn_r50_caffe_fpn_mstrain-poly_2x_coco_bbox_mAP-0.403__segm_mAP-0.365_20200504_231822-a75c98ce.pth) | [log](http://download.openmmlab.com/mmdetection/v2.0/mask_rcnn/mask_rcnn_r50_caffe_fpn_mstrain-poly_2x_coco/mask_rcnn_r50_caffe_fpn_mstrain-poly_2x_coco_20200504_231822.log.json) -| [R-50-FPN](./mask_rcnn_r50_caffe_fpn_mstrain-poly_3x_coco.py) | caffe | 3x | 4.3 | | 40.8 | 37.0 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/mask_rcnn/mask_rcnn_r50_caffe_fpn_mstrain-poly_3x_coco.py) | [model](http://download.openmmlab.com/mmdetection/v2.0/mask_rcnn/mask_rcnn_r50_caffe_fpn_mstrain-poly_3x_coco/mask_rcnn_r50_caffe_fpn_mstrain-poly_3x_coco_bbox_mAP-0.408__segm_mAP-0.37_20200504_163245-42aa3d00.pth) | [log](http://download.openmmlab.com/mmdetection/v2.0/mask_rcnn/mask_rcnn_r50_caffe_fpn_mstrain-poly_3x_coco/mask_rcnn_r50_caffe_fpn_mstrain-poly_3x_coco_20200504_163245.log.json) +| [R-50-FPN](./mask_rcnn_r50_caffe_fpn_mstrain-poly_2x_coco.py) | caffe | 2x | 4.3 | | 40.3 | 36.5 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/mask_rcnn/mask_rcnn_r50_caffe_fpn_mstrain-poly_2x_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/mask_rcnn/mask_rcnn_r50_caffe_fpn_mstrain-poly_2x_coco/mask_rcnn_r50_caffe_fpn_mstrain-poly_2x_coco_bbox_mAP-0.403__segm_mAP-0.365_20200504_231822-a75c98ce.pth) | [log](https://download.openmmlab.com/mmdetection/v2.0/mask_rcnn/mask_rcnn_r50_caffe_fpn_mstrain-poly_2x_coco/mask_rcnn_r50_caffe_fpn_mstrain-poly_2x_coco_20200504_231822.log.json) +| [R-50-FPN](./mask_rcnn_r50_caffe_fpn_mstrain-poly_3x_coco.py) | caffe | 3x | 4.3 | | 40.8 | 37.0 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/mask_rcnn/mask_rcnn_r50_caffe_fpn_mstrain-poly_3x_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/mask_rcnn/mask_rcnn_r50_caffe_fpn_mstrain-poly_3x_coco/mask_rcnn_r50_caffe_fpn_mstrain-poly_3x_coco_bbox_mAP-0.408__segm_mAP-0.37_20200504_163245-42aa3d00.pth) | [log](https://download.openmmlab.com/mmdetection/v2.0/mask_rcnn/mask_rcnn_r50_caffe_fpn_mstrain-poly_3x_coco/mask_rcnn_r50_caffe_fpn_mstrain-poly_3x_coco_20200504_163245.log.json) +| [R-50-FPN](./mask_rcnn_r50_fpn_mstrain-poly_3x_coco.py) | pytorch| 3x | 4.1 | | 40.9 | 37.1 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/mask_rcnn/mask_rcnn_r50_fpn_mstrain-poly_3x_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/mask_rcnn/mask_rcnn_r50_fpn_mstrain-poly_3x_coco/mask_rcnn_r50_fpn_mstrain-poly_3x_coco_20210524_201154-21b550bb.pth) | [log](https://download.openmmlab.com/mmdetection/v2.0/mask_rcnn/mask_rcnn_r50_fpn_mstrain-poly_3x_coco/mask_rcnn_r50_fpn_mstrain-poly_3x_coco_20210524_201154.log.json) +| [R-101-FPN](./mask_rcnn_r101_caffe_fpn_mstrain-poly_3x_coco.py) | caffe | 3x | 5.9 | | 42.9 | 38.5 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/mask_rcnn/mask_rcnn_r101_caffe_fpn_mstrain-poly_3x_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/mask_rcnn/mask_rcnn_r101_caffe_fpn_mstrain-poly_3x_coco/mask_rcnn_r101_caffe_fpn_mstrain-poly_3x_coco_20210526_132339-3c33ce02.pth) | [log](https://download.openmmlab.com/mmdetection/v2.0/mask_rcnn_r101_caffe_fpn_mstrain-poly_3x_coco/mask_rcnn_r101_caffe_fpn_mstrain-poly_3x_coco_20210526_132339.log.json) +| [R-101-FPN](./mask_rcnn_r101_fpn_mstrain-poly_3x_coco.py) | pytorch| 3x | 6.1 | | 42.7 | 38.5 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/mask_rcnn/mask_rcnn_r101_fpn_mstrain-poly_3x_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/mask_rcnn/mask_rcnn_r101_fpn_mstrain-poly_3x_coco/mask_rcnn_r101_fpn_mstrain-poly_3x_coco_20210524_200244-5675c317.pth) | [log](https://download.openmmlab.com/mmdetection/v2.0/mask_rcnn/mask_rcnn_r101_fpn_mstrain-poly_3x_coco/mask_rcnn_r101_fpn_mstrain-poly_3x_coco_20210524_200244.log.json) +| [x101-32x4d-FPN](./mask_rcnn_x101_32x4d_fpn_mstrain-poly_3x_coco.py) | pytorch| 3x | 7.3 | | 43.6 | 39.0 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/mask_rcnn/mask_rcnn_x101_32x4d_fpn_mstrain-poly_3x_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/mask_rcnn/mask_rcnn_x101_32x4d_fpn_mstrain-poly_3x_coco/mask_rcnn_x101_32x4d_fpn_mstrain-poly_3x_coco_20210524_201410-abcd7859.pth) | [log](https://download.openmmlab.com/mmdetection/v2.0/mask_rcnn/mask_rcnn_x101_32x4d_fpn_mstrain-poly_3x_coco/mask_rcnn_x101_32x4d_fpn_mstrain-poly_3x_coco_20210524_201410.log.json) | [X-101-32x8d-FPN](./mask_rcnn_x101_32x8d_fpn_mstrain-poly_3x_coco.py) | pytorch | 1x | - | | 43.6 | 39.0 | -| [X-101-32x8d-FPN](./mask_rcnn_x101_32x8d_fpn_mstrain-poly_3x_coco.py) | pytorch | 3x | - | | 44.0 | 39.3 | +| [X-101-32x8d-FPN](./mask_rcnn_x101_32x8d_fpn_mstrain-poly_3x_coco.py) | pytorch | 3x | 10.3 | | 44.3 | 39.5 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/mask_rcnn/mask_rcnn_x101_32x8d_fpn_mstrain-poly_3x_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/mask_rcnn/mask_rcnn_x101_32x8d_fpn_mstrain-poly_3x_coco/mask_rcnn_x101_32x8d_fpn_mstrain-poly_3x_coco_20210607_161042-8bd2c639.pth) | [log](https://download.openmmlab.com/mmdetection/v2.0/mask_rcnn/mask_rcnn_x101_32x8d_fpn_mstrain-poly_3x_coco/mask_rcnn_x101_32x8d_fpn_mstrain-poly_3x_coco_20210607_161042.log.json) +| [X-101-64x4d-FPN](./mask_rcnn_x101_64x4d_fpn_mstrain-poly_3x_coco.py) | pytorch | 3x | 10.4 | | 44.5 | 39.7 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/mask_rcnn/mask_rcnn_x101_64x4d_fpn_mstrain-poly_3x_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/mask_rcnn/mask_rcnn_x101_64x4d_fpn_mstrain-poly_3x_coco/mask_rcnn_x101_64x4d_fpn_mstrain-poly_3x_coco_20210526_120447-c376f129.pth) | [log](https://download.openmmlab.com/mmdetection/v2.0/mask_rcnn/mask_rcnn_x101_64x4d_fpn_mstrain-poly_3x_coco/mask_rcnn_x101_64x4d_fpn_mstrain-poly_3x_coco_20210526_120447.log.json) diff --git a/configs/mask_rcnn/mask_rcnn_r101_caffe_fpn_1x_coco.py b/configs/mask_rcnn/mask_rcnn_r101_caffe_fpn_1x_coco.py index 230181c..95b324f 100644 --- a/configs/mask_rcnn/mask_rcnn_r101_caffe_fpn_1x_coco.py +++ b/configs/mask_rcnn/mask_rcnn_r101_caffe_fpn_1x_coco.py @@ -1,4 +1,7 @@ _base_ = './mask_rcnn_r50_caffe_fpn_1x_coco.py' model = dict( - pretrained='open-mmlab://detectron2/resnet101_caffe', - backbone=dict(depth=101)) + backbone=dict( + depth=101, + init_cfg=dict( + type='Pretrained', + checkpoint='open-mmlab://detectron2/resnet101_caffe'))) diff --git a/configs/mask_rcnn/mask_rcnn_r101_caffe_fpn_mstrain-poly_3x_coco.py b/configs/mask_rcnn/mask_rcnn_r101_caffe_fpn_mstrain-poly_3x_coco.py new file mode 100644 index 0000000..e39781d --- /dev/null +++ b/configs/mask_rcnn/mask_rcnn_r101_caffe_fpn_mstrain-poly_3x_coco.py @@ -0,0 +1,55 @@ +_base_ = [ + '../common/mstrain-poly_3x_coco_instance.py', + '../_base_/models/mask_rcnn_r50_fpn.py' +] + +model = dict( + backbone=dict( + depth=101, + norm_cfg=dict(requires_grad=False), + norm_eval=True, + style='caffe', + init_cfg=dict( + type='Pretrained', + checkpoint='open-mmlab://detectron2/resnet101_caffe'))) +# use caffe img_norm +img_norm_cfg = dict( + mean=[103.530, 116.280, 123.675], std=[1.0, 1.0, 1.0], to_rgb=False) +train_pipeline = [ + dict(type='LoadImageFromFile'), + dict( + type='LoadAnnotations', + with_bbox=True, + with_mask=True, + poly2mask=False), + dict( + type='Resize', + img_scale=[(1333, 640), (1333, 800)], + multiscale_mode='range', + keep_ratio=True), + dict(type='RandomFlip', flip_ratio=0.5), + dict(type='Normalize', **img_norm_cfg), + dict(type='Pad', size_divisor=32), + dict(type='DefaultFormatBundle'), + dict(type='Collect', keys=['img', 'gt_bboxes', 'gt_labels', 'gt_masks']), +] +test_pipeline = [ + dict(type='LoadImageFromFile'), + dict( + type='MultiScaleFlipAug', + img_scale=(1333, 800), + flip=False, + transforms=[ + dict(type='Resize', keep_ratio=True), + dict(type='RandomFlip'), + dict(type='Normalize', **img_norm_cfg), + dict(type='Pad', size_divisor=32), + dict(type='ImageToTensor', keys=['img']), + dict(type='Collect', keys=['img']), + ]) +] + +data = dict( + train=dict(dataset=dict(pipeline=train_pipeline)), + val=dict(pipeline=test_pipeline), + test=dict(pipeline=test_pipeline)) diff --git a/configs/mask_rcnn/mask_rcnn_r101_fpn_1x_coco.py b/configs/mask_rcnn/mask_rcnn_r101_fpn_1x_coco.py index db02d9b..b7986e8 100644 --- a/configs/mask_rcnn/mask_rcnn_r101_fpn_1x_coco.py +++ b/configs/mask_rcnn/mask_rcnn_r101_fpn_1x_coco.py @@ -1,2 +1,6 @@ _base_ = './mask_rcnn_r50_fpn_1x_coco.py' -model = dict(pretrained='torchvision://resnet101', backbone=dict(depth=101)) +model = dict( + backbone=dict( + depth=101, + init_cfg=dict(type='Pretrained', + checkpoint='torchvision://resnet101'))) diff --git a/configs/mask_rcnn/mask_rcnn_r101_fpn_2x_coco.py b/configs/mask_rcnn/mask_rcnn_r101_fpn_2x_coco.py index c8cb2d8..c9059d5 100644 --- a/configs/mask_rcnn/mask_rcnn_r101_fpn_2x_coco.py +++ b/configs/mask_rcnn/mask_rcnn_r101_fpn_2x_coco.py @@ -1,2 +1,6 @@ _base_ = './mask_rcnn_r50_fpn_2x_coco.py' -model = dict(pretrained='torchvision://resnet101', backbone=dict(depth=101)) +model = dict( + backbone=dict( + depth=101, + init_cfg=dict(type='Pretrained', + checkpoint='torchvision://resnet101'))) diff --git a/configs/mask_rcnn/mask_rcnn_r101_fpn_mstrain-poly_3x_coco.py b/configs/mask_rcnn/mask_rcnn_r101_fpn_mstrain-poly_3x_coco.py new file mode 100644 index 0000000..0696cbe --- /dev/null +++ b/configs/mask_rcnn/mask_rcnn_r101_fpn_mstrain-poly_3x_coco.py @@ -0,0 +1,10 @@ +_base_ = [ + '../common/mstrain-poly_3x_coco_instance.py', + '../_base_/models/mask_rcnn_r50_fpn.py' +] + +model = dict( + backbone=dict( + depth=101, + init_cfg=dict(type='Pretrained', + checkpoint='torchvision://resnet101'))) diff --git a/configs/mask_rcnn/mask_rcnn_r50_caffe_fpn_1x_coco.py b/configs/mask_rcnn/mask_rcnn_r50_caffe_fpn_1x_coco.py index 0471fe8..5a23f8c 100644 --- a/configs/mask_rcnn/mask_rcnn_r50_caffe_fpn_1x_coco.py +++ b/configs/mask_rcnn/mask_rcnn_r50_caffe_fpn_1x_coco.py @@ -1,7 +1,11 @@ _base_ = './mask_rcnn_r50_fpn_1x_coco.py' model = dict( - pretrained='open-mmlab://detectron2/resnet50_caffe', - backbone=dict(norm_cfg=dict(requires_grad=False), style='caffe')) + backbone=dict( + norm_cfg=dict(requires_grad=False), + style='caffe', + init_cfg=dict( + type='Pretrained', + checkpoint='open-mmlab://detectron2/resnet50_caffe'))) # use caffe img_norm img_norm_cfg = dict( mean=[103.530, 116.280, 123.675], std=[1.0, 1.0, 1.0], to_rgb=False) diff --git a/configs/mask_rcnn/mask_rcnn_r50_caffe_fpn_mstrain-poly_1x_coco.py b/configs/mask_rcnn/mask_rcnn_r50_caffe_fpn_mstrain-poly_1x_coco.py index 5d6215d..6308e40 100644 --- a/configs/mask_rcnn/mask_rcnn_r50_caffe_fpn_mstrain-poly_1x_coco.py +++ b/configs/mask_rcnn/mask_rcnn_r50_caffe_fpn_mstrain-poly_1x_coco.py @@ -1,7 +1,11 @@ _base_ = './mask_rcnn_r50_fpn_1x_coco.py' model = dict( - pretrained='open-mmlab://detectron2/resnet50_caffe', - backbone=dict(norm_cfg=dict(requires_grad=False), style='caffe')) + backbone=dict( + norm_cfg=dict(requires_grad=False), + style='caffe', + init_cfg=dict( + type='Pretrained', + checkpoint='open-mmlab://detectron2/resnet50_caffe'))) # use caffe img_norm img_norm_cfg = dict( mean=[103.530, 116.280, 123.675], std=[1.0, 1.0, 1.0], to_rgb=False) diff --git a/configs/mask_rcnn/mask_rcnn_r50_caffe_fpn_mstrain_1x_coco.py b/configs/mask_rcnn/mask_rcnn_r50_caffe_fpn_mstrain_1x_coco.py index 86c5b13..bebbaaa 100644 --- a/configs/mask_rcnn/mask_rcnn_r50_caffe_fpn_mstrain_1x_coco.py +++ b/configs/mask_rcnn/mask_rcnn_r50_caffe_fpn_mstrain_1x_coco.py @@ -1,7 +1,11 @@ _base_ = './mask_rcnn_r50_fpn_1x_coco.py' model = dict( - pretrained='open-mmlab://detectron2/resnet50_caffe', - backbone=dict(norm_cfg=dict(requires_grad=False), style='caffe')) + backbone=dict( + norm_cfg=dict(requires_grad=False), + style='caffe', + init_cfg=dict( + type='Pretrained', + checkpoint='open-mmlab://detectron2/resnet50_caffe'))) # use caffe img_norm img_norm_cfg = dict( mean=[103.530, 116.280, 123.675], std=[1.0, 1.0, 1.0], to_rgb=False) diff --git a/configs/mask_rcnn/mask_rcnn_r50_caffe_fpn_poly_1x_coco_v1.py b/configs/mask_rcnn/mask_rcnn_r50_caffe_fpn_poly_1x_coco_v1.py index 431e5ab..3f8079d 100644 --- a/configs/mask_rcnn/mask_rcnn_r50_caffe_fpn_poly_1x_coco_v1.py +++ b/configs/mask_rcnn/mask_rcnn_r50_caffe_fpn_poly_1x_coco_v1.py @@ -1,7 +1,11 @@ _base_ = './mask_rcnn_r50_fpn_1x_coco.py' model = dict( - pretrained='open-mmlab://resnet50_caffe_bgr', - backbone=dict(norm_cfg=dict(requires_grad=False), style='caffe'), + backbone=dict( + norm_cfg=dict(requires_grad=False), + style='caffe', + init_cfg=dict( + type='Pretrained', + checkpoint='open-mmlab://detectron2/resnet50_caffe')), rpn_head=dict( loss_bbox=dict(type='SmoothL1Loss', beta=1.0 / 9.0, loss_weight=1.0)), roi_head=dict( diff --git a/configs/mask_rcnn/mask_rcnn_r50_fpn_mstrain-poly_3x_coco.py b/configs/mask_rcnn/mask_rcnn_r50_fpn_mstrain-poly_3x_coco.py new file mode 100644 index 0000000..b3d9242 --- /dev/null +++ b/configs/mask_rcnn/mask_rcnn_r50_fpn_mstrain-poly_3x_coco.py @@ -0,0 +1,4 @@ +_base_ = [ + '../common/mstrain-poly_3x_coco_instance.py', + '../_base_/models/mask_rcnn_r50_fpn.py' +] diff --git a/configs/mask_rcnn/mask_rcnn_x101_32x4d_fpn_1x_coco.py b/configs/mask_rcnn/mask_rcnn_x101_32x4d_fpn_1x_coco.py index d0016d1..a8b3799 100644 --- a/configs/mask_rcnn/mask_rcnn_x101_32x4d_fpn_1x_coco.py +++ b/configs/mask_rcnn/mask_rcnn_x101_32x4d_fpn_1x_coco.py @@ -1,6 +1,5 @@ _base_ = './mask_rcnn_r101_fpn_1x_coco.py' model = dict( - pretrained='open-mmlab://resnext101_32x4d', backbone=dict( type='ResNeXt', depth=101, @@ -10,4 +9,6 @@ out_indices=(0, 1, 2, 3), frozen_stages=1, norm_cfg=dict(type='BN', requires_grad=True), - style='pytorch')) + style='pytorch', + init_cfg=dict( + type='Pretrained', checkpoint='open-mmlab://resnext101_32x4d'))) diff --git a/configs/mask_rcnn/mask_rcnn_x101_32x4d_fpn_2x_coco.py b/configs/mask_rcnn/mask_rcnn_x101_32x4d_fpn_2x_coco.py index d4189c6..2cd3cee 100644 --- a/configs/mask_rcnn/mask_rcnn_x101_32x4d_fpn_2x_coco.py +++ b/configs/mask_rcnn/mask_rcnn_x101_32x4d_fpn_2x_coco.py @@ -1,6 +1,5 @@ _base_ = './mask_rcnn_r101_fpn_2x_coco.py' model = dict( - pretrained='open-mmlab://resnext101_32x4d', backbone=dict( type='ResNeXt', depth=101, @@ -10,4 +9,6 @@ out_indices=(0, 1, 2, 3), frozen_stages=1, norm_cfg=dict(type='BN', requires_grad=True), - style='pytorch')) + style='pytorch', + init_cfg=dict( + type='Pretrained', checkpoint='open-mmlab://resnext101_32x4d'))) diff --git a/configs/mask_rcnn/mask_rcnn_x101_32x4d_fpn_mstrain-poly_3x_coco.py b/configs/mask_rcnn/mask_rcnn_x101_32x4d_fpn_mstrain-poly_3x_coco.py new file mode 100644 index 0000000..b698a7d --- /dev/null +++ b/configs/mask_rcnn/mask_rcnn_x101_32x4d_fpn_mstrain-poly_3x_coco.py @@ -0,0 +1,18 @@ +_base_ = [ + '../common/mstrain-poly_3x_coco_instance.py', + '../_base_/models/mask_rcnn_r50_fpn.py' +] + +model = dict( + backbone=dict( + type='ResNeXt', + depth=101, + groups=32, + base_width=4, + num_stages=4, + out_indices=(0, 1, 2, 3), + frozen_stages=1, + norm_cfg=dict(type='BN', requires_grad=True), + style='pytorch', + init_cfg=dict( + type='Pretrained', checkpoint='open-mmlab://resnext101_32x4d'))) diff --git a/configs/mask_rcnn/mask_rcnn_x101_32x8d_fpn_1x_coco.py b/configs/mask_rcnn/mask_rcnn_x101_32x8d_fpn_1x_coco.py index ee034b7..108ea4e 100644 --- a/configs/mask_rcnn/mask_rcnn_x101_32x8d_fpn_1x_coco.py +++ b/configs/mask_rcnn/mask_rcnn_x101_32x8d_fpn_1x_coco.py @@ -1,6 +1,5 @@ _base_ = './mask_rcnn_r101_fpn_1x_coco.py' model = dict( - pretrained='open-mmlab://detectron2/resnext101_32x8d', backbone=dict( type='ResNeXt', depth=101, @@ -10,7 +9,10 @@ out_indices=(0, 1, 2, 3), frozen_stages=1, norm_cfg=dict(type='BN', requires_grad=False), - style='pytorch')) + style='pytorch', + init_cfg=dict( + type='Pretrained', + checkpoint='open-mmlab://detectron2/resnext101_32x8d'))) dataset_type = 'CocoDataset' data_root = 'data/coco/' diff --git a/configs/mask_rcnn/mask_rcnn_x101_32x8d_fpn_mstrain-poly_1x_coco.py b/configs/mask_rcnn/mask_rcnn_x101_32x8d_fpn_mstrain-poly_1x_coco.py index 1c12432..6b912f6 100644 --- a/configs/mask_rcnn/mask_rcnn_x101_32x8d_fpn_mstrain-poly_1x_coco.py +++ b/configs/mask_rcnn/mask_rcnn_x101_32x8d_fpn_mstrain-poly_1x_coco.py @@ -1,6 +1,5 @@ _base_ = './mask_rcnn_r101_fpn_1x_coco.py' model = dict( - pretrained='open-mmlab://detectron2/resnext101_32x8d', backbone=dict( type='ResNeXt', depth=101, @@ -10,7 +9,10 @@ out_indices=(0, 1, 2, 3), frozen_stages=1, norm_cfg=dict(type='BN', requires_grad=False), - style='pytorch')) + style='pytorch', + init_cfg=dict( + type='Pretrained', + checkpoint='open-mmlab://detectron2/resnext101_32x8d'))) dataset_type = 'CocoDataset' data_root = 'data/coco/' diff --git a/configs/mask_rcnn/mask_rcnn_x101_32x8d_fpn_mstrain-poly_3x_coco.py b/configs/mask_rcnn/mask_rcnn_x101_32x8d_fpn_mstrain-poly_3x_coco.py index 93b7d51..8ba0e9c 100644 --- a/configs/mask_rcnn/mask_rcnn_x101_32x8d_fpn_mstrain-poly_3x_coco.py +++ b/configs/mask_rcnn/mask_rcnn_x101_32x8d_fpn_mstrain-poly_3x_coco.py @@ -1,6 +1,9 @@ -_base_ = './mask_rcnn_r101_fpn_1x_coco.py' +_base_ = [ + '../common/mstrain-poly_3x_coco_instance.py', + '../_base_/models/mask_rcnn_r50_fpn.py' +] + model = dict( - pretrained='open-mmlab://detectron2/resnext101_32x8d', backbone=dict( type='ResNeXt', depth=101, @@ -10,7 +13,10 @@ out_indices=(0, 1, 2, 3), frozen_stages=1, norm_cfg=dict(type='BN', requires_grad=False), - style='pytorch')) + style='pytorch', + init_cfg=dict( + type='Pretrained', + checkpoint='open-mmlab://detectron2/resnext101_32x8d'))) dataset_type = 'CocoDataset' data_root = 'data/coco/' @@ -18,6 +24,9 @@ mean=[103.530, 116.280, 123.675], std=[57.375, 57.120, 58.395], to_rgb=False) + +# In mstrain 3x config, img_scale=[(1333, 640), (1333, 800)], +# multiscale_mode='range' train_pipeline = [ dict(type='LoadImageFromFile'), dict( @@ -27,9 +36,8 @@ poly2mask=False), dict( type='Resize', - img_scale=[(1333, 640), (1333, 672), (1333, 704), (1333, 736), - (1333, 768), (1333, 800)], - multiscale_mode='value', + img_scale=[(1333, 640), (1333, 800)], + multiscale_mode='range', keep_ratio=True), dict(type='RandomFlip', flip_ratio=0.5), dict(type='Normalize', **img_norm_cfg), @@ -52,10 +60,26 @@ dict(type='Collect', keys=['img']), ]) ] -data = dict( - train=dict(pipeline=train_pipeline), - val=dict(pipeline=test_pipeline), - test=dict(pipeline=test_pipeline)) -lr_config = dict(step=[28, 34]) -runner = dict(type='EpochBasedRunner', max_epochs=36) +# Use RepeatDataset to speed up training +data = dict( + samples_per_gpu=2, + workers_per_gpu=2, + train=dict( + type='RepeatDataset', + times=3, + dataset=dict( + type=dataset_type, + ann_file=data_root + 'annotations/instances_train2017.json', + img_prefix=data_root + 'train2017/', + pipeline=train_pipeline)), + val=dict( + type=dataset_type, + ann_file=data_root + 'annotations/instances_val2017.json', + img_prefix=data_root + 'val2017/', + pipeline=test_pipeline), + test=dict( + type=dataset_type, + ann_file=data_root + 'annotations/instances_val2017.json', + img_prefix=data_root + 'val2017/', + pipeline=test_pipeline)) diff --git a/configs/mask_rcnn/mask_rcnn_x101_64x4d_fpn_1x_coco.py b/configs/mask_rcnn/mask_rcnn_x101_64x4d_fpn_1x_coco.py index 31e5943..2333b03 100644 --- a/configs/mask_rcnn/mask_rcnn_x101_64x4d_fpn_1x_coco.py +++ b/configs/mask_rcnn/mask_rcnn_x101_64x4d_fpn_1x_coco.py @@ -1,6 +1,5 @@ _base_ = './mask_rcnn_x101_32x4d_fpn_1x_coco.py' model = dict( - pretrained='open-mmlab://resnext101_64x4d', backbone=dict( type='ResNeXt', depth=101, @@ -10,4 +9,6 @@ out_indices=(0, 1, 2, 3), frozen_stages=1, norm_cfg=dict(type='BN', requires_grad=True), - style='pytorch')) + style='pytorch', + init_cfg=dict( + type='Pretrained', checkpoint='open-mmlab://resnext101_64x4d'))) diff --git a/configs/mask_rcnn/mask_rcnn_x101_64x4d_fpn_2x_coco.py b/configs/mask_rcnn/mask_rcnn_x101_64x4d_fpn_2x_coco.py index 9ba92c5..6074cca 100644 --- a/configs/mask_rcnn/mask_rcnn_x101_64x4d_fpn_2x_coco.py +++ b/configs/mask_rcnn/mask_rcnn_x101_64x4d_fpn_2x_coco.py @@ -1,6 +1,5 @@ _base_ = './mask_rcnn_x101_32x4d_fpn_2x_coco.py' model = dict( - pretrained='open-mmlab://resnext101_64x4d', backbone=dict( type='ResNeXt', depth=101, @@ -10,4 +9,6 @@ out_indices=(0, 1, 2, 3), frozen_stages=1, norm_cfg=dict(type='BN', requires_grad=True), - style='pytorch')) + style='pytorch', + init_cfg=dict( + type='Pretrained', checkpoint='open-mmlab://resnext101_64x4d'))) diff --git a/configs/mask_rcnn/mask_rcnn_x101_64x4d_fpn_mstrain-poly_3x_coco.py b/configs/mask_rcnn/mask_rcnn_x101_64x4d_fpn_mstrain-poly_3x_coco.py new file mode 100644 index 0000000..9f9cb1c --- /dev/null +++ b/configs/mask_rcnn/mask_rcnn_x101_64x4d_fpn_mstrain-poly_3x_coco.py @@ -0,0 +1,18 @@ +_base_ = [ + '../common/mstrain-poly_3x_coco_instance.py', + '../_base_/models/mask_rcnn_r50_fpn.py' +] + +model = dict( + backbone=dict( + type='ResNeXt', + depth=101, + groups=64, + base_width=4, + num_stages=4, + out_indices=(0, 1, 2, 3), + frozen_stages=1, + norm_cfg=dict(type='BN', requires_grad=True), + style='pytorch', + init_cfg=dict( + type='Pretrained', checkpoint='open-mmlab://resnext101_64x4d'))) diff --git a/configs/mask_rcnn/metafile.yml b/configs/mask_rcnn/metafile.yml new file mode 100644 index 0000000..747a99a --- /dev/null +++ b/configs/mask_rcnn/metafile.yml @@ -0,0 +1,419 @@ +Collections: + - Name: Mask R-CNN + Metadata: + Training Data: COCO + Training Techniques: + - SGD with Momentum + - Weight Decay + Training Resources: 8x V100 GPUs + Architecture: + - Softmax + - RPN + - Convolution + - Dense Connections + - FPN + - ResNet + - RoIAlign + Paper: + URL: https://arxiv.org/abs/1703.06870v3 + Title: 'Mask R-CNN' + README: configs/mask_rcnn/README.md + Code: + URL: https://github.com/open-mmlab/mmdetection/blob/v2.0.0/mmdet/models/detectors/mask_rcnn.py#L6 + Version: v2.0.0 + +Models: + - Name: mask_rcnn_r50_caffe_fpn_1x_coco + In Collection: Mask R-CNN + Config: configs/mask_rcnn/mask_rcnn_r50_caffe_fpn_1x_coco.py + Metadata: + Training Memory (GB): 4.3 + Epochs: 12 + Results: + - Task: Object Detection + Dataset: COCO + Metrics: + box AP: 38.0 + - Task: Instance Segmentation + Dataset: COCO + Metrics: + mask AP: 34.4 + Weights: https://download.openmmlab.com/mmdetection/v2.0/mask_rcnn/mask_rcnn_r50_caffe_fpn_1x_coco/mask_rcnn_r50_caffe_fpn_1x_coco_bbox_mAP-0.38__segm_mAP-0.344_20200504_231812-0ebd1859.pth + + - Name: mask_rcnn_r50_fpn_1x_coco + In Collection: Mask R-CNN + Config: configs/mask_rcnn/mask_rcnn_r50_fpn_1x_coco.py + Metadata: + Training Memory (GB): 4.4 + inference time (ms/im): + - value: 62.11 + hardware: V100 + backend: PyTorch + batch size: 1 + mode: FP32 + resolution: (800, 1333) + Epochs: 12 + Results: + - Task: Object Detection + Dataset: COCO + Metrics: + box AP: 38.2 + - Task: Instance Segmentation + Dataset: COCO + Metrics: + mask AP: 34.7 + Weights: https://download.openmmlab.com/mmdetection/v2.0/mask_rcnn/mask_rcnn_r50_fpn_1x_coco/mask_rcnn_r50_fpn_1x_coco_20200205-d4b0c5d6.pth + + - Name: mask_rcnn_r50_fpn_2x_coco + In Collection: Mask R-CNN + Config: configs/mask_rcnn/mask_rcnn_r50_fpn_2x_coco.py + Metadata: + Training Memory (GB): 4.4 + inference time (ms/im): + - value: 62.11 + hardware: V100 + backend: PyTorch + batch size: 1 + mode: FP32 + resolution: (800, 1333) + Epochs: 24 + Results: + - Task: Object Detection + Dataset: COCO + Metrics: + box AP: 39.2 + - Task: Instance Segmentation + Dataset: COCO + Metrics: + mask AP: 35.4 + Weights: https://download.openmmlab.com/mmdetection/v2.0/mask_rcnn/mask_rcnn_r50_fpn_2x_coco/mask_rcnn_r50_fpn_2x_coco_bbox_mAP-0.392__segm_mAP-0.354_20200505_003907-3e542a40.pth + + - Name: mask_rcnn_r101_caffe_fpn_1x_coco + In Collection: Mask R-CNN + Config: configs/mask_rcnn/mask_rcnn_r101_caffe_fpn_1x_coco.py + Metadata: + Epochs: 12 + Results: + - Task: Object Detection + Dataset: COCO + Metrics: + box AP: 40.4 + - Task: Instance Segmentation + Dataset: COCO + Metrics: + mask AP: 36.4 + Weights: https://download.openmmlab.com/mmdetection/v2.0/mask_rcnn/mask_rcnn_r101_caffe_fpn_1x_coco/mask_rcnn_r101_caffe_fpn_1x_coco_20200601_095758-805e06c1.pth + + - Name: mask_rcnn_r101_fpn_1x_coco + In Collection: Mask R-CNN + Config: configs/mask_rcnn/mask_rcnn_r101_fpn_1x_coco.py + Metadata: + Training Memory (GB): 6.4 + inference time (ms/im): + - value: 74.07 + hardware: V100 + backend: PyTorch + batch size: 1 + mode: FP32 + resolution: (800, 1333) + Epochs: 12 + Results: + - Task: Object Detection + Dataset: COCO + Metrics: + box AP: 40.0 + - Task: Instance Segmentation + Dataset: COCO + Metrics: + mask AP: 36.1 + Weights: https://download.openmmlab.com/mmdetection/v2.0/mask_rcnn/mask_rcnn_r101_fpn_1x_coco/mask_rcnn_r101_fpn_1x_coco_20200204-1efe0ed5.pth + + - Name: mask_rcnn_r101_fpn_2x_coco + In Collection: Mask R-CNN + Config: configs/mask_rcnn/mask_rcnn_r101_fpn_2x_coco.py + Metadata: + Training Memory (GB): 6.4 + inference time (ms/im): + - value: 74.07 + hardware: V100 + backend: PyTorch + batch size: 1 + mode: FP32 + resolution: (800, 1333) + Epochs: 24 + Results: + - Task: Object Detection + Dataset: COCO + Metrics: + box AP: 40.8 + - Task: Instance Segmentation + Dataset: COCO + Metrics: + mask AP: 36.6 + Weights: https://download.openmmlab.com/mmdetection/v2.0/mask_rcnn/mask_rcnn_r101_fpn_2x_coco/mask_rcnn_r101_fpn_2x_coco_bbox_mAP-0.408__segm_mAP-0.366_20200505_071027-14b391c7.pth + + - Name: mask_rcnn_x101_32x4d_fpn_1x_coco + In Collection: Mask R-CNN + Config: configs/mask_rcnn/mask_rcnn_x101_32x4d_fpn_1x_coco.py + Metadata: + Training Memory (GB): 7.6 + inference time (ms/im): + - value: 88.5 + hardware: V100 + backend: PyTorch + batch size: 1 + mode: FP32 + resolution: (800, 1333) + Epochs: 12 + Results: + - Task: Object Detection + Dataset: COCO + Metrics: + box AP: 41.9 + - Task: Instance Segmentation + Dataset: COCO + Metrics: + mask AP: 37.5 + Weights: https://download.openmmlab.com/mmdetection/v2.0/mask_rcnn/mask_rcnn_x101_32x4d_fpn_1x_coco/mask_rcnn_x101_32x4d_fpn_1x_coco_20200205-478d0b67.pth + + - Name: mask_rcnn_x101_32x4d_fpn_2x_coco + In Collection: Mask R-CNN + Config: configs/mask_rcnn/mask_rcnn_x101_32x4d_fpn_2x_coco.py + Metadata: + Training Memory (GB): 7.6 + inference time (ms/im): + - value: 88.5 + hardware: V100 + backend: PyTorch + batch size: 1 + mode: FP32 + resolution: (800, 1333) + Epochs: 24 + Results: + - Task: Object Detection + Dataset: COCO + Metrics: + box AP: 42.2 + - Task: Instance Segmentation + Dataset: COCO + Metrics: + mask AP: 37.8 + Weights: https://download.openmmlab.com/mmdetection/v2.0/mask_rcnn/mask_rcnn_x101_32x4d_fpn_2x_coco/mask_rcnn_x101_32x4d_fpn_2x_coco_bbox_mAP-0.422__segm_mAP-0.378_20200506_004702-faef898c.pth + + - Name: mask_rcnn_x101_64x4d_fpn_1x_coco + In Collection: Mask R-CNN + Config: configs/mask_rcnn/mask_rcnn_x101_64x4d_fpn_1x_coco.py + Metadata: + Training Memory (GB): 10.7 + inference time (ms/im): + - value: 125 + hardware: V100 + backend: PyTorch + batch size: 1 + mode: FP32 + resolution: (800, 1333) + Epochs: 12 + Results: + - Task: Object Detection + Dataset: COCO + Metrics: + box AP: 42.8 + - Task: Instance Segmentation + Dataset: COCO + Metrics: + mask AP: 38.4 + Weights: https://download.openmmlab.com/mmdetection/v2.0/mask_rcnn/mask_rcnn_x101_64x4d_fpn_1x_coco/mask_rcnn_x101_64x4d_fpn_1x_coco_20200201-9352eb0d.pth + + - Name: mask_rcnn_x101_64x4d_fpn_2x_coco + In Collection: Mask R-CNN + Config: configs/mask_rcnn/mask_rcnn_x101_64x4d_fpn_2x_coco.py + Metadata: + Training Memory (GB): 10.7 + inference time (ms/im): + - value: 125 + hardware: V100 + backend: PyTorch + batch size: 1 + mode: FP32 + resolution: (800, 1333) + Epochs: 24 + Results: + - Task: Object Detection + Dataset: COCO + Metrics: + box AP: 42.7 + - Task: Instance Segmentation + Dataset: COCO + Metrics: + mask AP: 38.1 + Weights: https://download.openmmlab.com/mmdetection/v2.0/mask_rcnn/mask_rcnn_x101_64x4d_fpn_2x_coco/mask_rcnn_x101_64x4d_fpn_2x_coco_20200509_224208-39d6f70c.pth + + - Name: mask_rcnn_x101_32x8d_fpn_1x_coco + In Collection: Mask R-CNN + Config: configs/mask_rcnn/mask_rcnn_x101_32x8d_fpn_1x_coco.py + Metadata: + Training Memory (GB): 10.7 + inference time (ms/im): + - value: 125 + hardware: V100 + backend: PyTorch + batch size: 1 + mode: FP32 + resolution: (800, 1333) + Epochs: 12 + Results: + - Task: Object Detection + Dataset: COCO + Metrics: + box AP: 42.8 + - Task: Instance Segmentation + Dataset: COCO + Metrics: + mask AP: 38.3 + + - Name: mask_rcnn_r50_caffe_fpn_mstrain-poly_2x_coco + In Collection: Mask R-CNN + Config: configs/mask_rcnn/mask_rcnn_r50_caffe_fpn_mstrain-poly_2x_coco.py + Metadata: + Training Memory (GB): 4.3 + Epochs: 24 + Results: + - Task: Object Detection + Dataset: COCO + Metrics: + box AP: 40.3 + - Task: Instance Segmentation + Dataset: COCO + Metrics: + mask AP: 36.5 + Weights: https://download.openmmlab.com/mmdetection/v2.0/mask_rcnn/mask_rcnn_r50_caffe_fpn_mstrain-poly_2x_coco/mask_rcnn_r50_caffe_fpn_mstrain-poly_2x_coco_bbox_mAP-0.403__segm_mAP-0.365_20200504_231822-a75c98ce.pth + + - Name: mask_rcnn_r50_caffe_fpn_mstrain-poly_3x_coco + In Collection: Mask R-CNN + Config: configs/mask_rcnn/mask_rcnn_r50_caffe_fpn_mstrain-poly_3x_coco.py + Metadata: + Training Memory (GB): 4.3 + Epochs: 36 + Results: + - Task: Object Detection + Dataset: COCO + Metrics: + box AP: 40.8 + - Task: Instance Segmentation + Dataset: COCO + Metrics: + mask AP: 37.0 + Weights: https://download.openmmlab.com/mmdetection/v2.0/mask_rcnn/mask_rcnn_r50_caffe_fpn_mstrain-poly_3x_coco/mask_rcnn_r50_caffe_fpn_mstrain-poly_3x_coco_bbox_mAP-0.408__segm_mAP-0.37_20200504_163245-42aa3d00.pth + + - Name: mask_rcnn_r50_fpn_mstrain-poly_3x_coco + In Collection: Mask R-CNN + Config: configs/mask_rcnn/mask_rcnn_r50_fpn_mstrain-poly_3x_coco.py + Metadata: + Training Memory (GB): 4.1 + Epochs: 36 + Results: + - Task: Object Detection + Dataset: COCO + Metrics: + box AP: 40.9 + - Task: Instance Segmentation + Dataset: COCO + Metrics: + mask AP: 37.1 + Weights: https://download.openmmlab.com/mmdetection/v2.0/mask_rcnn/mask_rcnn_r50_fpn_mstrain-poly_3x_coco/mask_rcnn_r50_fpn_mstrain-poly_3x_coco_20210524_201154-21b550bb.pth + + - Name: mask_rcnn_r101_fpn_mstrain-poly_3x_coco + In Collection: Mask R-CNN + Config: configs/mask_rcnn/mask_rcnn_r101_fpn_mstrain-poly_3x_coco.py + Metadata: + Training Memory (GB): 6.1 + Epochs: 36 + Results: + - Task: Object Detection + Dataset: COCO + Metrics: + box AP: 42.7 + - Task: Instance Segmentation + Dataset: COCO + Metrics: + mask AP: 38.5 + Weights: https://download.openmmlab.com/mmdetection/v2.0/mask_rcnn/mask_rcnn_r101_fpn_mstrain-poly_3x_coco/mask_rcnn_r101_fpn_mstrain-poly_3x_coco_20210524_200244-5675c317.pth + + - Name: mask_rcnn_r101_caffe_fpn_mstrain-poly_3x_coco + In Collection: Mask R-CNN + Config: configs/mask_rcnn/mask_rcnn_r101_caffe_fpn_mstrain-poly_3x_coco.py + Metadata: + Training Memory (GB): 5.9 + Epochs: 36 + Results: + - Task: Object Detection + Dataset: COCO + Metrics: + box AP: 42.9 + - Task: Instance Segmentation + Dataset: COCO + Metrics: + mask AP: 38.5 + Weights: https://download.openmmlab.com/mmdetection/v2.0/mask_rcnn/mask_rcnn_r101_caffe_fpn_mstrain-poly_3x_coco/mask_rcnn_r101_caffe_fpn_mstrain-poly_3x_coco_20210526_132339-3c33ce02.pth + + - Name: mask_rcnn_x101_32x4d_fpn_mstrain-poly_3x_coco + In Collection: Mask R-CNN + Config: configs/mask_rcnn/mask_rcnn_x101_32x4d_fpn_mstrain-poly_3x_coco.py + Metadata: + Training Memory (GB): 7.3 + Epochs: 36 + Results: + - Task: Object Detection + Dataset: COCO + Metrics: + box AP: 43.6 + - Task: Instance Segmentation + Dataset: COCO + Metrics: + mask AP: 39.0 + Weights: https://download.openmmlab.com/mmdetection/v2.0/mask_rcnn/mask_rcnn_x101_32x4d_fpn_mstrain-poly_3x_coco/mask_rcnn_x101_32x4d_fpn_mstrain-poly_3x_coco_20210524_201410-abcd7859.pth + + - Name: mask_rcnn_x101_32x8d_fpn_mstrain-poly_1x_coco + In Collection: Mask R-CNN + Config: configs/mask_rcnn/mask_rcnn_x101_32x8d_fpn_mstrain-poly_1x_coco.py + Metadata: + Epochs: 12 + Results: + - Task: Object Detection + Dataset: COCO + Metrics: + box AP: 43.6 + - Task: Instance Segmentation + Dataset: COCO + Metrics: + mask AP: 39.0 + + - Name: mask_rcnn_x101_32x8d_fpn_mstrain-poly_3x_coco + In Collection: Mask R-CNN + Config: configs/mask_rcnn/mask_rcnn_x101_32x8d_fpn_mstrain-poly_3x_coco + Metadata: + Training Memory (GB): 10.3 + Epochs: 36 + Results: + - Task: Object Detection + Dataset: COCO + Metrics: + box AP: 44.3 + Weights: https://download.openmmlab.com/mmdetection/v2.0/mask_rcnn/mask_rcnn_x101_32x8d_fpn_mstrain-poly_3x_coco/mask_rcnn_x101_32x8d_fpn_mstrain-poly_3x_coco_20210607_161042-8bd2c639.pth + + - Name: mask_rcnn_x101_64x4d_fpn_mstrain-poly_3x_coco + In Collection: Mask R-CNN + Config: configs/mask_rcnn/mask_rcnn_x101_64x4d_fpn_mstrain-poly_3x_coco.py + Metadata: + Epochs: 36 + Training Memory (GB): 10.4 + Results: + - Task: Object Detection + Dataset: COCO + Metrics: + box AP: 44.5 + - Task: Instance Segmentation + Dataset: COCO + Metrics: + mask AP: 39.7 + Weights: https://download.openmmlab.com/mmdetection/v2.0/mask_rcnn/mask_rcnn_x101_64x4d_fpn_mstrain-poly_3x_coco/mask_rcnn_x101_64x4d_fpn_mstrain-poly_3x_coco_20210526_120447-c376f129.pth diff --git a/configs/ms_rcnn/README.md b/configs/ms_rcnn/README.md index c19dee3..f36b64c 100644 --- a/configs/ms_rcnn/README.md +++ b/configs/ms_rcnn/README.md @@ -2,7 +2,7 @@ ## Introduction -[ALGORITHM] + ``` @inproceedings{huang2019msrcnn, @@ -17,10 +17,10 @@ | Backbone | style | Lr schd | Mem (GB) | Inf time (fps) | box AP | mask AP | Config | Download | |:-------------:|:----------:|:-------:|:--------:|:--------------:|:------:|:-------:|:------:|:--------:| -| R-50-FPN | caffe | 1x | 4.5 | | 38.2 | 36.0 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/ms_rcnn/ms_rcnn_r50_caffe_fpn_1x_coco.py) | [model](http://download.openmmlab.com/mmdetection/v2.0/ms_rcnn/ms_rcnn_r50_caffe_fpn_1x_coco/ms_rcnn_r50_caffe_fpn_1x_coco_20200702_180848-61c9355e.pth) | [log](http://download.openmmlab.com/mmdetection/v2.0/ms_rcnn/ms_rcnn_r50_caffe_fpn_1x_coco/ms_rcnn_r50_caffe_fpn_1x_coco_20200702_180848.log.json) | -| R-50-FPN | caffe | 2x | - | - | 38.8 | 36.3 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/ms_rcnn/ms_rcnn_r50_caffe_fpn_2x_coco.py) | [model](http://download.openmmlab.com/mmdetection/v2.0/ms_rcnn/ms_rcnn_r50_caffe_fpn_2x_coco/ms_rcnn_r50_caffe_fpn_2x_coco_bbox_mAP-0.388__segm_mAP-0.363_20200506_004738-ee87b137.pth) | [log](http://download.openmmlab.com/mmdetection/v2.0/ms_rcnn/ms_rcnn_r50_caffe_fpn_2x_coco/ms_rcnn_r50_caffe_fpn_2x_coco_20200506_004738.log.json) | -| R-101-FPN | caffe | 1x | 6.5 | | 40.4 | 37.6 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/ms_rcnn/ms_rcnn_r101_caffe_fpn_1x_coco.py) | [model](http://download.openmmlab.com/mmdetection/v2.0/ms_rcnn/ms_rcnn_r101_caffe_fpn_1x_coco/ms_rcnn_r101_caffe_fpn_1x_coco_bbox_mAP-0.404__segm_mAP-0.376_20200506_004755-b9b12a37.pth) | [log](http://download.openmmlab.com/mmdetection/v2.0/ms_rcnn/ms_rcnn_r101_caffe_fpn_1x_coco/ms_rcnn_r101_caffe_fpn_1x_coco_20200506_004755.log.json) | -| R-101-FPN | caffe | 2x | - | - | 41.1 | 38.1 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/ms_rcnn/ms_rcnn_r101_caffe_fpn_2x_coco.py) | [model](http://download.openmmlab.com/mmdetection/v2.0/ms_rcnn/ms_rcnn_r101_caffe_fpn_2x_coco/ms_rcnn_r101_caffe_fpn_2x_coco_bbox_mAP-0.411__segm_mAP-0.381_20200506_011134-5f3cc74f.pth) | [log](http://download.openmmlab.com/mmdetection/v2.0/ms_rcnn/ms_rcnn_r101_caffe_fpn_2x_coco/ms_rcnn_r101_caffe_fpn_2x_coco_20200506_011134.log.json) | -| R-X101-32x4d | pytorch | 2x | 7.9 | 11.0 | 41.8 | 38.7 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/ms_rcnn/ms_rcnn_x101_32x4d_fpn_1x_coco.py) | [model](http://download.openmmlab.com/mmdetection/v2.0/ms_rcnn/ms_rcnn_x101_32x4d_fpn_1x_coco/ms_rcnn_x101_32x4d_fpn_1x_coco_20200206-81fd1740.pth) | [log](http://download.openmmlab.com/mmdetection/v2.0/ms_rcnn/ms_rcnn_x101_32x4d_fpn_1x_coco/ms_rcnn_x101_32x4d_fpn_1x_coco_20200206_100113.log.json) | -| R-X101-64x4d | pytorch | 1x | 11.0 | 8.0 | 43.0 | 39.5 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/ms_rcnn/ms_rcnn_x101_64x4d_fpn_1x_coco.py) | [model](http://download.openmmlab.com/mmdetection/v2.0/ms_rcnn/ms_rcnn_x101_64x4d_fpn_1x_coco/ms_rcnn_x101_64x4d_fpn_1x_coco_20200206-86ba88d2.pth) | [log](http://download.openmmlab.com/mmdetection/v2.0/ms_rcnn/ms_rcnn_x101_64x4d_fpn_1x_coco/ms_rcnn_x101_64x4d_fpn_1x_coco_20200206_091744.log.json) | -| R-X101-64x4d | pytorch | 2x | 11.0 | 8.0 | 42.6 | 39.5 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/ms_rcnn/ms_rcnn_x101_64x4d_fpn_2x_coco.py) | [model](http://download.openmmlab.com/mmdetection/v2.0/ms_rcnn/ms_rcnn_x101_64x4d_fpn_2x_coco/ms_rcnn_x101_64x4d_fpn_2x_coco_20200308-02a445e2.pth) | [log](http://download.openmmlab.com/mmdetection/v2.0/ms_rcnn/ms_rcnn_x101_64x4d_fpn_2x_coco/ms_rcnn_x101_64x4d_fpn_2x_coco_20200308_012247.log.json) | +| R-50-FPN | caffe | 1x | 4.5 | | 38.2 | 36.0 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/ms_rcnn/ms_rcnn_r50_caffe_fpn_1x_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/ms_rcnn/ms_rcnn_r50_caffe_fpn_1x_coco/ms_rcnn_r50_caffe_fpn_1x_coco_20200702_180848-61c9355e.pth) | [log](https://download.openmmlab.com/mmdetection/v2.0/ms_rcnn/ms_rcnn_r50_caffe_fpn_1x_coco/ms_rcnn_r50_caffe_fpn_1x_coco_20200702_180848.log.json) | +| R-50-FPN | caffe | 2x | - | - | 38.8 | 36.3 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/ms_rcnn/ms_rcnn_r50_caffe_fpn_2x_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/ms_rcnn/ms_rcnn_r50_caffe_fpn_2x_coco/ms_rcnn_r50_caffe_fpn_2x_coco_bbox_mAP-0.388__segm_mAP-0.363_20200506_004738-ee87b137.pth) | [log](https://download.openmmlab.com/mmdetection/v2.0/ms_rcnn/ms_rcnn_r50_caffe_fpn_2x_coco/ms_rcnn_r50_caffe_fpn_2x_coco_20200506_004738.log.json) | +| R-101-FPN | caffe | 1x | 6.5 | | 40.4 | 37.6 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/ms_rcnn/ms_rcnn_r101_caffe_fpn_1x_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/ms_rcnn/ms_rcnn_r101_caffe_fpn_1x_coco/ms_rcnn_r101_caffe_fpn_1x_coco_bbox_mAP-0.404__segm_mAP-0.376_20200506_004755-b9b12a37.pth) | [log](https://download.openmmlab.com/mmdetection/v2.0/ms_rcnn/ms_rcnn_r101_caffe_fpn_1x_coco/ms_rcnn_r101_caffe_fpn_1x_coco_20200506_004755.log.json) | +| R-101-FPN | caffe | 2x | - | - | 41.1 | 38.1 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/ms_rcnn/ms_rcnn_r101_caffe_fpn_2x_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/ms_rcnn/ms_rcnn_r101_caffe_fpn_2x_coco/ms_rcnn_r101_caffe_fpn_2x_coco_bbox_mAP-0.411__segm_mAP-0.381_20200506_011134-5f3cc74f.pth) | [log](https://download.openmmlab.com/mmdetection/v2.0/ms_rcnn/ms_rcnn_r101_caffe_fpn_2x_coco/ms_rcnn_r101_caffe_fpn_2x_coco_20200506_011134.log.json) | +| R-X101-32x4d | pytorch | 2x | 7.9 | 11.0 | 41.8 | 38.7 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/ms_rcnn/ms_rcnn_x101_32x4d_fpn_1x_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/ms_rcnn/ms_rcnn_x101_32x4d_fpn_1x_coco/ms_rcnn_x101_32x4d_fpn_1x_coco_20200206-81fd1740.pth) | [log](https://download.openmmlab.com/mmdetection/v2.0/ms_rcnn/ms_rcnn_x101_32x4d_fpn_1x_coco/ms_rcnn_x101_32x4d_fpn_1x_coco_20200206_100113.log.json) | +| R-X101-64x4d | pytorch | 1x | 11.0 | 8.0 | 43.0 | 39.5 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/ms_rcnn/ms_rcnn_x101_64x4d_fpn_1x_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/ms_rcnn/ms_rcnn_x101_64x4d_fpn_1x_coco/ms_rcnn_x101_64x4d_fpn_1x_coco_20200206-86ba88d2.pth) | [log](https://download.openmmlab.com/mmdetection/v2.0/ms_rcnn/ms_rcnn_x101_64x4d_fpn_1x_coco/ms_rcnn_x101_64x4d_fpn_1x_coco_20200206_091744.log.json) | +| R-X101-64x4d | pytorch | 2x | 11.0 | 8.0 | 42.6 | 39.5 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/ms_rcnn/ms_rcnn_x101_64x4d_fpn_2x_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/ms_rcnn/ms_rcnn_x101_64x4d_fpn_2x_coco/ms_rcnn_x101_64x4d_fpn_2x_coco_20200308-02a445e2.pth) | [log](https://download.openmmlab.com/mmdetection/v2.0/ms_rcnn/ms_rcnn_x101_64x4d_fpn_2x_coco/ms_rcnn_x101_64x4d_fpn_2x_coco_20200308_012247.log.json) | diff --git a/configs/ms_rcnn/metafile.yml b/configs/ms_rcnn/metafile.yml new file mode 100644 index 0000000..a6c7dc5 --- /dev/null +++ b/configs/ms_rcnn/metafile.yml @@ -0,0 +1,159 @@ +Collections: + - Name: Mask Scoring R-CNN + Metadata: + Training Data: COCO + Training Techniques: + - SGD with Momentum + - Weight Decay + Training Resources: 8x V100 GPUs + Architecture: + - RPN + - FPN + - ResNet + - RoIAlign + Paper: + URL: https://arxiv.org/abs/1903.00241 + Title: 'Mask Scoring R-CNN' + README: configs/ms_rcnn/README.md + Code: + URL: https://github.com/open-mmlab/mmdetection/blob/v2.0.0/mmdet/models/detectors/mask_scoring_rcnn.py#L6 + Version: v2.0.0 + +Models: + - Name: ms_rcnn_r50_caffe_fpn_1x_coco + In Collection: Mask Scoring R-CNN + Config: configs/ms_rcnn/ms_rcnn_r50_caffe_fpn_1x_coco.py + Metadata: + Training Memory (GB): 4.5 + Epochs: 12 + Results: + - Task: Object Detection + Dataset: COCO + Metrics: + box AP: 38.2 + - Task: Instance Segmentation + Dataset: COCO + Metrics: + mask AP: 36.0 + Weights: https://download.openmmlab.com/mmdetection/v2.0/ms_rcnn/ms_rcnn_r50_caffe_fpn_1x_coco/ms_rcnn_r50_caffe_fpn_1x_coco_20200702_180848-61c9355e.pth + + - Name: ms_rcnn_r50_caffe_fpn_2x_coco + In Collection: Mask Scoring R-CNN + Config: configs/ms_rcnn/ms_rcnn_r50_caffe_fpn_2x_coco.py + Metadata: + Epochs: 24 + Results: + - Task: Object Detection + Dataset: COCO + Metrics: + box AP: 38.8 + - Task: Instance Segmentation + Dataset: COCO + Metrics: + mask AP: 36.3 + Weights: https://download.openmmlab.com/mmdetection/v2.0/ms_rcnn/ms_rcnn_r50_caffe_fpn_2x_coco/ms_rcnn_r50_caffe_fpn_2x_coco_bbox_mAP-0.388__segm_mAP-0.363_20200506_004738-ee87b137.pth + + - Name: ms_rcnn_r101_caffe_fpn_1x_coco + In Collection: Mask Scoring R-CNN + Config: configs/ms_rcnn/ms_rcnn_r101_caffe_fpn_1x_coco.py + Metadata: + Training Memory (GB): 6.5 + Epochs: 12 + Results: + - Task: Object Detection + Dataset: COCO + Metrics: + box AP: 40.4 + - Task: Instance Segmentation + Dataset: COCO + Metrics: + mask AP: 37.6 + Weights: https://download.openmmlab.com/mmdetection/v2.0/ms_rcnn/ms_rcnn_r101_caffe_fpn_1x_coco/ms_rcnn_r101_caffe_fpn_1x_coco_bbox_mAP-0.404__segm_mAP-0.376_20200506_004755-b9b12a37.pth + + - Name: ms_rcnn_r101_caffe_fpn_2x_coco + In Collection: Mask Scoring R-CNN + Config: configs/ms_rcnn/ms_rcnn_r101_caffe_fpn_2x_coco.py + Metadata: + Epochs: 24 + Results: + - Task: Object Detection + Dataset: COCO + Metrics: + box AP: 41.1 + - Task: Instance Segmentation + Dataset: COCO + Metrics: + mask AP: 38.1 + Weights: https://download.openmmlab.com/mmdetection/v2.0/ms_rcnn/ms_rcnn_r101_caffe_fpn_2x_coco/ms_rcnn_r101_caffe_fpn_2x_coco_bbox_mAP-0.411__segm_mAP-0.381_20200506_011134-5f3cc74f.pth + + - Name: ms_rcnn_x101_32x4d_fpn_1x_coco + In Collection: Mask Scoring R-CNN + Config: configs/ms_rcnn/ms_rcnn_x101_32x4d_fpn_1x_coco.py + Metadata: + Training Memory (GB): 7.9 + inference time (ms/im): + - value: 90.91 + hardware: V100 + backend: PyTorch + batch size: 1 + mode: FP32 + resolution: (800, 1333) + Epochs: 12 + Results: + - Task: Object Detection + Dataset: COCO + Metrics: + box AP: 41.8 + - Task: Instance Segmentation + Dataset: COCO + Metrics: + mask AP: 38.7 + Weights: https://download.openmmlab.com/mmdetection/v2.0/ms_rcnn/ms_rcnn_x101_32x4d_fpn_1x_coco/ms_rcnn_x101_32x4d_fpn_1x_coco_20200206-81fd1740.pth + + - Name: ms_rcnn_x101_64x4d_fpn_1x_coco + In Collection: Mask Scoring R-CNN + Config: configs/ms_rcnn/ms_rcnn_x101_64x4d_fpn_1x_coco.py + Metadata: + Training Memory (GB): 11.0 + inference time (ms/im): + - value: 125 + hardware: V100 + backend: PyTorch + batch size: 1 + mode: FP32 + resolution: (800, 1333) + Epochs: 12 + Results: + - Task: Object Detection + Dataset: COCO + Metrics: + box AP: 43.0 + - Task: Instance Segmentation + Dataset: COCO + Metrics: + mask AP: 39.5 + Weights: https://download.openmmlab.com/mmdetection/v2.0/ms_rcnn/ms_rcnn_x101_64x4d_fpn_1x_coco/ms_rcnn_x101_64x4d_fpn_1x_coco_20200206-86ba88d2.pth + + - Name: ms_rcnn_x101_64x4d_fpn_2x_coco + In Collection: Mask Scoring R-CNN + Config: configs/ms_rcnn/ms_rcnn_x101_64x4d_fpn_2x_coco.py + Metadata: + Training Memory (GB): 11.0 + inference time (ms/im): + - value: 125 + hardware: V100 + backend: PyTorch + batch size: 1 + mode: FP32 + resolution: (800, 1333) + Epochs: 24 + Results: + - Task: Object Detection + Dataset: COCO + Metrics: + box AP: 42.6 + - Task: Instance Segmentation + Dataset: COCO + Metrics: + mask AP: 39.5 + Weights: https://download.openmmlab.com/mmdetection/v2.0/ms_rcnn/ms_rcnn_x101_64x4d_fpn_2x_coco/ms_rcnn_x101_64x4d_fpn_2x_coco_20200308-02a445e2.pth diff --git a/configs/ms_rcnn/ms_rcnn_r101_caffe_fpn_1x_coco.py b/configs/ms_rcnn/ms_rcnn_r101_caffe_fpn_1x_coco.py index 3bd33c4..9b7dcbb 100644 --- a/configs/ms_rcnn/ms_rcnn_r101_caffe_fpn_1x_coco.py +++ b/configs/ms_rcnn/ms_rcnn_r101_caffe_fpn_1x_coco.py @@ -1,4 +1,7 @@ _base_ = './ms_rcnn_r50_caffe_fpn_1x_coco.py' model = dict( - pretrained='open-mmlab://detectron2/resnet101_caffe', - backbone=dict(depth=101)) + backbone=dict( + depth=101, + init_cfg=dict( + type='Pretrained', + checkpoint='open-mmlab://detectron2/resnet101_caffe'))) diff --git a/configs/ms_rcnn/ms_rcnn_x101_32x4d_fpn_1x_coco.py b/configs/ms_rcnn/ms_rcnn_x101_32x4d_fpn_1x_coco.py index 4a78a25..20479bb 100644 --- a/configs/ms_rcnn/ms_rcnn_x101_32x4d_fpn_1x_coco.py +++ b/configs/ms_rcnn/ms_rcnn_x101_32x4d_fpn_1x_coco.py @@ -1,6 +1,5 @@ _base_ = './ms_rcnn_r50_fpn_1x_coco.py' model = dict( - pretrained='open-mmlab://resnext101_32x4d', backbone=dict( type='ResNeXt', depth=101, @@ -10,4 +9,6 @@ out_indices=(0, 1, 2, 3), frozen_stages=1, norm_cfg=dict(type='BN', requires_grad=True), - style='pytorch')) + style='pytorch', + init_cfg=dict( + type='Pretrained', checkpoint='open-mmlab://resnext101_32x4d'))) diff --git a/configs/ms_rcnn/ms_rcnn_x101_64x4d_fpn_1x_coco.py b/configs/ms_rcnn/ms_rcnn_x101_64x4d_fpn_1x_coco.py index 61a0cef..ee5b734 100644 --- a/configs/ms_rcnn/ms_rcnn_x101_64x4d_fpn_1x_coco.py +++ b/configs/ms_rcnn/ms_rcnn_x101_64x4d_fpn_1x_coco.py @@ -1,6 +1,5 @@ _base_ = './ms_rcnn_r50_fpn_1x_coco.py' model = dict( - pretrained='open-mmlab://resnext101_64x4d', backbone=dict( type='ResNeXt', depth=101, @@ -10,4 +9,6 @@ out_indices=(0, 1, 2, 3), frozen_stages=1, norm_cfg=dict(type='BN', requires_grad=True), - style='pytorch')) + style='pytorch', + init_cfg=dict( + type='Pretrained', checkpoint='open-mmlab://resnext101_64x4d'))) diff --git a/configs/nas_fcos/README.md b/configs/nas_fcos/README.md index 05ac996..21a2b22 100644 --- a/configs/nas_fcos/README.md +++ b/configs/nas_fcos/README.md @@ -2,7 +2,7 @@ ## Introduction -[ALGORITHM] + ```latex @article{wang2019fcos, @@ -17,8 +17,8 @@ | Head | Backbone | Style | GN-head | Lr schd | Mem (GB) | Inf time (fps) | box AP | Config | Download | |:---------:|:---------:|:-------:|:-------:|:-------:|:--------:|:--------------:|:------:|:------:|:--------:| -| NAS-FCOSHead | R-50 | caffe | Y | 1x | | | 39.4 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/nas_fcos/nas_fcos_nashead_r50_caffe_fpn_gn-head_4x4_1x_coco.py) | [model](http://download.openmmlab.com/mmdetection/v2.0/nas_fcos/nas_fcos_nashead_r50_caffe_fpn_gn-head_4x4_1x_coco/nas_fcos_nashead_r50_caffe_fpn_gn-head_4x4_1x_coco_20200520-1bdba3ce.pth) | [log](http://download.openmmlab.com/mmdetection/v2.0/nas_fcos/nas_fcos_nashead_r50_caffe_fpn_gn-head_4x4_1x_coco/nas_fcos_nashead_r50_caffe_fpn_gn-head_4x4_1x_coco_20200520.log.json) | -| FCOSHead | R-50 | caffe | Y | 1x | | | 38.5 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/nas_fcos/nas_fcos_fcoshead_r50_caffe_fpn_gn-head_4x4_1x_coco.py) | [model](http://download.openmmlab.com/mmdetection/v2.0/nas_fcos/nas_fcos_fcoshead_r50_caffe_fpn_gn-head_4x4_1x_coco/nas_fcos_fcoshead_r50_caffe_fpn_gn-head_4x4_1x_coco_20200521-7fdcbce0.pth) | [log](http://download.openmmlab.com/mmdetection/v2.0/nas_fcos/nas_fcos_fcoshead_r50_caffe_fpn_gn-head_4x4_1x_coco/nas_fcos_fcoshead_r50_caffe_fpn_gn-head_4x4_1x_coco_20200521.log.json) | +| NAS-FCOSHead | R-50 | caffe | Y | 1x | | | 39.4 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/nas_fcos/nas_fcos_nashead_r50_caffe_fpn_gn-head_4x4_1x_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/nas_fcos/nas_fcos_nashead_r50_caffe_fpn_gn-head_4x4_1x_coco/nas_fcos_nashead_r50_caffe_fpn_gn-head_4x4_1x_coco_20200520-1bdba3ce.pth) | [log](https://download.openmmlab.com/mmdetection/v2.0/nas_fcos/nas_fcos_nashead_r50_caffe_fpn_gn-head_4x4_1x_coco/nas_fcos_nashead_r50_caffe_fpn_gn-head_4x4_1x_coco_20200520.log.json) | +| FCOSHead | R-50 | caffe | Y | 1x | | | 38.5 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/nas_fcos/nas_fcos_fcoshead_r50_caffe_fpn_gn-head_4x4_1x_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/nas_fcos/nas_fcos_fcoshead_r50_caffe_fpn_gn-head_4x4_1x_coco/nas_fcos_fcoshead_r50_caffe_fpn_gn-head_4x4_1x_coco_20200521-7fdcbce0.pth) | [log](https://download.openmmlab.com/mmdetection/v2.0/nas_fcos/nas_fcos_fcoshead_r50_caffe_fpn_gn-head_4x4_1x_coco/nas_fcos_fcoshead_r50_caffe_fpn_gn-head_4x4_1x_coco_20200521.log.json) | **Notes:** diff --git a/configs/nas_fcos/metafile.yml b/configs/nas_fcos/metafile.yml new file mode 100644 index 0000000..1ea28cf --- /dev/null +++ b/configs/nas_fcos/metafile.yml @@ -0,0 +1,44 @@ +Collections: + - Name: NAS-FCOS + Metadata: + Training Data: COCO + Training Techniques: + - SGD with Momentum + - Weight Decay + Training Resources: 4x V100 GPUs + Architecture: + - FPN + - NAS-FCOS + - ResNet + Paper: + URL: https://arxiv.org/abs/1906.04423 + Title: 'NAS-FCOS: Fast Neural Architecture Search for Object Detection' + README: configs/nas_fcos/README.md + Code: + URL: https://github.com/open-mmlab/mmdetection/blob/v2.1.0/mmdet/models/detectors/nasfcos.py#L6 + Version: v2.1.0 + +Models: + - Name: nas_fcos_nashead_r50_caffe_fpn_gn-head_4x4_1x_coco + In Collection: NAS-FCOS + Config: configs/nas_fcos/nas_fcos_nashead_r50_caffe_fpn_gn-head_4x4_1x_coco.py + Metadata: + Epochs: 12 + Results: + - Task: Object Detection + Dataset: COCO + Metrics: + box AP: 39.4 + Weights: https://download.openmmlab.com/mmdetection/v2.0/nas_fcos/nas_fcos_nashead_r50_caffe_fpn_gn-head_4x4_1x_coco/nas_fcos_nashead_r50_caffe_fpn_gn-head_4x4_1x_coco_20200520-1bdba3ce.pth + + - Name: nas_fcos_fcoshead_r50_caffe_fpn_gn-head_4x4_1x_coco + In Collection: NAS-FCOS + Config: configs/nas_fcos/nas_fcos_fcoshead_r50_caffe_fpn_gn-head_4x4_1x_coco.py + Metadata: + Epochs: 12 + Results: + - Task: Object Detection + Dataset: COCO + Metrics: + box AP: 38.5 + Weights: https://download.openmmlab.com/mmdetection/v2.0/nas_fcos/nas_fcos_fcoshead_r50_caffe_fpn_gn-head_4x4_1x_coco/nas_fcos_fcoshead_r50_caffe_fpn_gn-head_4x4_1x_coco_20200521-7fdcbce0.pth diff --git a/configs/nas_fcos/nas_fcos_fcoshead_r50_caffe_fpn_gn-head_4x4_1x_coco.py b/configs/nas_fcos/nas_fcos_fcoshead_r50_caffe_fpn_gn-head_4x4_1x_coco.py index 1910312..a455c92 100644 --- a/configs/nas_fcos/nas_fcos_fcoshead_r50_caffe_fpn_gn-head_4x4_1x_coco.py +++ b/configs/nas_fcos/nas_fcos_fcoshead_r50_caffe_fpn_gn-head_4x4_1x_coco.py @@ -5,7 +5,6 @@ model = dict( type='NASFCOS', - pretrained='open-mmlab://detectron2/resnet50_caffe', backbone=dict( type='ResNet', depth=50, @@ -13,7 +12,10 @@ out_indices=(0, 1, 2, 3), frozen_stages=1, norm_cfg=dict(type='BN', requires_grad=False, eps=0), - style='caffe'), + style='caffe', + init_cfg=dict( + type='Pretrained', + checkpoint='open-mmlab://detectron2/resnet50_caffe')), neck=dict( type='NASFCOS_FPN', in_channels=[256, 512, 1024, 2048], diff --git a/configs/nas_fcos/nas_fcos_nashead_r50_caffe_fpn_gn-head_4x4_1x_coco.py b/configs/nas_fcos/nas_fcos_nashead_r50_caffe_fpn_gn-head_4x4_1x_coco.py index ef81123..b779492 100644 --- a/configs/nas_fcos/nas_fcos_nashead_r50_caffe_fpn_gn-head_4x4_1x_coco.py +++ b/configs/nas_fcos/nas_fcos_nashead_r50_caffe_fpn_gn-head_4x4_1x_coco.py @@ -5,7 +5,6 @@ model = dict( type='NASFCOS', - pretrained='open-mmlab://detectron2/resnet50_caffe', backbone=dict( type='ResNet', depth=50, @@ -13,7 +12,10 @@ out_indices=(0, 1, 2, 3), frozen_stages=1, norm_cfg=dict(type='BN', requires_grad=False, eps=0), - style='caffe'), + style='caffe', + init_cfg=dict( + type='Pretrained', + checkpoint='open-mmlab://detectron2/resnet50_caffe')), neck=dict( type='NASFCOS_FPN', in_channels=[256, 512, 1024, 2048], diff --git a/configs/nas_fpn/README.md b/configs/nas_fpn/README.md index 81f25b2..9862b9a 100644 --- a/configs/nas_fpn/README.md +++ b/configs/nas_fpn/README.md @@ -2,7 +2,7 @@ ## Introduction -[ALGORITHM] + ```latex @inproceedings{ghiasi2019fpn, @@ -20,7 +20,7 @@ We benchmark the new training schedule (crop training, large batch, unfrozen BN, | Backbone | Lr schd | Mem (GB) | Inf time (fps) | box AP | Config | Download | |:-----------:|:-------:|:--------:|:--------------:|:------:|:------:|:--------:| -| R-50-FPN | 50e | 12.9 | 22.9 | 37.9 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/nas_fpn/retinanet_r50_fpn_crop640_50e_coco.py) | [model](http://download.openmmlab.com/mmdetection/v2.0/nas_fpn/retinanet_r50_fpn_crop640_50e_coco/retinanet_r50_fpn_crop640_50e_coco-9b953d76.pth) | [log](http://download.openmmlab.com/mmdetection/v2.0/nas_fpn/retinanet_r50_fpn_crop640_50e_coco/retinanet_r50_fpn_crop640_50e_coco_20200529_095329.log.json) | -| R-50-NASFPN | 50e | 13.2 | 23.0 | 40.5 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/nas_fpn/retinanet_r50_nasfpn_crop640_50e_coco.py) | [model](http://download.openmmlab.com/mmdetection/v2.0/nas_fpn/retinanet_r50_nasfpn_crop640_50e_coco/retinanet_r50_nasfpn_crop640_50e_coco-0ad1f644.pth) | [log](http://download.openmmlab.com/mmdetection/v2.0/nas_fpn/retinanet_r50_nasfpn_crop640_50e_coco/retinanet_r50_nasfpn_crop640_50e_coco_20200528_230008.log.json) | +| R-50-FPN | 50e | 12.9 | 22.9 | 37.9 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/nas_fpn/retinanet_r50_fpn_crop640_50e_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/nas_fpn/retinanet_r50_fpn_crop640_50e_coco/retinanet_r50_fpn_crop640_50e_coco-9b953d76.pth) | [log](https://download.openmmlab.com/mmdetection/v2.0/nas_fpn/retinanet_r50_fpn_crop640_50e_coco/retinanet_r50_fpn_crop640_50e_coco_20200529_095329.log.json) | +| R-50-NASFPN | 50e | 13.2 | 23.0 | 40.5 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/nas_fpn/retinanet_r50_nasfpn_crop640_50e_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/nas_fpn/retinanet_r50_nasfpn_crop640_50e_coco/retinanet_r50_nasfpn_crop640_50e_coco-0ad1f644.pth) | [log](https://download.openmmlab.com/mmdetection/v2.0/nas_fpn/retinanet_r50_nasfpn_crop640_50e_coco/retinanet_r50_nasfpn_crop640_50e_coco_20200528_230008.log.json) | **Note**: We find that it is unstable to train NAS-FPN and there is a small chance that results can be 3% mAP lower. diff --git a/configs/nas_fpn/metafile.yml b/configs/nas_fpn/metafile.yml new file mode 100644 index 0000000..ab8d649 --- /dev/null +++ b/configs/nas_fpn/metafile.yml @@ -0,0 +1,59 @@ +Collections: + - Name: NAS-FPN + Metadata: + Training Data: COCO + Training Techniques: + - SGD with Momentum + - Weight Decay + Training Resources: 8x V100 GPUs + Architecture: + - NAS-FPN + - ResNet + Paper: + URL: https://arxiv.org/abs/1904.07392 + Title: 'NAS-FPN: Learning Scalable Feature Pyramid Architecture for Object Detection' + README: configs/nas_fpn/README.md + Code: + URL: https://github.com/open-mmlab/mmdetection/blob/v2.0.0/mmdet/models/necks/nas_fpn.py#L67 + Version: v2.0.0 + +Models: + - Name: retinanet_r50_fpn_crop640_50e_coco + In Collection: NAS-FPN + Config: configs/nas_fpn/retinanet_r50_fpn_crop640_50e_coco.py + Metadata: + Training Memory (GB): 12.9 + inference time (ms/im): + - value: 43.67 + hardware: V100 + backend: PyTorch + batch size: 1 + mode: FP32 + resolution: (800, 1333) + Epochs: 50 + Results: + - Task: Object Detection + Dataset: COCO + Metrics: + box AP: 37.9 + Weights: https://download.openmmlab.com/mmdetection/v2.0/nas_fpn/retinanet_r50_fpn_crop640_50e_coco/retinanet_r50_fpn_crop640_50e_coco-9b953d76.pth + + - Name: retinanet_r50_nasfpn_crop640_50e_coco + In Collection: NAS-FPN + Config: configs/nas_fpn/retinanet_r50_nasfpn_crop640_50e_coco.py + Metadata: + Training Memory (GB): 13.2 + inference time (ms/im): + - value: 43.48 + hardware: V100 + backend: PyTorch + batch size: 1 + mode: FP32 + resolution: (800, 1333) + Epochs: 50 + Results: + - Task: Object Detection + Dataset: COCO + Metrics: + box AP: 40.5 + Weights: https://download.openmmlab.com/mmdetection/v2.0/nas_fpn/retinanet_r50_nasfpn_crop640_50e_coco/retinanet_r50_nasfpn_crop640_50e_coco-0ad1f644.pth diff --git a/configs/nas_fpn/retinanet_r50_fpn_crop640_50e_coco.py b/configs/nas_fpn/retinanet_r50_fpn_crop640_50e_coco.py index d4c7c98..6ea44a0 100644 --- a/configs/nas_fpn/retinanet_r50_fpn_crop640_50e_coco.py +++ b/configs/nas_fpn/retinanet_r50_fpn_crop640_50e_coco.py @@ -5,7 +5,6 @@ cudnn_benchmark = True norm_cfg = dict(type='BN', requires_grad=True) model = dict( - pretrained='torchvision://resnet50', backbone=dict( type='ResNet', depth=50, @@ -14,7 +13,8 @@ frozen_stages=1, norm_cfg=norm_cfg, norm_eval=False, - style='pytorch'), + style='pytorch', + init_cfg=dict(type='Pretrained', checkpoint='torchvision://resnet50')), neck=dict( relu_before_extra_convs=True, no_norm_on_lateral=True, diff --git a/configs/nas_fpn/retinanet_r50_nasfpn_crop640_50e_coco.py b/configs/nas_fpn/retinanet_r50_nasfpn_crop640_50e_coco.py index 8a2ef26..3e03919 100644 --- a/configs/nas_fpn/retinanet_r50_nasfpn_crop640_50e_coco.py +++ b/configs/nas_fpn/retinanet_r50_nasfpn_crop640_50e_coco.py @@ -7,7 +7,6 @@ norm_cfg = dict(type='BN', requires_grad=True) model = dict( type='RetinaNet', - pretrained='torchvision://resnet50', backbone=dict( type='ResNet', depth=50, @@ -16,7 +15,8 @@ frozen_stages=1, norm_cfg=norm_cfg, norm_eval=False, - style='pytorch'), + style='pytorch', + init_cfg=dict(type='Pretrained', checkpoint='torchvision://resnet50')), neck=dict(type='NASFPN', stack_times=7, norm_cfg=norm_cfg), bbox_head=dict(type='RetinaSepBNHead', num_ins=5, norm_cfg=norm_cfg), # training and testing settings diff --git a/configs/paa/README.md b/configs/paa/README.md index 9960dcf..3e9b6e3 100644 --- a/configs/paa/README.md +++ b/configs/paa/README.md @@ -1,6 +1,6 @@ # Probabilistic Anchor Assignment with IoU Prediction for Object Detection -[ALGORITHM] + ```latex @inproceedings{paa-eccv2020, @@ -19,16 +19,16 @@ Prediction for Object Detection. | Backbone | Lr schd | Mem (GB) | Score voting | box AP | Config | Download | |:-----------:|:-------:|:--------:|:------------:|:------:|:------:|:--------:| -| R-50-FPN | 12e | 3.7 | True | 40.4 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/paa/paa_r50_fpn_1x_coco.py) | [model](http://download.openmmlab.com/mmdetection/v2.0/paa/paa_r50_fpn_1x_coco/paa_r50_fpn_1x_coco_20200821-936edec3.pth) | [log](http://download.openmmlab.com/mmdetection/v2.0/paa/paa_r50_fpn_1x_coco/paa_r50_fpn_1x_coco_20200821-936edec3.log.json) | +| R-50-FPN | 12e | 3.7 | True | 40.4 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/paa/paa_r50_fpn_1x_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/paa/paa_r50_fpn_1x_coco/paa_r50_fpn_1x_coco_20200821-936edec3.pth) | [log](https://download.openmmlab.com/mmdetection/v2.0/paa/paa_r50_fpn_1x_coco/paa_r50_fpn_1x_coco_20200821-936edec3.log.json) | | R-50-FPN | 12e | 3.7 | False | 40.2 | - | -| R-50-FPN | 18e | 3.7 | True | 41.4 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/paa/paa_r50_fpn_1.5x_coco.py) | [model](http://download.openmmlab.com/mmdetection/v2.0/paa/paa_r50_fpn_1.5x_coco/paa_r50_fpn_1.5x_coco_20200823-805d6078.pth) | [log](http://download.openmmlab.com/mmdetection/v2.0/paa/paa_r50_fpn_1.5x_coco/paa_r50_fpn_1.5x_coco_20200823-805d6078.log.json) | +| R-50-FPN | 18e | 3.7 | True | 41.4 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/paa/paa_r50_fpn_1.5x_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/paa/paa_r50_fpn_1.5x_coco/paa_r50_fpn_1.5x_coco_20200823-805d6078.pth) | [log](https://download.openmmlab.com/mmdetection/v2.0/paa/paa_r50_fpn_1.5x_coco/paa_r50_fpn_1.5x_coco_20200823-805d6078.log.json) | | R-50-FPN | 18e | 3.7 | False | 41.2 | - | -| R-50-FPN | 24e | 3.7 | True | 41.6 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/paa/paa_r50_fpn_2x_coco.py) | [model](http://download.openmmlab.com/mmdetection/v2.0/paa/paa_r50_fpn_2x_coco/paa_r50_fpn_2x_coco_20200821-c98bfc4e.pth) | [log](http://download.openmmlab.com/mmdetection/v2.0/paa/paa_r50_fpn_2x_coco/paa_r50_fpn_2x_coco_20200821-c98bfc4e.log.json) | -| R-50-FPN | 36e | 3.7 | True | 43.3 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/paa/paa_r50_fpn_mstrain_3x_coco.py) | [model](http://download.openmmlab.com/mmdetection/v2.0/paa/paa_r50_fpn_mstrain_3x_coco/paa_r50_fpn_mstrain_3x_coco_20210121_145722-06a6880b.pth) | [log](http://download.openmmlab.com/mmdetection/v2.0/paa/paa_r50_fpn_mstrain_3x_coco/paa_r50_fpn_mstrain_3x_coco_20210121_145722.log.json) | -| R-101-FPN | 12e | 6.2 | True | 42.6 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/paa/paa_r101_fpn_1x_coco.py) | [model](http://download.openmmlab.com/mmdetection/v2.0/paa/paa_r101_fpn_1x_coco/paa_r101_fpn_1x_coco_20200821-0a1825a4.pth) | [log](http://download.openmmlab.com/mmdetection/v2.0/paa/paa_r101_fpn_1x_coco/paa_r101_fpn_1x_coco_20200821-0a1825a4.log.json) | +| R-50-FPN | 24e | 3.7 | True | 41.6 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/paa/paa_r50_fpn_2x_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/paa/paa_r50_fpn_2x_coco/paa_r50_fpn_2x_coco_20200821-c98bfc4e.pth) | [log](https://download.openmmlab.com/mmdetection/v2.0/paa/paa_r50_fpn_2x_coco/paa_r50_fpn_2x_coco_20200821-c98bfc4e.log.json) | +| R-50-FPN | 36e | 3.7 | True | 43.3 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/paa/paa_r50_fpn_mstrain_3x_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/paa/paa_r50_fpn_mstrain_3x_coco/paa_r50_fpn_mstrain_3x_coco_20210121_145722-06a6880b.pth) | [log](https://download.openmmlab.com/mmdetection/v2.0/paa/paa_r50_fpn_mstrain_3x_coco/paa_r50_fpn_mstrain_3x_coco_20210121_145722.log.json) | +| R-101-FPN | 12e | 6.2 | True | 42.6 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/paa/paa_r101_fpn_1x_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/paa/paa_r101_fpn_1x_coco/paa_r101_fpn_1x_coco_20200821-0a1825a4.pth) | [log](https://download.openmmlab.com/mmdetection/v2.0/paa/paa_r101_fpn_1x_coco/paa_r101_fpn_1x_coco_20200821-0a1825a4.log.json) | | R-101-FPN | 12e | 6.2 | False | 42.4 | - | -| R-101-FPN | 24e | 6.2 | True | 43.5 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/paa/paa_r101_fpn_2x_coco.py) | [model](http://download.openmmlab.com/mmdetection/v2.0/paa/paa_r101_fpn_2x_coco/paa_r101_fpn_2x_coco_20200821-6829f96b.pth) | [log](http://download.openmmlab.com/mmdetection/v2.0/paa/paa_r101_fpn_2x_coco/paa_r101_fpn_2x_coco_20200821-6829f96b.log.json) | -| R-101-FPN | 36e | 6.2 | True | 45.1 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/paa/paa_r101_fpn_mstrain_3x_coco.py) | [model](http://download.openmmlab.com/mmdetection/v2.0/paa/paa_r101_fpn_mstrain_3x_coco/paa_r101_fpn_mstrain_3x_coco_20210122_084202-83250d22.pth) | [log](http://download.openmmlab.com/mmdetection/v2.0/paa/paa_r101_fpn_mstrain_3x_coco/paa_r101_fpn_mstrain_3x_coco_20210122_084202.log.json) | +| R-101-FPN | 24e | 6.2 | True | 43.5 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/paa/paa_r101_fpn_2x_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/paa/paa_r101_fpn_2x_coco/paa_r101_fpn_2x_coco_20200821-6829f96b.pth) | [log](https://download.openmmlab.com/mmdetection/v2.0/paa/paa_r101_fpn_2x_coco/paa_r101_fpn_2x_coco_20200821-6829f96b.log.json) | +| R-101-FPN | 36e | 6.2 | True | 45.1 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/paa/paa_r101_fpn_mstrain_3x_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/paa/paa_r101_fpn_mstrain_3x_coco/paa_r101_fpn_mstrain_3x_coco_20210122_084202-83250d22.pth) | [log](https://download.openmmlab.com/mmdetection/v2.0/paa/paa_r101_fpn_mstrain_3x_coco/paa_r101_fpn_mstrain_3x_coco_20210122_084202.log.json) | **Note**: diff --git a/configs/paa/metafile.yml b/configs/paa/metafile.yml new file mode 100644 index 0000000..e08b663 --- /dev/null +++ b/configs/paa/metafile.yml @@ -0,0 +1,104 @@ +Collections: + - Name: PAA + Metadata: + Training Data: COCO + Training Techniques: + - SGD with Momentum + - Weight Decay + Training Resources: 8x V100 GPUs + Architecture: + - FPN + - Probabilistic Anchor Assignment + - ResNet + Paper: + URL: https://arxiv.org/abs/2007.08103 + Title: 'Probabilistic Anchor Assignment with IoU Prediction for Object Detection' + README: configs/paa/README.md + Code: + URL: https://github.com/open-mmlab/mmdetection/blob/v2.4.0/mmdet/models/detectors/paa.py#L6 + Version: v2.4.0 + +Models: + - Name: paa_r50_fpn_1x_coco + In Collection: PAA + Config: configs/paa/paa_r50_fpn_1x_coco.py + Metadata: + Training Memory (GB): 3.7 + Results: + - Task: Object Detection + Dataset: COCO + Metrics: + box AP: 40.4 + Weights: https://download.openmmlab.com/mmdetection/v2.0/paa/paa_r50_fpn_1x_coco/paa_r50_fpn_1x_coco_20200821-936edec3.pth + + - Name: paa_r50_fpn_1.5x_coco + In Collection: PAA + Config: configs/paa/paa_r50_fpn_1.5x_coco.py + Metadata: + Training Memory (GB): 3.7 + Results: + - Task: Object Detection + Dataset: COCO + Metrics: + box AP: 41.4 + Weights: https://download.openmmlab.com/mmdetection/v2.0/paa/paa_r50_fpn_1.5x_coco/paa_r50_fpn_1.5x_coco_20200823-805d6078.pth + + - Name: paa_r50_fpn_2x_coco + In Collection: PAA + Config: configs/paa/paa_r50_fpn_2x_coco.py + Metadata: + Training Memory (GB): 3.7 + Results: + - Task: Object Detection + Dataset: COCO + Metrics: + box AP: 41.6 + Weights: https://download.openmmlab.com/mmdetection/v2.0/paa/paa_r50_fpn_2x_coco/paa_r50_fpn_2x_coco_20200821-c98bfc4e.pth + + - Name: paa_r50_fpn_mstrain_3x_coco + In Collection: PAA + Config: configs/paa/paa_r50_fpn_mstrain_3x_coco.py + Metadata: + Training Memory (GB): 3.7 + Results: + - Task: Object Detection + Dataset: COCO + Metrics: + box AP: 43.3 + Weights: https://download.openmmlab.com/mmdetection/v2.0/paa/paa_r50_fpn_mstrain_3x_coco/paa_r50_fpn_mstrain_3x_coco_20210121_145722-06a6880b.pth + + - Name: paa_r101_fpn_1x_coco + In Collection: PAA + Config: configs/paa/paa_r101_fpn_1x_coco.py + Metadata: + Training Memory (GB): 6.2 + Results: + - Task: Object Detection + Dataset: COCO + Metrics: + box AP: 42.6 + Weights: https://download.openmmlab.com/mmdetection/v2.0/paa/paa_r101_fpn_1x_coco/paa_r101_fpn_1x_coco_20200821-0a1825a4.pth + + - Name: paa_r101_fpn_2x_coco + In Collection: PAA + Config: configs/paa/paa_r101_fpn_2x_coco.py + Metadata: + Training Memory (GB): 6.2 + Results: + - Task: Object Detection + Dataset: COCO + Metrics: + box AP: 43.5 + Weights: https://download.openmmlab.com/mmdetection/v2.0/paa/paa_r101_fpn_2x_coco/paa_r101_fpn_2x_coco_20200821-6829f96b.pth + + - Name: paa_r101_fpn_mstrain_3x_coco + In Collection: PAA + Config: configs/paa/paa_r101_fpn_mstrain_3x_coco.py + Metadata: + Training Memory (GB): 6.2 + Results: + - Task: Object Detection + Dataset: COCO + Metrics: + box AP: 45.1 + Weights: https://download.openmmlab.com/mmdetection/v2.0/paa/paa_r101_fpn_mstrain_3x_coco/paa_r101_fpn_mstrain_3x_coco_20210122_084202-83250d22.pth diff --git a/configs/paa/paa_r101_fpn_1x_coco.py b/configs/paa/paa_r101_fpn_1x_coco.py index 9d2b1a6..94f1c27 100644 --- a/configs/paa/paa_r101_fpn_1x_coco.py +++ b/configs/paa/paa_r101_fpn_1x_coco.py @@ -1,2 +1,6 @@ _base_ = './paa_r50_fpn_1x_coco.py' -model = dict(pretrained='torchvision://resnet101', backbone=dict(depth=101)) +model = dict( + backbone=dict( + depth=101, + init_cfg=dict(type='Pretrained', + checkpoint='torchvision://resnet101'))) diff --git a/configs/paa/paa_r101_fpn_mstrain_3x_coco.py b/configs/paa/paa_r101_fpn_mstrain_3x_coco.py index 6f23df7..71858ed 100644 --- a/configs/paa/paa_r101_fpn_mstrain_3x_coco.py +++ b/configs/paa/paa_r101_fpn_mstrain_3x_coco.py @@ -1,2 +1,6 @@ _base_ = './paa_r50_fpn_mstrain_3x_coco.py' -model = dict(pretrained='torchvision://resnet101', backbone=dict(depth=101)) +model = dict( + backbone=dict( + depth=101, + init_cfg=dict(type='Pretrained', + checkpoint='torchvision://resnet101'))) diff --git a/configs/paa/paa_r50_fpn_1x_coco.py b/configs/paa/paa_r50_fpn_1x_coco.py index cd84410..4c9c4aa 100644 --- a/configs/paa/paa_r50_fpn_1x_coco.py +++ b/configs/paa/paa_r50_fpn_1x_coco.py @@ -4,7 +4,6 @@ ] model = dict( type='PAA', - pretrained='torchvision://resnet50', backbone=dict( type='ResNet', depth=50, @@ -13,7 +12,8 @@ frozen_stages=1, norm_cfg=dict(type='BN', requires_grad=True), norm_eval=True, - style='pytorch'), + style='pytorch', + init_cfg=dict(type='Pretrained', checkpoint='torchvision://resnet50')), neck=dict( type='FPN', in_channels=[256, 512, 1024, 2048], diff --git a/configs/pafpn/README.md b/configs/pafpn/README.md index 03227e2..9602c6d 100644 --- a/configs/pafpn/README.md +++ b/configs/pafpn/README.md @@ -2,7 +2,7 @@ ## Introduction -[ALGORITHM] + ``` @inproceedings{liu2018path, @@ -23,4 +23,4 @@ | Backbone | style | Lr schd | Mem (GB) | Inf time (fps) | box AP | mask AP | Config | Download | |:-------------:|:----------:|:-------:|:--------:|:--------------:|:------:|:-------:|:------:|:--------:| -| R-50-FPN | pytorch | 1x | 4.0 | 17.2 | 37.5 | | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/pafpn/faster_rcnn_r50_pafpn_1x_coco.py) | [model](http://download.openmmlab.com/mmdetection/v2.0/pafpn/faster_rcnn_r50_pafpn_1x_coco/faster_rcnn_r50_pafpn_1x_coco_bbox_mAP-0.375_20200503_105836-b7b4b9bd.pth) | [log](http://download.openmmlab.com/mmdetection/v2.0/pafpn/faster_rcnn_r50_pafpn_1x_coco/faster_rcnn_r50_pafpn_1x_coco_20200503_105836.log.json) | +| R-50-FPN | pytorch | 1x | 4.0 | 17.2 | 37.5 | | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/pafpn/faster_rcnn_r50_pafpn_1x_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/pafpn/faster_rcnn_r50_pafpn_1x_coco/faster_rcnn_r50_pafpn_1x_coco_bbox_mAP-0.375_20200503_105836-b7b4b9bd.pth) | [log](https://download.openmmlab.com/mmdetection/v2.0/pafpn/faster_rcnn_r50_pafpn_1x_coco/faster_rcnn_r50_pafpn_1x_coco_20200503_105836.log.json) | diff --git a/configs/pafpn/metafile.yml b/configs/pafpn/metafile.yml new file mode 100644 index 0000000..f9cf97c --- /dev/null +++ b/configs/pafpn/metafile.yml @@ -0,0 +1,38 @@ +Collections: + - Name: PAFPN + Metadata: + Training Data: COCO + Training Techniques: + - SGD with Momentum + - Weight Decay + Training Resources: 8x V100 GPUs + Architecture: + - PAFPN + Paper: + URL: https://arxiv.org/abs/1803.01534 + Title: 'Path Aggregation Network for Instance Segmentation' + README: configs/pafpn/README.md + Code: + URL: https://github.com/open-mmlab/mmdetection/blob/v2.0.0/mmdet/models/necks/pafpn.py#L11 + Version: v2.0.0 + +Models: + - Name: faster_rcnn_r50_pafpn_1x_coco + In Collection: PAFPN + Config: configs/pafpn/faster_rcnn_r50_pafpn_1x_coco.py + Metadata: + Training Memory (GB): 4.0 + inference time (ms/im): + - value: 58.14 + hardware: V100 + backend: PyTorch + batch size: 1 + mode: FP32 + resolution: (800, 1333) + Epochs: 12 + Results: + - Task: Object Detection + Dataset: COCO + Metrics: + box AP: 37.5 + Weights: https://download.openmmlab.com/mmdetection/v2.0/pafpn/faster_rcnn_r50_pafpn_1x_coco/faster_rcnn_r50_pafpn_1x_coco_bbox_mAP-0.375_20200503_105836-b7b4b9bd.pth diff --git a/configs/panoptic_fpn/README.md b/configs/panoptic_fpn/README.md new file mode 100644 index 0000000..d209183 --- /dev/null +++ b/configs/panoptic_fpn/README.md @@ -0,0 +1,50 @@ +# Panoptic feature pyramid networks +## Introduction + + +The base method for panoptic segmentation task. + +``` +@inproceedings{kirillov2018panopticfpn, + author = { + Alexander Kirillov, + Ross Girshick, + Kaiming He, + Piotr Dollar, + }, + title = {Panoptic Feature Pyramid Networks}, + booktitle = {Proceedings of IEEE Conference on Computer Vision and Pattern Recognition (CVPR)}, + year = {2019} +} +``` + +## Dataset + +PanopticFPN requires COCO and [COCO-panoptic](http://images.cocodataset.org/annotations/panoptic_annotations_trainval2017.zip) dataset for training and evaluation. You need to download and extract it in the COCO dataset path. +The directory should be like this. + +```none +mmdetection +├── mmdet +├── tools +├── configs +├── data +│ ├── coco +│ │ ├── annotations +│ │ │ ├── panoptic_train2017.json +│ │ │ ├── panoptic_train2017 +│ │ │ ├── panoptic_val2017.json +│ │ │ ├── panoptic_val2017 +│ │ ├── train2017 +│ │ ├── val2017 +│ │ ├── test2017 +``` + +## Results and Models + +| Backbone | style | Lr schd | Mem (GB) | Inf time (fps) | PQ | SQ | RQ | PQ_th | SQ_th | RQ_th | PQ_st | SQ_st | RQ_st | Config | Download | +|:-------------:|:----------:|:-------:|:--------:|:--------------:|:----:|:----:|:----:|:-----:|:-----:|:-----:|:-----:|:-----:|:-----:|:------:|:--------:| +| R-50-FPN | pytorch | 1x | 4.7 | | 40.2 | 77.8 | 49.3 | 47.8 | 80.9 | 57.5 | 28.9 | 73.1 | 37.0 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/panoptic_fpn/panoptic_fpn_r50_fpn_1x_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/panoptic_fpn/panoptic_fpn_r50_fpn_1x_coco/panoptic_fpn_r50_fpn_1x_coco_20210821_101153-9668fd13.pth) | [log](https://download.openmmlab.com/mmdetection/v2.0/panoptic_fpn/panoptic_fpn_r50_fpn_1x_coco/panoptic_fpn_r50_fpn_1x_coco_20210821_101153.log.json) | +| R-50-FPN | pytorch | 3x | - | - | 42.5 | 78.1 | 51.7 | 50.3 | 81.5 | 60.3 | 30.7 | 73.0 | 38.8 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/panoptic_fpn/panoptic_fpn_r50_fpn_mstrain_3x_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/panoptic_fpn/panoptic_fpn_r50_fpn_mstrain_3x_coco/panoptic_fpn_r50_fpn_mstrain_3x_coco_20210824_171155-5650f98b.pth) | [log](https://download.openmmlab.com/mmdetection/v2.0/panoptic_fpn/panoptic_fpn_r50_fpn_mstrain_3x_coco/panoptic_fpn_r50_fpn_mstrain_3x_coco_20210824_171155.log.json) | +| R-101-FPN | pytorch | 1x | 6.7 | | 42.2 | 78.3 | 51.4 | 50.1 | 81.4 | 59.9 | 30.3 | 73.6 | 38.5 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/panoptic_fpn/panoptic_fpn_r101_fpn_1x_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/panoptic_fpn/panoptic_fpn_r101_fpn_1x_coco/panoptic_fpn_r101_fpn_1x_coco_20210820_193950-ab9157a2.pth) | [log](https://download.openmmlab.com/mmdetection/v2.0/panoptic_fpn/panoptic_fpn_r101_fpn_1x_coco/panoptic_fpn_r101_fpn_1x_coco_20210820_193950.log.json) | +| R-101-FPN | pytorch | 3x | - | - | 44.1 | 78.9 | 53.6 | 52.1 | 81.7 | 62.3 | 32.0 | 74.6 | 40.3 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/panoptic_fpn/panoptic_fpn_r101_fpn_mstrain_3x_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/panoptic_fpn/panoptic_fpn_r101_fpn_mstrain_3x_coco/panoptic_fpn_r101_fpn_mstrain_3x_coco_20210823_114712-9c99acc4.pth) | [log](https://download.openmmlab.com/mmdetection/v2.0/panoptic_fpn/panoptic_fpn_r101_fpn_mstrain_3x_coco/panoptic_fpn_r101_fpn_mstrain_3x_coco_20210823_114712.log.json) | diff --git a/configs/panoptic_fpn/metafile.yml b/configs/panoptic_fpn/metafile.yml new file mode 100644 index 0000000..8c9d39d --- /dev/null +++ b/configs/panoptic_fpn/metafile.yml @@ -0,0 +1,70 @@ +Collections: + - Name: PanopticFPN + Metadata: + Training Data: COCO + Training Techniques: + - SGD with Momentum + - Weight Decay + Training Resources: 8x V100 GPUs + Architecture: + - PanopticFPN + Paper: + URL: https://arxiv.org/pdf/1901.02446 + Title: 'Panoptic feature pyramid networks' + README: configs/panoptic_fpn/README.md + Code: + URL: https://github.com/open-mmlab/mmdetection/blob/v2.16.0/mmdet/models/detectors/panoptic_fpn.py#L7 + Version: v2.16.0 + +Models: + - Name: panoptic_fpn_r50_fpn_1x_coco + In Collection: PanopticFPN + Config: configs/panoptic_fpn/panoptic_fpn_r50_fpn_1x_coco.py + Metadata: + Training Memory (GB): 4.6 + Epochs: 12 + Results: + - Task: Panoptic Segmentation + Dataset: COCO + Metrics: + PQ: 40.2 + Weights: https://download.openmmlab.com/mmdetection/v2.0/panoptic_fpn/panoptic_fpn_r50_fpn_1x_coco/panoptic_fpn_r50_fpn_1x_coco_20210821_101153-9668fd13.pth + + - Name: panoptic_fpn_r50_fpn_mstrain_3x_coco + In Collection: PanopticFPN + Config: configs/panoptic_fpn/panoptic_fpn_r50_fpn_mstrain_3x_coco.py + Metadata: + Training Memory (GB): 4.6 + Epochs: 36 + Results: + - Task: Panoptic Segmentation + Dataset: COCO + Metrics: + PQ: 42.5 + Weights: https://download.openmmlab.com/mmdetection/v2.0/panoptic_fpn/panoptic_fpn_r50_fpn_mstrain_3x_coco/panoptic_fpn_r50_fpn_mstrain_3x_coco_20210824_171155-5650f98b.pth + + - Name: panoptic_fpn_r101_fpn_1x_coco + In Collection: PanopticFPN + Config: configs/panoptic_fpn/panoptic_fpn_r101_fpn_1x_coco.py + Metadata: + Training Memory (GB): 6.5 + Epochs: 12 + Results: + - Task: Panoptic Segmentation + Dataset: COCO + Metrics: + PQ: 42.2 + Weights: https://download.openmmlab.com/mmdetection/v2.0/panoptic_fpn/panoptic_fpn_r101_fpn_1x_coco/panoptic_fpn_r101_fpn_1x_coco_20210820_193950-ab9157a2.pth + + - Name: panoptic_fpn_r101_fpn_mstrain_3x_coco + In Collection: PanopticFPN + Config: configs/panoptic_fpn/panoptic_fpn_r101_fpn_mstrain_3x_coco.py + Metadata: + Training Memory (GB): 6.5 + Epochs: 36 + Results: + - Task: Panoptic Segmentation + Dataset: COCO + Metrics: + PQ: 44.1 + Weights: https://download.openmmlab.com/mmdetection/v2.0/panoptic_fpn/panoptic_fpn_r101_fpn_mstrain_3x_coco/panoptic_fpn_r101_fpn_mstrain_3x_coco_20210823_114712-9c99acc4.pth diff --git a/configs/panoptic_fpn/panoptic_fpn_r101_fpn_1x_coco.py b/configs/panoptic_fpn/panoptic_fpn_r101_fpn_1x_coco.py new file mode 100644 index 0000000..78b8079 --- /dev/null +++ b/configs/panoptic_fpn/panoptic_fpn_r101_fpn_1x_coco.py @@ -0,0 +1,6 @@ +_base_ = './panoptic_fpn_r50_fpn_1x_coco.py' +model = dict( + backbone=dict( + depth=101, + init_cfg=dict(type='Pretrained', + checkpoint='torchvision://resnet101'))) diff --git a/configs/panoptic_fpn/panoptic_fpn_r101_fpn_mstrain_3x_coco.py b/configs/panoptic_fpn/panoptic_fpn_r101_fpn_mstrain_3x_coco.py new file mode 100644 index 0000000..057e481 --- /dev/null +++ b/configs/panoptic_fpn/panoptic_fpn_r101_fpn_mstrain_3x_coco.py @@ -0,0 +1,6 @@ +_base_ = './panoptic_fpn_r50_fpn_mstrain_3x_coco.py' +model = dict( + backbone=dict( + depth=101, + init_cfg=dict(type='Pretrained', + checkpoint='torchvision://resnet101'))) diff --git a/configs/panoptic_fpn/panoptic_fpn_r50_fpn_1x_coco.py b/configs/panoptic_fpn/panoptic_fpn_r50_fpn_1x_coco.py new file mode 100644 index 0000000..6f463c1 --- /dev/null +++ b/configs/panoptic_fpn/panoptic_fpn_r50_fpn_1x_coco.py @@ -0,0 +1,32 @@ +_base_ = [ + '../_base_/models/mask_rcnn_r50_fpn.py', + '../_base_/datasets/coco_panoptic.py', + '../_base_/schedules/schedule_1x.py', '../_base_/default_runtime.py' +] +model = dict( + type='PanopticFPN', + semantic_head=dict( + type='PanopticFPNHead', + num_classes=54, + in_channels=256, + inner_channels=128, + start_level=0, + end_level=4, + norm_cfg=dict(type='GN', num_groups=32, requires_grad=True), + conv_cfg=None, + loss_seg=dict( + type='CrossEntropyLoss', ignore_index=-1, loss_weight=0.5)), + panoptic_fusion_head=dict( + type='HeuristicFusionHead', + num_things_classes=80, + num_stuff_classes=53), + test_cfg=dict( + panoptic=dict( + score_thr=0.6, + max_per_img=100, + mask_thr_binary=0.5, + mask_overlap=0.5, + nms=dict(type='nms', iou_threshold=0.5, class_agnostic=True), + stuff_area_limit=4096))) + +custom_hooks = [] diff --git a/configs/panoptic_fpn/panoptic_fpn_r50_fpn_mstrain_3x_coco.py b/configs/panoptic_fpn/panoptic_fpn_r50_fpn_mstrain_3x_coco.py new file mode 100644 index 0000000..b510935 --- /dev/null +++ b/configs/panoptic_fpn/panoptic_fpn_r50_fpn_mstrain_3x_coco.py @@ -0,0 +1,61 @@ +_base_ = './panoptic_fpn_r50_fpn_1x_coco.py' + +# dataset settings +dataset_type = 'CocoPanopticDataset' +data_root = 'data/coco/' +img_norm_cfg = dict( + mean=[123.675, 116.28, 103.53], std=[58.395, 57.12, 57.375], to_rgb=True) + +# In mstrain 3x config, img_scale=[(1333, 640), (1333, 800)], +# multiscale_mode='range' +train_pipeline = [ + dict(type='LoadImageFromFile'), + dict( + type='LoadPanopticAnnotations', + with_bbox=True, + with_mask=True, + with_seg=True), + dict( + type='Resize', + img_scale=[(1333, 640), (1333, 800)], + multiscale_mode='range', + keep_ratio=True), + dict(type='RandomFlip', flip_ratio=0.5), + dict(type='Normalize', **img_norm_cfg), + dict(type='Pad', size_divisor=32), + dict(type='SegRescale', scale_factor=1 / 4), + dict(type='DefaultFormatBundle'), + dict( + type='Collect', + keys=['img', 'gt_bboxes', 'gt_labels', 'gt_masks', 'gt_semantic_seg']), +] +test_pipeline = [ + dict(type='LoadImageFromFile'), + dict( + type='MultiScaleFlipAug', + img_scale=(1333, 800), + flip=False, + transforms=[ + dict(type='Resize', keep_ratio=True), + dict(type='RandomFlip'), + dict(type='Normalize', **img_norm_cfg), + dict(type='Pad', size_divisor=32), + dict(type='ImageToTensor', keys=['img']), + dict(type='Collect', keys=['img']), + ]) +] + +# Use RepeatDataset to speed up training +data = dict( + train=dict( + _delete_=True, + type='RepeatDataset', + times=3, + dataset=dict( + type=dataset_type, + ann_file=data_root + 'annotations/panoptic_train2017.json', + img_prefix=data_root + 'train2017/', + seg_prefix=data_root + 'annotations/panoptic_train2017/', + pipeline=train_pipeline)), + val=dict(pipeline=test_pipeline), + test=dict(pipeline=test_pipeline)) diff --git a/configs/pascal_voc/README.md b/configs/pascal_voc/README.md index f730242..69e0220 100644 --- a/configs/pascal_voc/README.md +++ b/configs/pascal_voc/README.md @@ -1,6 +1,6 @@ # PASCAL VOC Dataset -[DATASET] + ``` @Article{Everingham10, @@ -19,5 +19,5 @@ | Architecture | Backbone | Style | Lr schd | Mem (GB) | Inf time (fps) | box AP | Config | Download | |:------------:|:---------:|:-------:|:-------:|:--------:|:--------------:|:------:|:------:|:--------:| -| Faster R-CNN | R-50 | pytorch | 1x | 2.6 | - | 79.5 |[config](https://github.com/open-mmlab/mmdetection/tree/master/configs/pascal_voc/faster_rcnn_r50_fpn_1x_voc0712.py) | [model](http://download.openmmlab.com/mmdetection/v2.0/pascal_voc/faster_rcnn_r50_fpn_1x_voc0712/faster_rcnn_r50_fpn_1x_voc0712_20200624-c9895d40.pth) | [log](http://download.openmmlab.com/mmdetection/v2.0/pascal_voc/faster_rcnn_r50_fpn_1x_voc0712/20200623_015208.log.json) | -| Retinanet | R-50 | pytorch | 1x | 2.1 | - | 77.3 |[config](https://github.com/open-mmlab/mmdetection/tree/master/configs/pascal_voc/retinanet_r50_fpn_1x_voc0712.py) | [model](http://download.openmmlab.com/mmdetection/v2.0/pascal_voc/retinanet_r50_fpn_1x_voc0712/retinanet_r50_fpn_1x_voc0712_20200617-47cbdd0e.pth) | [log](http://download.openmmlab.com/mmdetection/v2.0/pascal_voc/retinanet_r50_fpn_1x_voc0712/retinanet_r50_fpn_1x_voc0712_20200616_014642.log.json) | +| Faster R-CNN | R-50 | pytorch | 1x | 2.6 | - | 79.5 |[config](https://github.com/open-mmlab/mmdetection/tree/master/configs/pascal_voc/faster_rcnn_r50_fpn_1x_voc0712.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/pascal_voc/faster_rcnn_r50_fpn_1x_voc0712/faster_rcnn_r50_fpn_1x_voc0712_20200624-c9895d40.pth) | [log](https://download.openmmlab.com/mmdetection/v2.0/pascal_voc/faster_rcnn_r50_fpn_1x_voc0712/20200623_015208.log.json) | +| Retinanet | R-50 | pytorch | 1x | 2.1 | - | 77.3 |[config](https://github.com/open-mmlab/mmdetection/tree/master/configs/pascal_voc/retinanet_r50_fpn_1x_voc0712.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/pascal_voc/retinanet_r50_fpn_1x_voc0712/retinanet_r50_fpn_1x_voc0712_20200617-47cbdd0e.pth) | [log](https://download.openmmlab.com/mmdetection/v2.0/pascal_voc/retinanet_r50_fpn_1x_voc0712/retinanet_r50_fpn_1x_voc0712_20200616_014642.log.json) | diff --git a/configs/pascal_voc/faster_rcnn_r18_fpn_1x_voc0712.py b/configs/pascal_voc/faster_rcnn_r18_fpn_1x_voc0712.py deleted file mode 100644 index 02973e8..0000000 --- a/configs/pascal_voc/faster_rcnn_r18_fpn_1x_voc0712.py +++ /dev/null @@ -1,14 +0,0 @@ -_base_ = [ - "../_base_/models/faster_rcnn_r50_fpn.py", - "../_base_/datasets/voc0712.py", - "../_base_/default_runtime.py", -] -model = dict(roi_head=dict(bbox_head=dict(num_classes=20))) -# optimizer -optimizer = dict(type="SGD", lr=0.01, momentum=0.9, weight_decay=0.0001) -optimizer_config = dict(grad_clip=None) -# learning policy -# actual epoch = 3 * 3 = 9 -lr_config = dict(policy="step", step=[3]) -# runtime settings -total_epochs = 4 # actual epoch = 4 * 3 = 12 diff --git a/configs/pascal_voc/ssd512_voc0712.py b/configs/pascal_voc/ssd512_voc0712.py index 365a65f..ea2b69f 100644 --- a/configs/pascal_voc/ssd512_voc0712.py +++ b/configs/pascal_voc/ssd512_voc0712.py @@ -1,7 +1,6 @@ _base_ = 'ssd300_voc0712.py' input_size = 512 model = dict( - backbone=dict(input_size=input_size), bbox_head=dict( in_channels=(512, 1024, 512, 256, 256, 256, 256), anchor_generator=dict( diff --git a/configs/pisa/README.md b/configs/pisa/README.md index 2ab689e..3ae3392 100644 --- a/configs/pisa/README.md +++ b/configs/pisa/README.md @@ -2,7 +2,7 @@ ## Introduction -[ALGORITHM] + ```latex @inproceedings{cao2019prime, @@ -18,21 +18,21 @@ | PISA | Network | Backbone | Lr schd | box AP | mask AP | Config | Download | |:----:|:-------:|:-------------------:|:-------:|:------:|:-------:|:------:|:--------:| | × | Faster R-CNN | R-50-FPN | 1x | 36.4 | | - | -| √ | Faster R-CNN | R-50-FPN | 1x | 38.4 | | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/pisa/pisa_faster_rcnn_r50_fpn_1x_coco.py) | [model](http://download.openmmlab.com/mmdetection/v2.0/pisa/pisa_faster_rcnn_r50_fpn_1x_coco/pisa_faster_rcnn_r50_fpn_1x_coco-dea93523.pth) | [log](http://download.openmmlab.com/mmdetection/v2.0/pisa/pisa_faster_rcnn_r50_fpn_1x_coco/pisa_faster_rcnn_r50_fpn_1x_coco_20200506_185619.log.json) | +| √ | Faster R-CNN | R-50-FPN | 1x | 38.4 | | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/pisa/pisa_faster_rcnn_r50_fpn_1x_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/pisa/pisa_faster_rcnn_r50_fpn_1x_coco/pisa_faster_rcnn_r50_fpn_1x_coco-dea93523.pth) | [log](https://download.openmmlab.com/mmdetection/v2.0/pisa/pisa_faster_rcnn_r50_fpn_1x_coco/pisa_faster_rcnn_r50_fpn_1x_coco_20200506_185619.log.json) | | × | Faster R-CNN | X101-32x4d-FPN | 1x | 40.1 | | - | -| √ | Faster R-CNN | X101-32x4d-FPN | 1x | 41.9 | | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/pisa/pisa_faster_rcnn_x101_32x4d_fpn_1x_coco.py) | [model](http://download.openmmlab.com/mmdetection/v2.0/pisa/pisa_faster_rcnn_x101_32x4d_fpn_1x_coco/pisa_faster_rcnn_x101_32x4d_fpn_1x_coco-e4accec4.pth) | [log](http://download.openmmlab.com/mmdetection/v2.0/pisa/pisa_faster_rcnn_x101_32x4d_fpn_1x_coco/pisa_faster_rcnn_x101_32x4d_fpn_1x_coco_20200505_181503.log.json) | +| √ | Faster R-CNN | X101-32x4d-FPN | 1x | 41.9 | | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/pisa/pisa_faster_rcnn_x101_32x4d_fpn_1x_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/pisa/pisa_faster_rcnn_x101_32x4d_fpn_1x_coco/pisa_faster_rcnn_x101_32x4d_fpn_1x_coco-e4accec4.pth) | [log](https://download.openmmlab.com/mmdetection/v2.0/pisa/pisa_faster_rcnn_x101_32x4d_fpn_1x_coco/pisa_faster_rcnn_x101_32x4d_fpn_1x_coco_20200505_181503.log.json) | | × | Mask R-CNN | R-50-FPN | 1x | 37.3 | 34.2 | - | -| √ | Mask R-CNN | R-50-FPN | 1x | 39.1 | 35.2 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/pisa/pisa_mask_rcnn_r50_fpn_1x_coco.py) | [model](http://download.openmmlab.com/mmdetection/v2.0/pisa/pisa_mask_rcnn_r50_fpn_1x_coco/pisa_mask_rcnn_r50_fpn_1x_coco-dfcedba6.pth) | [log](http://download.openmmlab.com/mmdetection/v2.0/pisa/pisa_mask_rcnn_r50_fpn_1x_coco/pisa_mask_rcnn_r50_fpn_1x_coco_20200508_150500.log.json) | +| √ | Mask R-CNN | R-50-FPN | 1x | 39.1 | 35.2 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/pisa/pisa_mask_rcnn_r50_fpn_1x_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/pisa/pisa_mask_rcnn_r50_fpn_1x_coco/pisa_mask_rcnn_r50_fpn_1x_coco-dfcedba6.pth) | [log](https://download.openmmlab.com/mmdetection/v2.0/pisa/pisa_mask_rcnn_r50_fpn_1x_coco/pisa_mask_rcnn_r50_fpn_1x_coco_20200508_150500.log.json) | | × | Mask R-CNN | X101-32x4d-FPN | 1x | 41.1 | 37.1 | - | | √ | Mask R-CNN | X101-32x4d-FPN | 1x | | | | | × | RetinaNet | R-50-FPN | 1x | 35.6 | | - | -| √ | RetinaNet | R-50-FPN | 1x | 36.9 | | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/pisa/pisa_retinanet_r50_fpn_1x_coco.py) | [model](http://download.openmmlab.com/mmdetection/v2.0/pisa/pisa_retinanet_r50_fpn_1x_coco/pisa_retinanet_r50_fpn_1x_coco-76409952.pth) | [log](http://download.openmmlab.com/mmdetection/v2.0/pisa/pisa_retinanet_r50_fpn_1x_coco/pisa_retinanet_r50_fpn_1x_coco_20200504_014311.log.json) | +| √ | RetinaNet | R-50-FPN | 1x | 36.9 | | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/pisa/pisa_retinanet_r50_fpn_1x_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/pisa/pisa_retinanet_r50_fpn_1x_coco/pisa_retinanet_r50_fpn_1x_coco-76409952.pth) | [log](https://download.openmmlab.com/mmdetection/v2.0/pisa/pisa_retinanet_r50_fpn_1x_coco/pisa_retinanet_r50_fpn_1x_coco_20200504_014311.log.json) | | × | RetinaNet | X101-32x4d-FPN | 1x | 39.0 | | - | -| √ | RetinaNet | X101-32x4d-FPN | 1x | 40.7 | | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/pisa/pisa_retinanet_x101_32x4d_fpn_1x_coco.py) | [model](http://download.openmmlab.com/mmdetection/v2.0/pisa/pisa_retinanet_x101_32x4d_fpn_1x_coco/pisa_retinanet_x101_32x4d_fpn_1x_coco-a0c13c73.pth) | [log](http://download.openmmlab.com/mmdetection/v2.0/pisa/pisa_retinanet_x101_32x4d_fpn_1x_coco/pisa_retinanet_x101_32x4d_fpn_1x_coco_20200505_001404.log.json) | +| √ | RetinaNet | X101-32x4d-FPN | 1x | 40.7 | | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/pisa/pisa_retinanet_x101_32x4d_fpn_1x_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/pisa/pisa_retinanet_x101_32x4d_fpn_1x_coco/pisa_retinanet_x101_32x4d_fpn_1x_coco-a0c13c73.pth) | [log](https://download.openmmlab.com/mmdetection/v2.0/pisa/pisa_retinanet_x101_32x4d_fpn_1x_coco/pisa_retinanet_x101_32x4d_fpn_1x_coco_20200505_001404.log.json) | | × | SSD300 | VGG16 | 1x | 25.6 | | - | -| √ | SSD300 | VGG16 | 1x | 27.6 | | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/pisa/pisa_ssd300_coco.py) | [model](http://download.openmmlab.com/mmdetection/v2.0/pisa/pisa_ssd300_coco/pisa_ssd300_coco-710e3ac9.pth) | [log](http://download.openmmlab.com/mmdetection/v2.0/pisa/pisa_ssd300_coco/pisa_ssd300_coco_20200504_144325.log.json) | +| √ | SSD300 | VGG16 | 1x | 27.6 | | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/pisa/pisa_ssd300_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/pisa/pisa_ssd300_coco/pisa_ssd300_coco-710e3ac9.pth) | [log](https://download.openmmlab.com/mmdetection/v2.0/pisa/pisa_ssd300_coco/pisa_ssd300_coco_20200504_144325.log.json) | | × | SSD300 | VGG16 | 1x | 29.3 | | - | -| √ | SSD300 | VGG16 | 1x | 31.8 | | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/pisa/pisa_ssd512_coco.py) | [model](http://download.openmmlab.com/mmdetection/v2.0/pisa/pisa_ssd512_coco/pisa_ssd512_coco-247addee.pth) | [log](http://download.openmmlab.com/mmdetection/v2.0/pisa/pisa_ssd512_coco/pisa_ssd512_coco_20200508_131030.log.json) | +| √ | SSD300 | VGG16 | 1x | 31.8 | | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/pisa/pisa_ssd512_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/pisa/pisa_ssd512_coco/pisa_ssd512_coco-247addee.pth) | [log](https://download.openmmlab.com/mmdetection/v2.0/pisa/pisa_ssd512_coco/pisa_ssd512_coco_20200508_131030.log.json) | **Notes:** diff --git a/configs/pisa/metafile.yml b/configs/pisa/metafile.yml new file mode 100644 index 0000000..cd43afb --- /dev/null +++ b/configs/pisa/metafile.yml @@ -0,0 +1,110 @@ +Collections: + - Name: PISA + Metadata: + Training Data: COCO + Training Techniques: + - SGD with Momentum + - Weight Decay + Training Resources: 8x V100 GPUs + Architecture: + - FPN + - PISA + - RPN + - ResNet + - RoIPool + Paper: + URL: https://arxiv.org/abs/1904.04821 + Title: 'Prime Sample Attention in Object Detection' + README: configs/pisa/README.md + Code: + URL: https://github.com/open-mmlab/mmdetection/blob/v2.1.0/mmdet/models/roi_heads/pisa_roi_head.py#L8 + Version: v2.1.0 + +Models: + - Name: pisa_faster_rcnn_r50_fpn_1x_coco + In Collection: PISA + Config: configs/pisa/pisa_faster_rcnn_r50_fpn_1x_coco.py + Metadata: + Epochs: 12 + Results: + - Task: Object Detection + Dataset: COCO + Metrics: + box AP: 38.4 + Weights: https://download.openmmlab.com/mmdetection/v2.0/pisa/pisa_faster_rcnn_r50_fpn_1x_coco/pisa_faster_rcnn_r50_fpn_1x_coco-dea93523.pth + + - Name: pisa_faster_rcnn_x101_32x4d_fpn_1x_coco + In Collection: PISA + Config: configs/pisa/pisa_faster_rcnn_x101_32x4d_fpn_1x_coco.py + Metadata: + Epochs: 12 + Results: + - Task: Object Detection + Dataset: COCO + Metrics: + box AP: 41.9 + Weights: https://download.openmmlab.com/mmdetection/v2.0/pisa/pisa_faster_rcnn_x101_32x4d_fpn_1x_coco/pisa_faster_rcnn_x101_32x4d_fpn_1x_coco-e4accec4.pth + + - Name: pisa_mask_rcnn_r50_fpn_1x_coco + In Collection: PISA + Config: configs/pisa/pisa_mask_rcnn_r50_fpn_1x_coco.py + Metadata: + Epochs: 12 + Results: + - Task: Object Detection + Dataset: COCO + Metrics: + box AP: 39.1 + - Task: Instance Segmentation + Dataset: COCO + Metrics: + mask AP: 35.2 + Weights: https://download.openmmlab.com/mmdetection/v2.0/pisa/pisa_mask_rcnn_r50_fpn_1x_coco/pisa_mask_rcnn_r50_fpn_1x_coco-dfcedba6.pth + + - Name: pisa_retinanet_r50_fpn_1x_coco + In Collection: PISA + Config: configs/pisa/pisa_retinanet_r50_fpn_1x_coco.py + Metadata: + Epochs: 12 + Results: + - Task: Object Detection + Dataset: COCO + Metrics: + box AP: 36.9 + Weights: https://download.openmmlab.com/mmdetection/v2.0/pisa/pisa_retinanet_r50_fpn_1x_coco/pisa_retinanet_r50_fpn_1x_coco-76409952.pth + + - Name: pisa_retinanet_x101_32x4d_fpn_1x_coco + In Collection: PISA + Config: configs/pisa/pisa_retinanet_x101_32x4d_fpn_1x_coco.py + Metadata: + Epochs: 12 + Results: + - Task: Object Detection + Dataset: COCO + Metrics: + box AP: 40.7 + Weights: https://download.openmmlab.com/mmdetection/v2.0/pisa/pisa_retinanet_x101_32x4d_fpn_1x_coco/pisa_retinanet_x101_32x4d_fpn_1x_coco-a0c13c73.pth + + - Name: pisa_ssd300_coco + In Collection: PISA + Config: configs/pisa/pisa_ssd300_coco.py + Metadata: + Epochs: 24 + Results: + - Task: Object Detection + Dataset: COCO + Metrics: + box AP: 27.6 + Weights: https://download.openmmlab.com/mmdetection/v2.0/pisa/pisa_ssd300_coco/pisa_ssd300_coco-710e3ac9.pth + + - Name: pisa_ssd512_coco + In Collection: PISA + Config: configs/pisa/pisa_ssd512_coco.py + Metadata: + Epochs: 24 + Results: + - Task: Object Detection + Dataset: COCO + Metrics: + box AP: 31.8 + Weights: https://download.openmmlab.com/mmdetection/v2.0/pisa/pisa_ssd512_coco/pisa_ssd512_coco-247addee.pth diff --git a/configs/point_rend/README.md b/configs/point_rend/README.md index af5ded1..fafb29c 100644 --- a/configs/point_rend/README.md +++ b/configs/point_rend/README.md @@ -2,7 +2,7 @@ ## Introduction -[ALGORITHM] + ```latex @InProceedings{kirillov2019pointrend, @@ -17,7 +17,7 @@ | Backbone | Style | Lr schd | Mem (GB) | Inf time (fps) | box AP | mask AP | Config | Download | | :-------------: | :-----: | :-----: | :------: | :------------: | :----: | :-----: | :------: | :--------: | -| R-50-FPN | caffe | 1x | 4.6 | | 38.4 | 36.3 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/point_rend/point_rend_r50_caffe_fpn_mstrain_1x_coco.py) | [model](http://download.openmmlab.com/mmdetection/v2.0/point_rend/point_rend_r50_caffe_fpn_mstrain_1x_coco/point_rend_r50_caffe_fpn_mstrain_1x_coco-1bcb5fb4.pth) | [log](http://download.openmmlab.com/mmdetection/v2.0/point_rend/point_rend_r50_caffe_fpn_mstrain_1x_coco/point_rend_r50_caffe_fpn_mstrain_1x_coco_20200612_161407.log.json) | -| R-50-FPN | caffe | 3x | 4.6 | | 41.0 | 38.0 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/point_rend/point_rend_r50_caffe_fpn_mstrain_3x_coco.py) | [model](http://download.openmmlab.com/mmdetection/v2.0/point_rend/point_rend_r50_caffe_fpn_mstrain_3x_coco/point_rend_r50_caffe_fpn_mstrain_3x_coco-e0ebb6b7.pth) | [log](http://download.openmmlab.com/mmdetection/v2.0/point_rend/point_rend_r50_caffe_fpn_mstrain_3x_coco/point_rend_r50_caffe_fpn_mstrain_3x_coco_20200614_002632.log.json) | +| R-50-FPN | caffe | 1x | 4.6 | | 38.4 | 36.3 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/point_rend/point_rend_r50_caffe_fpn_mstrain_1x_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/point_rend/point_rend_r50_caffe_fpn_mstrain_1x_coco/point_rend_r50_caffe_fpn_mstrain_1x_coco-1bcb5fb4.pth) | [log](https://download.openmmlab.com/mmdetection/v2.0/point_rend/point_rend_r50_caffe_fpn_mstrain_1x_coco/point_rend_r50_caffe_fpn_mstrain_1x_coco_20200612_161407.log.json) | +| R-50-FPN | caffe | 3x | 4.6 | | 41.0 | 38.0 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/point_rend/point_rend_r50_caffe_fpn_mstrain_3x_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/point_rend/point_rend_r50_caffe_fpn_mstrain_3x_coco/point_rend_r50_caffe_fpn_mstrain_3x_coco-e0ebb6b7.pth) | [log](https://download.openmmlab.com/mmdetection/v2.0/point_rend/point_rend_r50_caffe_fpn_mstrain_3x_coco/point_rend_r50_caffe_fpn_mstrain_3x_coco_20200614_002632.log.json) | Note: All models are trained with multi-scale, the input image shorter side is randomly scaled to one of (640, 672, 704, 736, 768, 800). diff --git a/configs/point_rend/metafile.yml b/configs/point_rend/metafile.yml new file mode 100644 index 0000000..82aea05 --- /dev/null +++ b/configs/point_rend/metafile.yml @@ -0,0 +1,54 @@ +Collections: + - Name: PointRend + Metadata: + Training Data: COCO + Training Techniques: + - SGD with Momentum + - Weight Decay + Training Resources: 8x V100 GPUs + Architecture: + - PointRend + - FPN + - ResNet + Paper: + URL: https://arxiv.org/abs/1912.08193 + Title: 'PointRend: Image Segmentation as Rendering' + README: configs/point_rend/README.md + Code: + URL: https://github.com/open-mmlab/mmdetection/blob/v2.2.0/mmdet/models/detectors/point_rend.py#L6 + Version: v2.2.0 + +Models: + - Name: point_rend_r50_caffe_fpn_mstrain_1x_coco + In Collection: PointRend + Config: configs/point_rend/point_rend_r50_caffe_fpn_mstrain_1x_coco.py + Metadata: + Training Memory (GB): 4.6 + Epochs: 12 + Results: + - Task: Object Detection + Dataset: COCO + Metrics: + box AP: 38.4 + - Task: Instance Segmentation + Dataset: COCO + Metrics: + mask AP: 36.3 + Weights: https://download.openmmlab.com/mmdetection/v2.0/point_rend/point_rend_r50_caffe_fpn_mstrain_1x_coco/point_rend_r50_caffe_fpn_mstrain_1x_coco-1bcb5fb4.pth + + - Name: point_rend_r50_caffe_fpn_mstrain_3x_coco + In Collection: PointRend + Config: configs/point_rend/point_rend_r50_caffe_fpn_mstrain_3x_coco.py + Metadata: + Training Memory (GB): 4.6 + Epochs: 36 + Results: + - Task: Object Detection + Dataset: COCO + Metrics: + box AP: 41.0 + - Task: Instance Segmentation + Dataset: COCO + Metrics: + mask AP: 38.0 + Weights: https://download.openmmlab.com/mmdetection/v2.0/point_rend/point_rend_r50_caffe_fpn_mstrain_3x_coco/point_rend_r50_caffe_fpn_mstrain_3x_coco-e0ebb6b7.pth diff --git a/configs/regnet/README.md b/configs/regnet/README.md index 0ccd407..79ee258 100644 --- a/configs/regnet/README.md +++ b/configs/regnet/README.md @@ -54,32 +54,32 @@ For other pre-trained models or self-implemented regnet models, the users are re | Backbone | Style | Lr schd | Mem (GB) | Inf time (fps) | box AP | mask AP | Config | Download | | :---------: | :-----: | :-----: | :------: | :------------: | :----: | :-----: | :------: | :--------: | -| [R-50-FPN](../mask_rcnn/mask_rcnn_r50_fpn_1x_coco.py)| pytorch | 1x | 4.4 | 12.0 | 38.2 | 34.7 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/mask_rcnn/mask_rcnn_r50_fpn_1x_coco.py) | [model](http://download.openmmlab.com/mmdetection/v2.0/mask_rcnn/mask_rcnn_r50_fpn_1x_coco/mask_rcnn_r50_fpn_1x_coco_20200205-d4b0c5d6.pth) | [log](http://download.openmmlab.com/mmdetection/v2.0/mask_rcnn/mask_rcnn_r50_fpn_1x_coco/mask_rcnn_r50_fpn_1x_coco_20200205_050542.log.json) | -|[RegNetX-3.2GF-FPN](./mask_rcnn_regnetx-3.2GF_fpn_1x_coco.py)| pytorch | 1x |5.0 ||40.3|36.6|[config](https://github.com/open-mmlab/mmdetection/tree/master/configs/regnet/mask_rcnn_regnetx-3.2GF_fpn_1x_coco.py) | [model](http://download.openmmlab.com/mmdetection/v2.0/regnet/mask_rcnn_regnetx-3.2GF_fpn_1x_coco/mask_rcnn_regnetx-3.2GF_fpn_1x_coco_20200520_163141-2a9d1814.pth) | [log](http://download.openmmlab.com/mmdetection/v2.0/regnet/mask_rcnn_regnetx-3.2GF_fpn_1x_coco/mask_rcnn_regnetx-3.2GF_fpn_1x_coco_20200520_163141.log.json) | -|[RegNetX-4.0GF-FPN](./mask_rcnn_regnetx-4GF_fpn_1x_coco.py)| pytorch | 1x |5.5||41.5|37.4|[config](https://github.com/open-mmlab/mmdetection/tree/master/configs/regnet/mask_rcnn_regnetx-4GF_fpn_1x_coco.py) | [model](http://download.openmmlab.com/mmdetection/v2.0/regnet/mask_rcnn_regnetx-4GF_fpn_1x_coco/mask_rcnn_regnetx-4GF_fpn_1x_coco_20200517_180217-32e9c92d.pth) | [log](http://download.openmmlab.com/mmdetection/v2.0/regnet/mask_rcnn_regnetx-4GF_fpn_1x_coco/mask_rcnn_regnetx-4GF_fpn_1x_coco_20200517_180217.log.json) | -| [R-101-FPN](../mask_rcnn/mask_rcnn_r101_fpn_1x_coco.py)| pytorch | 1x | 6.4 | 10.3 | 40.0 | 36.1 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/mask_rcnn/mask_rcnn_r101_fpn_1x_coco.py) | [model](http://download.openmmlab.com/mmdetection/v2.0/mask_rcnn/mask_rcnn_r101_fpn_1x_coco/mask_rcnn_r101_fpn_1x_coco_20200204-1efe0ed5.pth) | [log](http://download.openmmlab.com/mmdetection/v2.0/mask_rcnn/mask_rcnn_r101_fpn_1x_coco/mask_rcnn_r101_fpn_1x_coco_20200204_144809.log.json) | -|[RegNetX-6.4GF-FPN](./mask_rcnn_regnetx-6.4GF_fpn_1x_coco.py)| pytorch | 1x |6.1 ||41.0|37.1|[config](https://github.com/open-mmlab/mmdetection/tree/master/configs/regnet/mask_rcnn_regnetx-6.4GF_fpn_1x_coco.py) | [model](http://download.openmmlab.com/mmdetection/v2.0/regnet/mask_rcnn_regnetx-6.4GF_fpn_1x_coco/mask_rcnn_regnetx-6.4GF_fpn_1x_coco_20200517_180439-3a7aae83.pth) | [log](http://download.openmmlab.com/mmdetection/v2.0/regnet/mask_rcnn_regnetx-6.4GF_fpn_1x_coco/mask_rcnn_regnetx-6.4GF_fpn_1x_coco_20200517_180439.log.json) | -| [X-101-32x4d-FPN](../mask_rcnn/mask_rcnn_x101_32x4d_fpn_1x_coco.py) | pytorch | 1x | 7.6 | 9.4 | 41.9 | 37.5 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/mask_rcnn/mask_rcnn_x101_32x4d_fpn_1x_coco.py) | [model](http://download.openmmlab.com/mmdetection/v2.0/mask_rcnn/mask_rcnn_x101_32x4d_fpn_1x_coco/mask_rcnn_x101_32x4d_fpn_1x_coco_20200205-478d0b67.pth) | [log](http://download.openmmlab.com/mmdetection/v2.0/mask_rcnn/mask_rcnn_x101_32x4d_fpn_1x_coco/mask_rcnn_x101_32x4d_fpn_1x_coco_20200205_034906.log.json) | -|[RegNetX-8.0GF-FPN](./mask_rcnn_regnetx-8GF_fpn_1x_coco.py)| pytorch | 1x |6.4 ||41.7|37.5|[config](https://github.com/open-mmlab/mmdetection/tree/master/configs/regnet/mask_rcnn_regnetx-8GF_fpn_1x_coco.py) | [model](http://download.openmmlab.com/mmdetection/v2.0/regnet/mask_rcnn_regnetx-8GF_fpn_1x_coco/mask_rcnn_regnetx-8GF_fpn_1x_coco_20200517_180515-09daa87e.pth) | [log](http://download.openmmlab.com/mmdetection/v2.0/regnet/mask_rcnn_regnetx-8GF_fpn_1x_coco/mask_rcnn_regnetx-8GF_fpn_1x_coco_20200517_180515.log.json) | -|[RegNetX-12GF-FPN](./mask_rcnn_regnetx-12GF_fpn_1x_coco.py)| pytorch | 1x |7.4 ||42.2|38|[config](https://github.com/open-mmlab/mmdetection/tree/master/configs/regnet/mask_rcnn_regnetx-12GF_fpn_1x_coco.py) | [model](http://download.openmmlab.com/mmdetection/v2.0/regnet/mask_rcnn_regnetx-12GF_fpn_1x_coco/mask_rcnn_regnetx-12GF_fpn_1x_coco_20200517_180552-b538bd8b.pth) | [log](http://download.openmmlab.com/mmdetection/v2.0/regnet/mask_rcnn_regnetx-12GF_fpn_1x_coco/mask_rcnn_regnetx-12GF_fpn_1x_coco_20200517_180552.log.json) | -|[RegNetX-3.2GF-FPN-DCN-C3-C5](./mask_rcnn_regnetx-3.2GF_fpn_mdconv_c3-c5_1x_coco.py)| pytorch | 1x |5.0 ||40.3|36.6|[config](https://github.com/open-mmlab/mmdetection/tree/master/configs/regnet/mask_rcnn_regnetx-3.2GF_fpn_mdconv_c3-c5_1x_coco.py) | [model](http://download.openmmlab.com/mmdetection/v2.0/regnet/mask_rcnn_regnetx-3.2GF_fpn_mdconv_c3-c5_1x_coco/mask_rcnn_regnetx-3.2GF_fpn_mdconv_c3-c5_1x_coco_20200520_172726-75f40794.pth) | [log](http://download.openmmlab.com/mmdetection/v2.0/regnet/mask_rcnn_regnetx-3.2GF_fpn_mdconv_c3-c5_1x_coco/mask_rcnn_regnetx-3.2GF_fpn_mdconv_c3-c5_1x_coco_20200520_172726.log.json) | +| [R-50-FPN](../mask_rcnn/mask_rcnn_r50_fpn_1x_coco.py)| pytorch | 1x | 4.4 | 12.0 | 38.2 | 34.7 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/mask_rcnn/mask_rcnn_r50_fpn_1x_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/mask_rcnn/mask_rcnn_r50_fpn_1x_coco/mask_rcnn_r50_fpn_1x_coco_20200205-d4b0c5d6.pth) | [log](https://download.openmmlab.com/mmdetection/v2.0/mask_rcnn/mask_rcnn_r50_fpn_1x_coco/mask_rcnn_r50_fpn_1x_coco_20200205_050542.log.json) | +|[RegNetX-3.2GF-FPN](./mask_rcnn_regnetx-3.2GF_fpn_1x_coco.py)| pytorch | 1x |5.0 ||40.3|36.6|[config](https://github.com/open-mmlab/mmdetection/tree/master/configs/regnet/mask_rcnn_regnetx-3.2GF_fpn_1x_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/regnet/mask_rcnn_regnetx-3.2GF_fpn_1x_coco/mask_rcnn_regnetx-3.2GF_fpn_1x_coco_20200520_163141-2a9d1814.pth) | [log](https://download.openmmlab.com/mmdetection/v2.0/regnet/mask_rcnn_regnetx-3.2GF_fpn_1x_coco/mask_rcnn_regnetx-3.2GF_fpn_1x_coco_20200520_163141.log.json) | +|[RegNetX-4.0GF-FPN](./mask_rcnn_regnetx-4GF_fpn_1x_coco.py)| pytorch | 1x |5.5||41.5|37.4|[config](https://github.com/open-mmlab/mmdetection/tree/master/configs/regnet/mask_rcnn_regnetx-4GF_fpn_1x_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/regnet/mask_rcnn_regnetx-4GF_fpn_1x_coco/mask_rcnn_regnetx-4GF_fpn_1x_coco_20200517_180217-32e9c92d.pth) | [log](https://download.openmmlab.com/mmdetection/v2.0/regnet/mask_rcnn_regnetx-4GF_fpn_1x_coco/mask_rcnn_regnetx-4GF_fpn_1x_coco_20200517_180217.log.json) | +| [R-101-FPN](../mask_rcnn/mask_rcnn_r101_fpn_1x_coco.py)| pytorch | 1x | 6.4 | 10.3 | 40.0 | 36.1 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/mask_rcnn/mask_rcnn_r101_fpn_1x_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/mask_rcnn/mask_rcnn_r101_fpn_1x_coco/mask_rcnn_r101_fpn_1x_coco_20200204-1efe0ed5.pth) | [log](https://download.openmmlab.com/mmdetection/v2.0/mask_rcnn/mask_rcnn_r101_fpn_1x_coco/mask_rcnn_r101_fpn_1x_coco_20200204_144809.log.json) | +|[RegNetX-6.4GF-FPN](./mask_rcnn_regnetx-6.4GF_fpn_1x_coco.py)| pytorch | 1x |6.1 ||41.0|37.1|[config](https://github.com/open-mmlab/mmdetection/tree/master/configs/regnet/mask_rcnn_regnetx-6.4GF_fpn_1x_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/regnet/mask_rcnn_regnetx-6.4GF_fpn_1x_coco/mask_rcnn_regnetx-6.4GF_fpn_1x_coco_20200517_180439-3a7aae83.pth) | [log](https://download.openmmlab.com/mmdetection/v2.0/regnet/mask_rcnn_regnetx-6.4GF_fpn_1x_coco/mask_rcnn_regnetx-6.4GF_fpn_1x_coco_20200517_180439.log.json) | +| [X-101-32x4d-FPN](../mask_rcnn/mask_rcnn_x101_32x4d_fpn_1x_coco.py) | pytorch | 1x | 7.6 | 9.4 | 41.9 | 37.5 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/mask_rcnn/mask_rcnn_x101_32x4d_fpn_1x_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/mask_rcnn/mask_rcnn_x101_32x4d_fpn_1x_coco/mask_rcnn_x101_32x4d_fpn_1x_coco_20200205-478d0b67.pth) | [log](https://download.openmmlab.com/mmdetection/v2.0/mask_rcnn/mask_rcnn_x101_32x4d_fpn_1x_coco/mask_rcnn_x101_32x4d_fpn_1x_coco_20200205_034906.log.json) | +|[RegNetX-8.0GF-FPN](./mask_rcnn_regnetx-8GF_fpn_1x_coco.py)| pytorch | 1x |6.4 ||41.7|37.5|[config](https://github.com/open-mmlab/mmdetection/tree/master/configs/regnet/mask_rcnn_regnetx-8GF_fpn_1x_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/regnet/mask_rcnn_regnetx-8GF_fpn_1x_coco/mask_rcnn_regnetx-8GF_fpn_1x_coco_20200517_180515-09daa87e.pth) | [log](https://download.openmmlab.com/mmdetection/v2.0/regnet/mask_rcnn_regnetx-8GF_fpn_1x_coco/mask_rcnn_regnetx-8GF_fpn_1x_coco_20200517_180515.log.json) | +|[RegNetX-12GF-FPN](./mask_rcnn_regnetx-12GF_fpn_1x_coco.py)| pytorch | 1x |7.4 ||42.2|38|[config](https://github.com/open-mmlab/mmdetection/tree/master/configs/regnet/mask_rcnn_regnetx-12GF_fpn_1x_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/regnet/mask_rcnn_regnetx-12GF_fpn_1x_coco/mask_rcnn_regnetx-12GF_fpn_1x_coco_20200517_180552-b538bd8b.pth) | [log](https://download.openmmlab.com/mmdetection/v2.0/regnet/mask_rcnn_regnetx-12GF_fpn_1x_coco/mask_rcnn_regnetx-12GF_fpn_1x_coco_20200517_180552.log.json) | +|[RegNetX-3.2GF-FPN-DCN-C3-C5](./mask_rcnn_regnetx-3.2GF_fpn_mdconv_c3-c5_1x_coco.py)| pytorch | 1x |5.0 ||40.3|36.6|[config](https://github.com/open-mmlab/mmdetection/tree/master/configs/regnet/mask_rcnn_regnetx-3.2GF_fpn_mdconv_c3-c5_1x_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/regnet/mask_rcnn_regnetx-3.2GF_fpn_mdconv_c3-c5_1x_coco/mask_rcnn_regnetx-3.2GF_fpn_mdconv_c3-c5_1x_coco_20200520_172726-75f40794.pth) | [log](https://download.openmmlab.com/mmdetection/v2.0/regnet/mask_rcnn_regnetx-3.2GF_fpn_mdconv_c3-c5_1x_coco/mask_rcnn_regnetx-3.2GF_fpn_mdconv_c3-c5_1x_coco_20200520_172726.log.json) | ### Faster R-CNN | Backbone | Style | Lr schd | Mem (GB) | Inf time (fps) | box AP | Config | Download | | :---------: | :-----: | :-----: | :------: | :------------: | :----: | :------: | :--------: | -| [R-50-FPN](../faster_rcnn/faster_rcnn_r50_fpn_1x_coco.py)| pytorch | 1x | 4.0 | 18.2 | 37.4 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/faster_rcnn/faster_rcnn_r50_fpn_1x_coco.py) | [model](http://download.openmmlab.com/mmdetection/v2.0/faster_rcnn/faster_rcnn_r50_fpn_1x_coco/faster_rcnn_r50_fpn_1x_coco_20200130-047c8118.pth) | [log](http://download.openmmlab.com/mmdetection/v2.0/faster_rcnn/faster_rcnn_r50_fpn_1x_coco/faster_rcnn_r50_fpn_1x_coco_20200130_204655.log.json) | -|[RegNetX-3.2GF-FPN](./faster_rcnn_regnetx-3.2GF_fpn_1x_coco.py)| pytorch | 1x | 4.5||39.9|[config](https://github.com/open-mmlab/mmdetection/tree/master/configs/regnet/faster_rcnn_regnetx-3.2GF_fpn_1x_coco.py) | [model](http://download.openmmlab.com/mmdetection/v2.0/regnet/faster_rcnn_regnetx-3.2GF_fpn_1x_coco/faster_rcnn_regnetx-3.2GF_fpn_1x_coco_20200517_175927-126fd9bf.pth) | [log](http://download.openmmlab.com/mmdetection/v2.0/regnet/faster_rcnn_regnetx-3.2GF_fpn_1x_coco/faster_rcnn_regnetx-3.2GF_fpn_1x_coco_20200517_175927.log.json) | -|[RegNetX-3.2GF-FPN](./faster_rcnn_regnetx-3.2GF_fpn_2x_coco.py)| pytorch | 2x | 4.5||41.1|[config](https://github.com/open-mmlab/mmdetection/tree/master/configs/regnet/faster_rcnn_regnetx-3.2GF_fpn_2x_coco.py) | [model](http://download.openmmlab.com/mmdetection/v2.0/regnet/faster_rcnn_regnetx-3.2GF_fpn_2x_coco/faster_rcnn_regnetx-3.2GF_fpn_2x_coco_20200520_223955-e2081918.pth) | [log](http://download.openmmlab.com/mmdetection/v2.0/regnet/faster_rcnn_regnetx-3.2GF_fpn_2x_coco/faster_rcnn_regnetx-3.2GF_fpn_2x_coco_20200520_223955.log.json) | +| [R-50-FPN](../faster_rcnn/faster_rcnn_r50_fpn_1x_coco.py)| pytorch | 1x | 4.0 | 18.2 | 37.4 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/faster_rcnn/faster_rcnn_r50_fpn_1x_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/faster_rcnn/faster_rcnn_r50_fpn_1x_coco/faster_rcnn_r50_fpn_1x_coco_20200130-047c8118.pth) | [log](https://download.openmmlab.com/mmdetection/v2.0/faster_rcnn/faster_rcnn_r50_fpn_1x_coco/faster_rcnn_r50_fpn_1x_coco_20200130_204655.log.json) | +|[RegNetX-3.2GF-FPN](./faster_rcnn_regnetx-3.2GF_fpn_1x_coco.py)| pytorch | 1x | 4.5||39.9|[config](https://github.com/open-mmlab/mmdetection/tree/master/configs/regnet/faster_rcnn_regnetx-3.2GF_fpn_1x_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/regnet/faster_rcnn_regnetx-3.2GF_fpn_1x_coco/faster_rcnn_regnetx-3.2GF_fpn_1x_coco_20200517_175927-126fd9bf.pth) | [log](https://download.openmmlab.com/mmdetection/v2.0/regnet/faster_rcnn_regnetx-3.2GF_fpn_1x_coco/faster_rcnn_regnetx-3.2GF_fpn_1x_coco_20200517_175927.log.json) | +|[RegNetX-3.2GF-FPN](./faster_rcnn_regnetx-3.2GF_fpn_2x_coco.py)| pytorch | 2x | 4.5||41.1|[config](https://github.com/open-mmlab/mmdetection/tree/master/configs/regnet/faster_rcnn_regnetx-3.2GF_fpn_2x_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/regnet/faster_rcnn_regnetx-3.2GF_fpn_2x_coco/faster_rcnn_regnetx-3.2GF_fpn_2x_coco_20200520_223955-e2081918.pth) | [log](https://download.openmmlab.com/mmdetection/v2.0/regnet/faster_rcnn_regnetx-3.2GF_fpn_2x_coco/faster_rcnn_regnetx-3.2GF_fpn_2x_coco_20200520_223955.log.json) | ### RetinaNet | Backbone | Style | Lr schd | Mem (GB) | Inf time (fps) | box AP | Config | Download | | :---------: | :-----: | :-----: | :------: | :------------: | :----: | :------: | :--------: | -| [R-50-FPN](../retinanet/retinanet_r50_fpn_1x_coco.py) | pytorch | 1x | 3.8 | 16.6 | 36.5 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/retinanet/retinanet_r50_fpn_1x_coco.py) | [model](http://download.openmmlab.com/mmdetection/v2.0/retinanet/retinanet_r50_fpn_1x_coco/retinanet_r50_fpn_1x_coco_20200130-c2398f9e.pth) | [log](http://download.openmmlab.com/mmdetection/v2.0/retinanet/retinanet_r50_fpn_1x_coco/retinanet_r50_fpn_1x_coco_20200130_002941.log.json) | -|[RegNetX-800MF-FPN](./retinanet_regnetx-800MF_fpn_1x_coco.py)| pytorch | 1x |2.5||35.6|[config](https://github.com/open-mmlab/mmdetection/tree/master/configs/regnet/retinanet_regnetx-800MF_fpn_1x_coco.py) | [model](http://download.openmmlab.com/mmdetection/v2.0/regnet/retinanet_regnetx-800MF_fpn_1x_coco/retinanet_regnetx-800MF_fpn_1x_coco_20200517_191403-f6f91d10.pth) | [log](http://download.openmmlab.com/mmdetection/v2.0/regnet/retinanet_regnetx-800MF_fpn_1x_coco/retinanet_regnetx-800MF_fpn_1x_coco_20200517_191403.log.json) | -|[RegNetX-1.6GF-FPN](./retinanet_regnetx-1.6GF_fpn_1x_coco.py)| pytorch | 1x |3.3||37.3|[config](https://github.com/open-mmlab/mmdetection/tree/master/configs/regnet/retinanet_regnetx-1.6GF_fpn_1x_coco.py) | [model](http://download.openmmlab.com/mmdetection/v2.0/regnet/retinanet_regnetx-1.6GF_fpn_1x_coco/retinanet_regnetx-1.6GF_fpn_1x_coco_20200517_191403-37009a9d.pth) | [log](http://download.openmmlab.com/mmdetection/v2.0/regnet/retinanet_regnetx-1.6GF_fpn_1x_coco/retinanet_regnetx-1.6GF_fpn_1x_coco_20200517_191403.log.json) | -|[RegNetX-3.2GF-FPN](./retinanet_regnetx-3.2GF_fpn_1x_coco.py)| pytorch | 1x |4.2 ||39.1|[config](https://github.com/open-mmlab/mmdetection/tree/master/configs/regnet/retinanet_regnetx-3.2GF_fpn_1x_coco.py) | [model](http://download.openmmlab.com/mmdetection/v2.0/regnet/retinanet_regnetx-3.2GF_fpn_1x_coco/retinanet_regnetx-3.2GF_fpn_1x_coco_20200520_163141-cb1509e8.pth) | [log](http://download.openmmlab.com/mmdetection/v2.0/regnet/retinanet_regnetx-3.2GF_fpn_1x_coco/retinanet_regnetx-3.2GF_fpn_1x_coco_20200520_163141.log.json) | +| [R-50-FPN](../retinanet/retinanet_r50_fpn_1x_coco.py) | pytorch | 1x | 3.8 | 16.6 | 36.5 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/retinanet/retinanet_r50_fpn_1x_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/retinanet/retinanet_r50_fpn_1x_coco/retinanet_r50_fpn_1x_coco_20200130-c2398f9e.pth) | [log](https://download.openmmlab.com/mmdetection/v2.0/retinanet/retinanet_r50_fpn_1x_coco/retinanet_r50_fpn_1x_coco_20200130_002941.log.json) | +|[RegNetX-800MF-FPN](./retinanet_regnetx-800MF_fpn_1x_coco.py)| pytorch | 1x |2.5||35.6|[config](https://github.com/open-mmlab/mmdetection/tree/master/configs/regnet/retinanet_regnetx-800MF_fpn_1x_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/regnet/retinanet_regnetx-800MF_fpn_1x_coco/retinanet_regnetx-800MF_fpn_1x_coco_20200517_191403-f6f91d10.pth) | [log](https://download.openmmlab.com/mmdetection/v2.0/regnet/retinanet_regnetx-800MF_fpn_1x_coco/retinanet_regnetx-800MF_fpn_1x_coco_20200517_191403.log.json) | +|[RegNetX-1.6GF-FPN](./retinanet_regnetx-1.6GF_fpn_1x_coco.py)| pytorch | 1x |3.3||37.3|[config](https://github.com/open-mmlab/mmdetection/tree/master/configs/regnet/retinanet_regnetx-1.6GF_fpn_1x_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/regnet/retinanet_regnetx-1.6GF_fpn_1x_coco/retinanet_regnetx-1.6GF_fpn_1x_coco_20200517_191403-37009a9d.pth) | [log](https://download.openmmlab.com/mmdetection/v2.0/regnet/retinanet_regnetx-1.6GF_fpn_1x_coco/retinanet_regnetx-1.6GF_fpn_1x_coco_20200517_191403.log.json) | +|[RegNetX-3.2GF-FPN](./retinanet_regnetx-3.2GF_fpn_1x_coco.py)| pytorch | 1x |4.2 ||39.1|[config](https://github.com/open-mmlab/mmdetection/tree/master/configs/regnet/retinanet_regnetx-3.2GF_fpn_1x_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/regnet/retinanet_regnetx-3.2GF_fpn_1x_coco/retinanet_regnetx-3.2GF_fpn_1x_coco_20200520_163141-cb1509e8.pth) | [log](https://download.openmmlab.com/mmdetection/v2.0/regnet/retinanet_regnetx-3.2GF_fpn_1x_coco/retinanet_regnetx-3.2GF_fpn_1x_coco_20200520_163141.log.json) | ### Pre-trained models @@ -87,8 +87,22 @@ We also train some models with longer schedules and multi-scale training. The us | Method | Backbone | Style | Lr schd | Mem (GB) | Inf time (fps) | box AP | mask AP | Config | Download | | :-----: | :-----: | :-----: | :-----: | :------: | :------------: | :----: | :-----: | :------: | :--------: | -|Faster RCNN |[RegNetX-3.2GF-FPN](./faster_rcnn_regnetx-3.2GF_fpn_mstrain_3x_coco.py)| pytorch | 3x |5.0 ||42.2|-|[config](https://github.com/open-mmlab/mmdetection/tree/master/configs/regnet/faster_rcnn_regnetx-3.2GF_fpn_mstrain_3x_coco.py) | [model](http://download.openmmlab.com/mmdetection/v2.0/regnet/faster_rcnn_regnetx-3.2GF_fpn_mstrain_3x_coco/faster_rcnn_regnetx-3.2GF_fpn_mstrain_3x_coco_20200520_224253-bf85ae3e.pth) | [log](http://download.openmmlab.com/mmdetection/v2.0/regnet/faster_rcnn_regnetx-3.2GF_fpn_mstrain_3x_coco/faster_rcnn_regnetx-3.2GF_fpn_mstrain_3x_coco_20200520_224253.log.json) | -|Mask RCNN |[RegNetX-3.2GF-FPN](./mask_rcnn_regnetx-3.2GF_fpn_mstrain_3x_coco.py)| pytorch | 3x |5.0 ||43.1|38.7|[config](https://github.com/open-mmlab/mmdetection/tree/master/configs/regnet/mask_rcnn_regnetx-3.2GF_fpn_mstrain_3x_coco.py) | [model](http://download.openmmlab.com/mmdetection/v2.0/regnet/mask_rcnn_regnetx-3.2GF_fpn_mstrain_3x_coco/mask_rcnn_regnetx-3.2GF_fpn_mstrain_3x_coco_20200521_202221-99879813.pth) | [log](http://download.openmmlab.com/mmdetection/v2.0/regnet/mask_rcnn_regnetx-3.2GF_fpn_mstrain_3x_coco/mask_rcnn_regnetx-3.2GF_fpn_mstrain_3x_coco_20200521_202221.log.json) | +|Faster RCNN |[RegNetX-400MF-FPN](./faster_rcnn_regnetx-400MF_fpn_mstrain_3x_coco.py)| pytorch | 3x |2.3 ||37.1|-|[config](https://github.com/open-mmlab/mmdetection/tree/master/configs/regnet/faster_rcnn_regnetx-400MF_fpn_mstrain_3x_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/regnet/faster_rcnn_regnetx-400MF_fpn_mstrain_3x_coco/faster_rcnn_regnetx-400MF_fpn_mstrain_3x_coco_20210526_095112-e1967c37.pth) | [log](https://download.openmmlab.com/mmdetection/v2.0/regnet/faster_rcnn_regnetx-400MF_fpn_mstrain_3x_coco/faster_rcnn_regnetx-400MF_fpn_mstrain_3x_coco_20210526_095112.log.json) | +|Faster RCNN |[RegNetX-800MF-FPN](./faster_rcnn_regnetx-800MF_fpn_mstrain_3x_coco.py)| pytorch | 3x |2.8 ||38.8|-|[config](https://github.com/open-mmlab/mmdetection/tree/master/configs/regnet/faster_rcnn_regnetx-800MF_fpn_mstrain_3x_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/regnet/faster_rcnn_regnetx-800MF_fpn_mstrain_3x_coco/faster_rcnn_regnetx-800MF_fpn_mstrain_3x_coco_20210526_095118-a2c70b20.pth) | [log](https://download.openmmlab.com/mmdetection/v2.0/regnet/faster_rcnn_regnetx-800MF_fpn_mstrain_3x_coco/faster_rcnn_regnetx-800MF_fpn_mstrain_3x_coco_20210526_095118.log.json) | +|Faster RCNN |[RegNetX-1.6GF-FPN](./faster_rcnn_regnetx-1.6GF_fpn_mstrain_3x_coco.py)| pytorch | 3x |3.4 ||40.5|-|[config](https://github.com/open-mmlab/mmdetection/tree/master/configs/regnet/faster_rcnn_regnetx-1.6GF_fpn_mstrain_3x_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/regnet/faster_rcnn_regnetx-1.6GF_fpn_mstrain_3x_coco/faster_rcnn_regnetx-1_20210526_095325-94aa46cc.pth) | [log](https://download.openmmlab.com/mmdetection/v2.0/regnet/faster_rcnn_regnetx-1.6GF_fpn_mstrain_3x_coco/faster_rcnn_regnetx-1_20210526_095325.log.json) | +|Faster RCNN |[RegNetX-3.2GF-FPN](./faster_rcnn_regnetx-3.2GF_fpn_mstrain_3x_coco.py)| pytorch | 3x |4.4 ||42.3|-|[config](https://github.com/open-mmlab/mmdetection/tree/master/configs/regnet/faster_rcnn_regnetx-3.2GF_fpn_mstrain_3x_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/regnet/faster_rcnn_regnetx-3.2GF_fpn_mstrain_3x_coco/faster_rcnn_regnetx-3_20210526_095152-e16a5227.pth) | [log](https://download.openmmlab.com/mmdetection/v2.0/regnet/faster_rcnn_regnetx-3.2GF_fpn_mstrain_3x_coco/faster_rcnn_regnetx-3_20210526_095152.log.json) | +|Faster RCNN |[RegNetX-4GF-FPN](./faster_rcnn_regnetx-4GF_fpn_mstrain_3x_coco.py)| pytorch | 3x |4.9 ||42.8|-|[config](https://github.com/open-mmlab/mmdetection/tree/master/configs/regnet/faster_rcnn_regnetx-4GF_fpn_mstrain_3x_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/regnet/faster_rcnn_regnetx-4GF_fpn_mstrain_3x_coco/faster_rcnn_regnetx-4GF_fpn_mstrain_3x_coco_20210526_095201-65eaf841.pth) | [log](https://download.openmmlab.com/mmdetection/v2.0/regnet/faster_rcnn_regnetx-4GF_fpn_mstrain_3x_coco/faster_rcnn_regnetx-4GF_fpn_mstrain_3x_coco_20210526_095201.log.json) | +|Mask RCNN |[RegNetX-3.2GF-FPN](./mask_rcnn_regnetx-3.2GF_fpn_mstrain_3x_coco.py)| pytorch | 3x |5.0 ||43.1|38.7|[config](https://github.com/open-mmlab/mmdetection/tree/master/configs/regnet/mask_rcnn_regnetx-3.2GF_fpn_mstrain_3x_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/regnet/mask_rcnn_regnetx-3.2GF_fpn_mstrain_3x_coco/mask_rcnn_regnetx-3.2GF_fpn_mstrain_3x_coco_20200521_202221-99879813.pth) | [log](https://download.openmmlab.com/mmdetection/v2.0/regnet/mask_rcnn_regnetx-3.2GF_fpn_mstrain_3x_coco/mask_rcnn_regnetx-3.2GF_fpn_mstrain_3x_coco_20200521_202221.log.json) | +|Mask RCNN |[RegNetX-400MF-FPN](./mask_rcnn_regnetx-400MF_fpn_mstrain-poly_3x_coco.py)| pytorch | 3x |2.5 ||37.6|34.4|[config](https://github.com/open-mmlab/mmdetection/tree/master/configs/regnet/mask_rcnn_regnetx-400MF_fpn_mstrain_3x_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/regnet/mask_rcnn_regnetx-400MF_fpn_mstrain-poly_3x_coco/mask_rcnn_regnetx-400MF_fpn_mstrain-poly_3x_coco_20210601_235443-8aac57a4.pth) | [log](https://download.openmmlab.com/mmdetection/v2.0/regnet/mask_rcnn_regnetx-400MF_fpn_mstrain-poly_3x_coco/mask_rcnn_regnetx-400MF_fpn_mstrain-poly_3x_coco_20210601_235443.log.json) | +|Mask RCNN |[RegNetX-800MF-FPN](./mask_rcnn_regnetx-800MF_fpn_mstrain-poly_3x_coco.py)| pytorch | 3x |2.9 ||39.5|36.1|[config](https://github.com/open-mmlab/mmdetection/tree/master/configs/regnet/mask_rcnn_regnetx-800MF_fpn_mstrain_3x_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/regnet/mask_rcnn_regnetx-800MF_fpn_mstrain-poly_3x_coco/mask_rcnn_regnetx-800MF_fpn_mstrain-poly_3x_coco_20210602_210641-715d51f5.pth) | [log](https://download.openmmlab.com/mmdetection/v2.0/regnet/mask_rcnn_regnetx-800MF_fpn_mstrain-poly_3x_coco/mask_rcnn_regnetx-800MF_fpn_mstrain-poly_3x_coco_20210602_210641.log.json) | +|Mask RCNN |[RegNetX-1.6GF-FPN](./mask_rcnn_regnetx-1.6GF_fpn_mstrain-poly_3x_coco.py)| pytorch | 3x |3.6 ||40.9|37.5|[config](https://github.com/open-mmlab/mmdetection/tree/master/configs/regnet/mask_rcnn_regnetx-1.6GF_fpn_mstrain_3x_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/regnet/mask_rcnn_regnetx-1.6GF_fpn_mstrain-poly_3x_coco/mask_rcnn_regnetx-1_20210602_210641-6764cff5.pth) | [log](https://download.openmmlab.com/mmdetection/v2.0/regnet/mask_rcnn_regnetx-1.6GF_fpn_mstrain-poly_3x_coco/mask_rcnn_regnetx-1_20210602_210641.log.json) | +|Mask RCNN |[RegNetX-3.2GF-FPN](./mask_rcnn_regnetx-3.2GF_fpn_mstrain_3x_coco.py) | pytorch | 3x |5.0 ||43.1|38.7|[config](https://github.com/open-mmlab/mmdetection/tree/master/configs/regnet/mask_rcnn_regnetx-3.2GF_fpn_mstrain_3x_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/regnet/mask_rcnn_regnetx-3.2GF_fpn_mstrain_3x_coco/mask_rcnn_regnetx-3.2GF_fpn_mstrain_3x_coco_20200521_202221-99879813.pth) | [log](https://download.openmmlab.com/mmdetection/v2.0/regnet/mask_rcnn_regnetx-3.2GF_fpn_mstrain_3x_coco/mask_rcnn_regnetx-3.2GF_fpn_mstrain_3x_coco_20200521_202221.log.json) | +|Mask RCNN |[RegNetX-4GF-FPN](./mask_rcnn_regnetx-4GF_fpn_mstrain-poly_3x_coco.py) | pytorch | 3x |5.1 ||43.4|39.2|[config](https://github.com/open-mmlab/mmdetection/tree/master/configs/regnet/mask_rcnn_regnetx-4GF_fpn_mstrain_3x_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/regnet/mask_rcnn_regnetx-4GF_fpn_mstrain-poly_3x_coco/mask_rcnn_regnetx-4GF_fpn_mstrain-poly_3x_coco_20210602_032621-00f0331c.pth) | [log](https://download.openmmlab.com/mmdetection/v2.0/regnet/mask_rcnn_regnetx-4GF_fpn_mstrain-poly_3x_coco/mask_rcnn_regnetx-4GF_fpn_mstrain-poly_3x_coco_20210602_032621.log.json) | +|Cascade Mask RCNN |[RegNetX-400MF-FPN](./cascade_mask_rcnn_regnetx-400MF_fpn_mstrain_3x_coco.py)| pytorch | 3x |4.3||41.6|36.4|[config](https://github.com/open-mmlab/mmdetection/tree/master/configs/regnet/cascade_mask_rcnn_regnetx-400MF_fpn_mstrain_3x_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/regnet/cascade_mask_rcnn_regnetx-400MF_fpn_mstrain_3x_coco/cascade_mask_rcnn_regnetx-400MF_fpn_mstrain_3x_coco_20210715_211619-5142f449.pth) | [log](https://download.openmmlab.com/mmdetection/v2.0/regnet/cascade_mask_rcnn_regnetx-400MF_fpn_mstrain_3x_coco/cascade_mask_rcnn_regnetx-400MF_fpn_mstrain_3x_coco_20210715_211619.log.json) | +|Cascade Mask RCNN |[RegNetX-800MF-FPN](./cascade_mask_rcnn_regnetx-800MF_fpn_mstrain_3x_coco.py)| pytorch | 3x |4.8||42.8|37.6|[config](https://github.com/open-mmlab/mmdetection/tree/master/configs/regnet/cascade_mask_rcnn_regnetx-800MF_fpn_mstrain_3x_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/regnet/cascade_mask_rcnn_regnetx-800MF_fpn_mstrain_3x_coco/cascade_mask_rcnn_regnetx-800MF_fpn_mstrain_3x_coco_20210715_211616-dcbd13f4.pth) | [log](https://download.openmmlab.com/mmdetection/v2.0/regnet/cascade_mask_rcnn_regnetx-800MF_fpn_mstrain_3x_coco/cascade_mask_rcnn_regnetx-800MF_fpn_mstrain_3x_coco_20210715_211616.log.json) | +|Cascade Mask RCNN |[RegNetX-1.6GF-FPN](./cascade_mask_rcnn_regnetx-1.6GF_fpn_mstrain_3x_coco.py)| pytorch | 3x |5.4||44.5|39.0|[config](https://github.com/open-mmlab/mmdetection/tree/master/configs/regnet/cascade_mask_rcnn_regnetx-1.6GF_fpn_mstrain_3x_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/regnet/cascade_mask_rcnn_regnetx-1.6GF_fpn_mstrain_3x_coco/cascade_mask_rcnn_regnetx-1_20210715_211616-75f29a61.pth) | [log](https://download.openmmlab.com/mmdetection/v2.0/regnet/cascade_mask_rcnn_regnetx-1.6GF_fpn_mstrain_3x_coco/cascade_mask_rcnn_regnetx-1_20210715_211616.log.json) | +|Cascade Mask RCNN |[RegNetX-3.2GF-FPN](./cascade_mask_rcnn_regnetx-3.2GF_fpn_mstrain_3x_coco.py)| pytorch | 3x |6.4||45.8|40.0|[config](https://github.com/open-mmlab/mmdetection/tree/master/configs/regnet/cascade_mask_rcnn_regnetx-3.2GF_fpn_mstrain_3x_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/regnet/cascade_mask_rcnn_regnetx-3.2GF_fpn_mstrain_3x_coco/cascade_mask_rcnn_regnetx-3_20210715_211616-b9c2c58b.pth) | [log](https://download.openmmlab.com/mmdetection/v2.0/regnet/cascade_mask_rcnn_regnetx-3.2GF_fpn_mstrain_3x_coco/cascade_mask_rcnn_regnetx-3_20210715_211616.log.json) | +|Cascade Mask RCNN |[RegNetX-4GF-FPN](./cascade_mask_rcnn_regnetx-4GF_fpn_mstrain_3x_coco.py) | pytorch | 3x |6.9||45.8|40.0|[config](https://github.com/open-mmlab/mmdetection/tree/master/configs/regnet/cascade_mask_rcnn_regnetx-4GF_fpn_mstrain_3x_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/regnet/cascade_mask_rcnn_regnetx-4GF_fpn_mstrain_3x_coco/cascade_mask_rcnn_regnetx-4GF_fpn_mstrain_3x_coco_20210715_212034-cbb1be4c.pth) | [log](https://download.openmmlab.com/mmdetection/v2.0/regnet/cascade_mask_rcnn_regnetx-4GF_fpn_mstrain_3x_coco/cascade_mask_rcnn_regnetx-4GF_fpn_mstrain_3x_coco_20210715_212034.log.json) | ### Notice diff --git a/configs/regnet/cascade_mask_rcnn_regnetx-1.6GF_fpn_mstrain_3x_coco.py b/configs/regnet/cascade_mask_rcnn_regnetx-1.6GF_fpn_mstrain_3x_coco.py new file mode 100644 index 0000000..358d85a --- /dev/null +++ b/configs/regnet/cascade_mask_rcnn_regnetx-1.6GF_fpn_mstrain_3x_coco.py @@ -0,0 +1,17 @@ +_base_ = 'cascade_mask_rcnn_regnetx-3.2GF_fpn_mstrain_3x_coco.py' +model = dict( + backbone=dict( + type='RegNet', + arch='regnetx_1.6gf', + out_indices=(0, 1, 2, 3), + frozen_stages=1, + norm_cfg=dict(type='BN', requires_grad=True), + norm_eval=True, + style='pytorch', + init_cfg=dict( + type='Pretrained', checkpoint='open-mmlab://regnetx_1.6gf')), + neck=dict( + type='FPN', + in_channels=[72, 168, 408, 912], + out_channels=256, + num_outs=5)) diff --git a/configs/regnet/cascade_mask_rcnn_regnetx-3.2GF_fpn_mstrain_3x_coco.py b/configs/regnet/cascade_mask_rcnn_regnetx-3.2GF_fpn_mstrain_3x_coco.py new file mode 100644 index 0000000..8464571 --- /dev/null +++ b/configs/regnet/cascade_mask_rcnn_regnetx-3.2GF_fpn_mstrain_3x_coco.py @@ -0,0 +1,63 @@ +_base_ = [ + '../common/mstrain_3x_coco_instance.py', + '../_base_/models/cascade_mask_rcnn_r50_fpn.py' +] +model = dict( + backbone=dict( + _delete_=True, + type='RegNet', + arch='regnetx_3.2gf', + out_indices=(0, 1, 2, 3), + frozen_stages=1, + norm_cfg=dict(type='BN', requires_grad=True), + norm_eval=True, + style='pytorch', + init_cfg=dict( + type='Pretrained', checkpoint='open-mmlab://regnetx_3.2gf')), + neck=dict( + type='FPN', + in_channels=[96, 192, 432, 1008], + out_channels=256, + num_outs=5)) +img_norm_cfg = dict( + # The mean and std are used in PyCls when training RegNets + mean=[103.53, 116.28, 123.675], + std=[57.375, 57.12, 58.395], + to_rgb=False) +train_pipeline = [ + # Images are converted to float32 directly after loading in PyCls + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations', with_bbox=True, with_mask=True), + dict( + type='Resize', + img_scale=[(1333, 640), (1333, 800)], + multiscale_mode='range', + keep_ratio=True), + dict(type='RandomFlip', flip_ratio=0.5), + dict(type='Normalize', **img_norm_cfg), + dict(type='Pad', size_divisor=32), + dict(type='DefaultFormatBundle'), + dict(type='Collect', keys=['img', 'gt_bboxes', 'gt_labels', 'gt_masks']), +] +test_pipeline = [ + dict(type='LoadImageFromFile'), + dict( + type='MultiScaleFlipAug', + img_scale=(1333, 800), + flip=False, + transforms=[ + dict(type='Resize', keep_ratio=True), + dict(type='RandomFlip'), + dict(type='Normalize', **img_norm_cfg), + dict(type='Pad', size_divisor=32), + dict(type='ImageToTensor', keys=['img']), + dict(type='Collect', keys=['img']), + ]) +] + +data = dict( + train=dict(dataset=dict(pipeline=train_pipeline)), + val=dict(pipeline=test_pipeline), + test=dict(pipeline=test_pipeline)) + +optimizer = dict(weight_decay=0.00005) diff --git a/configs/regnet/cascade_mask_rcnn_regnetx-400MF_fpn_mstrain_3x_coco.py b/configs/regnet/cascade_mask_rcnn_regnetx-400MF_fpn_mstrain_3x_coco.py new file mode 100644 index 0000000..2a8990a --- /dev/null +++ b/configs/regnet/cascade_mask_rcnn_regnetx-400MF_fpn_mstrain_3x_coco.py @@ -0,0 +1,17 @@ +_base_ = 'cascade_mask_rcnn_regnetx-3.2GF_fpn_mstrain_3x_coco.py' +model = dict( + backbone=dict( + type='RegNet', + arch='regnetx_400mf', + out_indices=(0, 1, 2, 3), + frozen_stages=1, + norm_cfg=dict(type='BN', requires_grad=True), + norm_eval=True, + style='pytorch', + init_cfg=dict( + type='Pretrained', checkpoint='open-mmlab://regnetx_400mf')), + neck=dict( + type='FPN', + in_channels=[32, 64, 160, 384], + out_channels=256, + num_outs=5)) diff --git a/configs/regnet/cascade_mask_rcnn_regnetx-4GF_fpn_mstrain_3x_coco.py b/configs/regnet/cascade_mask_rcnn_regnetx-4GF_fpn_mstrain_3x_coco.py new file mode 100644 index 0000000..3157863 --- /dev/null +++ b/configs/regnet/cascade_mask_rcnn_regnetx-4GF_fpn_mstrain_3x_coco.py @@ -0,0 +1,17 @@ +_base_ = 'cascade_mask_rcnn_regnetx-3.2GF_fpn_mstrain_3x_coco.py' +model = dict( + backbone=dict( + type='RegNet', + arch='regnetx_4.0gf', + out_indices=(0, 1, 2, 3), + frozen_stages=1, + norm_cfg=dict(type='BN', requires_grad=True), + norm_eval=True, + style='pytorch', + init_cfg=dict( + type='Pretrained', checkpoint='open-mmlab://regnetx_4.0gf')), + neck=dict( + type='FPN', + in_channels=[80, 240, 560, 1360], + out_channels=256, + num_outs=5)) diff --git a/configs/regnet/cascade_mask_rcnn_regnetx-800MF_fpn_mstrain_3x_coco.py b/configs/regnet/cascade_mask_rcnn_regnetx-800MF_fpn_mstrain_3x_coco.py new file mode 100644 index 0000000..41376ad --- /dev/null +++ b/configs/regnet/cascade_mask_rcnn_regnetx-800MF_fpn_mstrain_3x_coco.py @@ -0,0 +1,17 @@ +_base_ = 'cascade_mask_rcnn_regnetx-3.2GF_fpn_mstrain_3x_coco.py' +model = dict( + backbone=dict( + type='RegNet', + arch='regnetx_800mf', + out_indices=(0, 1, 2, 3), + frozen_stages=1, + norm_cfg=dict(type='BN', requires_grad=True), + norm_eval=True, + style='pytorch', + init_cfg=dict( + type='Pretrained', checkpoint='open-mmlab://regnetx_800mf')), + neck=dict( + type='FPN', + in_channels=[64, 128, 288, 672], + out_channels=256, + num_outs=5)) diff --git a/configs/regnet/faster_rcnn_regnetx-1.6GF_fpn_mstrain_3x_coco.py b/configs/regnet/faster_rcnn_regnetx-1.6GF_fpn_mstrain_3x_coco.py new file mode 100644 index 0000000..385b5ca --- /dev/null +++ b/configs/regnet/faster_rcnn_regnetx-1.6GF_fpn_mstrain_3x_coco.py @@ -0,0 +1,17 @@ +_base_ = 'faster_rcnn_regnetx-3.2GF_fpn_mstrain_3x_coco.py' +model = dict( + backbone=dict( + type='RegNet', + arch='regnetx_1.6gf', + out_indices=(0, 1, 2, 3), + frozen_stages=1, + norm_cfg=dict(type='BN', requires_grad=True), + norm_eval=True, + style='pytorch', + init_cfg=dict( + type='Pretrained', checkpoint='open-mmlab://regnetx_1.6gf')), + neck=dict( + type='FPN', + in_channels=[72, 168, 408, 912], + out_channels=256, + num_outs=5)) diff --git a/configs/regnet/faster_rcnn_regnetx-3.2GF_fpn_1x_coco.py b/configs/regnet/faster_rcnn_regnetx-3.2GF_fpn_1x_coco.py index 4fc61a3..88d270e 100644 --- a/configs/regnet/faster_rcnn_regnetx-3.2GF_fpn_1x_coco.py +++ b/configs/regnet/faster_rcnn_regnetx-3.2GF_fpn_1x_coco.py @@ -4,7 +4,6 @@ '../_base_/schedules/schedule_1x.py', '../_base_/default_runtime.py' ] model = dict( - pretrained='open-mmlab://regnetx_3.2gf', backbone=dict( _delete_=True, type='RegNet', @@ -13,7 +12,9 @@ frozen_stages=1, norm_cfg=dict(type='BN', requires_grad=True), norm_eval=True, - style='pytorch'), + style='pytorch', + init_cfg=dict( + type='Pretrained', checkpoint='open-mmlab://regnetx_3.2gf')), neck=dict( type='FPN', in_channels=[96, 192, 432, 1008], diff --git a/configs/regnet/faster_rcnn_regnetx-3.2GF_fpn_mstrain_3x_coco.py b/configs/regnet/faster_rcnn_regnetx-3.2GF_fpn_mstrain_3x_coco.py index e73a098..b7e6e1a 100644 --- a/configs/regnet/faster_rcnn_regnetx-3.2GF_fpn_mstrain_3x_coco.py +++ b/configs/regnet/faster_rcnn_regnetx-3.2GF_fpn_mstrain_3x_coco.py @@ -1,10 +1,7 @@ _base_ = [ - '../_base_/models/faster_rcnn_r50_fpn.py', - '../_base_/datasets/coco_detection.py', - '../_base_/schedules/schedule_1x.py', '../_base_/default_runtime.py' + '../common/mstrain_3x_coco.py', '../_base_/models/faster_rcnn_r50_fpn.py' ] model = dict( - pretrained='open-mmlab://regnetx_3.2gf', backbone=dict( _delete_=True, type='RegNet', @@ -13,7 +10,9 @@ frozen_stages=1, norm_cfg=dict(type='BN', requires_grad=True), norm_eval=True, - style='pytorch'), + style='pytorch', + init_cfg=dict( + type='Pretrained', checkpoint='open-mmlab://regnetx_3.2gf')), neck=dict( type='FPN', in_channels=[96, 192, 432, 1008], @@ -29,9 +28,8 @@ dict(type='LoadAnnotations', with_bbox=True), dict( type='Resize', - img_scale=[(1333, 640), (1333, 672), (1333, 704), (1333, 736), - (1333, 768), (1333, 800)], - multiscale_mode='value', + img_scale=[(1333, 640), (1333, 800)], + multiscale_mode='range', keep_ratio=True), dict(type='RandomFlip', flip_ratio=0.5), dict(type='Normalize', **img_norm_cfg), @@ -54,10 +52,10 @@ dict(type='Collect', keys=['img']), ]) ] + data = dict( - train=dict(pipeline=train_pipeline), + train=dict(dataset=dict(pipeline=train_pipeline)), val=dict(pipeline=test_pipeline), test=dict(pipeline=test_pipeline)) -optimizer = dict(type='SGD', lr=0.02, momentum=0.9, weight_decay=0.00005) -lr_config = dict(step=[28, 34]) -runner = dict(type='EpochBasedRunner', max_epochs=36) + +optimizer = dict(weight_decay=0.00005) diff --git a/configs/regnet/faster_rcnn_regnetx-400MF_fpn_mstrain_3x_coco.py b/configs/regnet/faster_rcnn_regnetx-400MF_fpn_mstrain_3x_coco.py new file mode 100644 index 0000000..0a05f6e --- /dev/null +++ b/configs/regnet/faster_rcnn_regnetx-400MF_fpn_mstrain_3x_coco.py @@ -0,0 +1,17 @@ +_base_ = 'faster_rcnn_regnetx-3.2GF_fpn_mstrain_3x_coco.py' +model = dict( + backbone=dict( + type='RegNet', + arch='regnetx_400mf', + out_indices=(0, 1, 2, 3), + frozen_stages=1, + norm_cfg=dict(type='BN', requires_grad=True), + norm_eval=True, + style='pytorch', + init_cfg=dict( + type='Pretrained', checkpoint='open-mmlab://regnetx_400mf')), + neck=dict( + type='FPN', + in_channels=[32, 64, 160, 384], + out_channels=256, + num_outs=5)) diff --git a/configs/regnet/faster_rcnn_regnetx-4GF_fpn_mstrain_3x_coco.py b/configs/regnet/faster_rcnn_regnetx-4GF_fpn_mstrain_3x_coco.py new file mode 100644 index 0000000..98b3fc2 --- /dev/null +++ b/configs/regnet/faster_rcnn_regnetx-4GF_fpn_mstrain_3x_coco.py @@ -0,0 +1,17 @@ +_base_ = 'faster_rcnn_regnetx-3.2GF_fpn_mstrain_3x_coco.py' +model = dict( + backbone=dict( + type='RegNet', + arch='regnetx_4.0gf', + out_indices=(0, 1, 2, 3), + frozen_stages=1, + norm_cfg=dict(type='BN', requires_grad=True), + norm_eval=True, + style='pytorch', + init_cfg=dict( + type='Pretrained', checkpoint='open-mmlab://regnetx_4.0gf')), + neck=dict( + type='FPN', + in_channels=[80, 240, 560, 1360], + out_channels=256, + num_outs=5)) diff --git a/configs/regnet/faster_rcnn_regnetx-800MF_fpn_mstrain_3x_coco.py b/configs/regnet/faster_rcnn_regnetx-800MF_fpn_mstrain_3x_coco.py new file mode 100644 index 0000000..67f448b --- /dev/null +++ b/configs/regnet/faster_rcnn_regnetx-800MF_fpn_mstrain_3x_coco.py @@ -0,0 +1,17 @@ +_base_ = 'faster_rcnn_regnetx-3.2GF_fpn_mstrain_3x_coco.py' +model = dict( + backbone=dict( + type='RegNet', + arch='regnetx_800mf', + out_indices=(0, 1, 2, 3), + frozen_stages=1, + norm_cfg=dict(type='BN', requires_grad=True), + norm_eval=True, + style='pytorch', + init_cfg=dict( + type='Pretrained', checkpoint='open-mmlab://regnetx_800mf')), + neck=dict( + type='FPN', + in_channels=[64, 128, 288, 672], + out_channels=256, + num_outs=5)) diff --git a/configs/regnet/mask_rcnn_regnetx-1.6GF_fpn_mstrain-poly_3x_coco.py b/configs/regnet/mask_rcnn_regnetx-1.6GF_fpn_mstrain-poly_3x_coco.py new file mode 100644 index 0000000..7970c3c --- /dev/null +++ b/configs/regnet/mask_rcnn_regnetx-1.6GF_fpn_mstrain-poly_3x_coco.py @@ -0,0 +1,26 @@ +_base_ = [ + '../common/mstrain-poly_3x_coco_instance.py', + '../_base_/models/mask_rcnn_r50_fpn.py' +] + +model = dict( + backbone=dict( + _delete_=True, + type='RegNet', + arch='regnetx_1.6gf', + out_indices=(0, 1, 2, 3), + frozen_stages=1, + norm_cfg=dict(type='BN', requires_grad=True), + norm_eval=True, + style='pytorch', + init_cfg=dict( + type='Pretrained', checkpoint='open-mmlab://regnetx_1.6gf')), + neck=dict( + type='FPN', + in_channels=[72, 168, 408, 912], + out_channels=256, + num_outs=5)) + +optimizer = dict(type='SGD', lr=0.02, momentum=0.9, weight_decay=0.00005) +optimizer_config = dict( + _delete_=True, grad_clip=dict(max_norm=35, norm_type=2)) diff --git a/configs/regnet/mask_rcnn_regnetx-12GF_fpn_1x_coco.py b/configs/regnet/mask_rcnn_regnetx-12GF_fpn_1x_coco.py index 104d6d4..ce3661c 100644 --- a/configs/regnet/mask_rcnn_regnetx-12GF_fpn_1x_coco.py +++ b/configs/regnet/mask_rcnn_regnetx-12GF_fpn_1x_coco.py @@ -1,6 +1,5 @@ _base_ = './mask_rcnn_regnetx-3.2GF_fpn_1x_coco.py' model = dict( - pretrained='open-mmlab://regnetx_12gf', backbone=dict( type='RegNet', arch='regnetx_12gf', @@ -8,7 +7,9 @@ frozen_stages=1, norm_cfg=dict(type='BN', requires_grad=True), norm_eval=True, - style='pytorch'), + style='pytorch', + init_cfg=dict( + type='Pretrained', checkpoint='open-mmlab://regnetx_12gf')), neck=dict( type='FPN', in_channels=[224, 448, 896, 2240], diff --git a/configs/regnet/mask_rcnn_regnetx-3.2GF_fpn_1x_coco.py b/configs/regnet/mask_rcnn_regnetx-3.2GF_fpn_1x_coco.py index 19168b5..44bf0d1 100644 --- a/configs/regnet/mask_rcnn_regnetx-3.2GF_fpn_1x_coco.py +++ b/configs/regnet/mask_rcnn_regnetx-3.2GF_fpn_1x_coco.py @@ -4,7 +4,6 @@ '../_base_/schedules/schedule_1x.py', '../_base_/default_runtime.py' ] model = dict( - pretrained='open-mmlab://regnetx_3.2gf', backbone=dict( _delete_=True, type='RegNet', @@ -13,7 +12,9 @@ frozen_stages=1, norm_cfg=dict(type='BN', requires_grad=True), norm_eval=True, - style='pytorch'), + style='pytorch', + init_cfg=dict( + type='Pretrained', checkpoint='open-mmlab://regnetx_3.2gf')), neck=dict( type='FPN', in_channels=[96, 192, 432, 1008], diff --git a/configs/regnet/mask_rcnn_regnetx-3.2GF_fpn_mdconv_c3-c5_1x_coco.py b/configs/regnet/mask_rcnn_regnetx-3.2GF_fpn_mdconv_c3-c5_1x_coco.py index dd5153e..5b53428 100644 --- a/configs/regnet/mask_rcnn_regnetx-3.2GF_fpn_mdconv_c3-c5_1x_coco.py +++ b/configs/regnet/mask_rcnn_regnetx-3.2GF_fpn_mdconv_c3-c5_1x_coco.py @@ -1,6 +1,7 @@ _base_ = 'mask_rcnn_regnetx-3.2GF_fpn_1x_coco.py' model = dict( - pretrained='open-mmlab://regnetx_3.2gf', backbone=dict( dcn=dict(type='DCNv2', deform_groups=1, fallback_on_stride=False), - stage_with_dcn=(False, True, True, True))) + stage_with_dcn=(False, True, True, True), + init_cfg=dict( + type='Pretrained', checkpoint='open-mmlab://regnetx_3.2gf'))) diff --git a/configs/regnet/mask_rcnn_regnetx-3.2GF_fpn_mstrain_3x_coco.py b/configs/regnet/mask_rcnn_regnetx-3.2GF_fpn_mstrain_3x_coco.py index e4107e7..aca64d3 100644 --- a/configs/regnet/mask_rcnn_regnetx-3.2GF_fpn_mstrain_3x_coco.py +++ b/configs/regnet/mask_rcnn_regnetx-3.2GF_fpn_mstrain_3x_coco.py @@ -4,7 +4,6 @@ '../_base_/schedules/schedule_1x.py', '../_base_/default_runtime.py' ] model = dict( - pretrained='open-mmlab://regnetx_3.2gf', backbone=dict( _delete_=True, type='RegNet', @@ -13,7 +12,9 @@ frozen_stages=1, norm_cfg=dict(type='BN', requires_grad=True), norm_eval=True, - style='pytorch'), + style='pytorch', + init_cfg=dict( + type='Pretrained', checkpoint='open-mmlab://regnetx_3.2gf')), neck=dict( type='FPN', in_channels=[96, 192, 432, 1008], diff --git a/configs/regnet/mask_rcnn_regnetx-400MF_fpn_mstrain-poly_3x_coco.py b/configs/regnet/mask_rcnn_regnetx-400MF_fpn_mstrain-poly_3x_coco.py new file mode 100644 index 0000000..c38dfa6 --- /dev/null +++ b/configs/regnet/mask_rcnn_regnetx-400MF_fpn_mstrain-poly_3x_coco.py @@ -0,0 +1,26 @@ +_base_ = [ + '../common/mstrain-poly_3x_coco_instance.py', + '../_base_/models/mask_rcnn_r50_fpn.py' +] + +model = dict( + backbone=dict( + _delete_=True, + type='RegNet', + arch='regnetx_400mf', + out_indices=(0, 1, 2, 3), + frozen_stages=1, + norm_cfg=dict(type='BN', requires_grad=True), + norm_eval=True, + style='pytorch', + init_cfg=dict( + type='Pretrained', checkpoint='open-mmlab://regnetx_400mf')), + neck=dict( + type='FPN', + in_channels=[32, 64, 160, 384], + out_channels=256, + num_outs=5)) + +optimizer = dict(type='SGD', lr=0.02, momentum=0.9, weight_decay=0.00005) +optimizer_config = dict( + _delete_=True, grad_clip=dict(max_norm=35, norm_type=2)) diff --git a/configs/regnet/mask_rcnn_regnetx-4GF_fpn_1x_coco.py b/configs/regnet/mask_rcnn_regnetx-4GF_fpn_1x_coco.py index 8830ef0..874d485 100644 --- a/configs/regnet/mask_rcnn_regnetx-4GF_fpn_1x_coco.py +++ b/configs/regnet/mask_rcnn_regnetx-4GF_fpn_1x_coco.py @@ -1,6 +1,5 @@ _base_ = './mask_rcnn_regnetx-3.2GF_fpn_1x_coco.py' model = dict( - pretrained='open-mmlab://regnetx_4.0gf', backbone=dict( type='RegNet', arch='regnetx_4.0gf', @@ -8,7 +7,9 @@ frozen_stages=1, norm_cfg=dict(type='BN', requires_grad=True), norm_eval=True, - style='pytorch'), + style='pytorch', + init_cfg=dict( + type='Pretrained', checkpoint='open-mmlab://regnetx_4.0gf')), neck=dict( type='FPN', in_channels=[80, 240, 560, 1360], diff --git a/configs/regnet/mask_rcnn_regnetx-4GF_fpn_mstrain-poly_3x_coco.py b/configs/regnet/mask_rcnn_regnetx-4GF_fpn_mstrain-poly_3x_coco.py new file mode 100644 index 0000000..f0b65ea --- /dev/null +++ b/configs/regnet/mask_rcnn_regnetx-4GF_fpn_mstrain-poly_3x_coco.py @@ -0,0 +1,26 @@ +_base_ = [ + '../common/mstrain-poly_3x_coco_instance.py', + '../_base_/models/mask_rcnn_r50_fpn.py' +] + +model = dict( + backbone=dict( + _delete_=True, + type='RegNet', + arch='regnetx_4.0gf', + out_indices=(0, 1, 2, 3), + frozen_stages=1, + norm_cfg=dict(type='BN', requires_grad=True), + norm_eval=True, + style='pytorch', + init_cfg=dict( + type='Pretrained', checkpoint='open-mmlab://regnetx_4.0gf')), + neck=dict( + type='FPN', + in_channels=[80, 240, 560, 1360], + out_channels=256, + num_outs=5)) + +optimizer = dict(type='SGD', lr=0.02, momentum=0.9, weight_decay=0.00005) +optimizer_config = dict( + _delete_=True, grad_clip=dict(max_norm=35, norm_type=2)) diff --git a/configs/regnet/mask_rcnn_regnetx-6.4GF_fpn_1x_coco.py b/configs/regnet/mask_rcnn_regnetx-6.4GF_fpn_1x_coco.py index 7569ef3..99387d8 100644 --- a/configs/regnet/mask_rcnn_regnetx-6.4GF_fpn_1x_coco.py +++ b/configs/regnet/mask_rcnn_regnetx-6.4GF_fpn_1x_coco.py @@ -1,6 +1,5 @@ _base_ = './mask_rcnn_regnetx-3.2GF_fpn_1x_coco.py' model = dict( - pretrained='open-mmlab://regnetx_6.4gf', backbone=dict( type='RegNet', arch='regnetx_6.4gf', @@ -8,7 +7,9 @@ frozen_stages=1, norm_cfg=dict(type='BN', requires_grad=True), norm_eval=True, - style='pytorch'), + style='pytorch', + init_cfg=dict( + type='Pretrained', checkpoint='open-mmlab://regnetx_6.4gf')), neck=dict( type='FPN', in_channels=[168, 392, 784, 1624], diff --git a/configs/regnet/mask_rcnn_regnetx-800MF_fpn_mstrain-poly_3x_coco.py b/configs/regnet/mask_rcnn_regnetx-800MF_fpn_mstrain-poly_3x_coco.py new file mode 100644 index 0000000..335ebab --- /dev/null +++ b/configs/regnet/mask_rcnn_regnetx-800MF_fpn_mstrain-poly_3x_coco.py @@ -0,0 +1,26 @@ +_base_ = [ + '../common/mstrain-poly_3x_coco_instance.py', + '../_base_/models/mask_rcnn_r50_fpn.py' +] + +model = dict( + backbone=dict( + _delete_=True, + type='RegNet', + arch='regnetx_800mf', + out_indices=(0, 1, 2, 3), + frozen_stages=1, + norm_cfg=dict(type='BN', requires_grad=True), + norm_eval=True, + style='pytorch', + init_cfg=dict( + type='Pretrained', checkpoint='open-mmlab://regnetx_800mf')), + neck=dict( + type='FPN', + in_channels=[64, 128, 288, 672], + out_channels=256, + num_outs=5)) + +optimizer = dict(type='SGD', lr=0.02, momentum=0.9, weight_decay=0.00005) +optimizer_config = dict( + _delete_=True, grad_clip=dict(max_norm=35, norm_type=2)) diff --git a/configs/regnet/mask_rcnn_regnetx-8GF_fpn_1x_coco.py b/configs/regnet/mask_rcnn_regnetx-8GF_fpn_1x_coco.py index b589026..1e7832f 100644 --- a/configs/regnet/mask_rcnn_regnetx-8GF_fpn_1x_coco.py +++ b/configs/regnet/mask_rcnn_regnetx-8GF_fpn_1x_coco.py @@ -1,6 +1,5 @@ _base_ = './mask_rcnn_regnetx-3.2GF_fpn_1x_coco.py' model = dict( - pretrained='open-mmlab://regnetx_8.0gf', backbone=dict( type='RegNet', arch='regnetx_8.0gf', @@ -8,7 +7,9 @@ frozen_stages=1, norm_cfg=dict(type='BN', requires_grad=True), norm_eval=True, - style='pytorch'), + style='pytorch', + init_cfg=dict( + type='Pretrained', checkpoint='open-mmlab://regnetx_8.0gf')), neck=dict( type='FPN', in_channels=[80, 240, 720, 1920], diff --git a/configs/regnet/metafile.yml b/configs/regnet/metafile.yml new file mode 100644 index 0000000..5390a35 --- /dev/null +++ b/configs/regnet/metafile.yml @@ -0,0 +1,437 @@ +Collections: + - Name: RegNet + Metadata: + Training Data: COCO + Training Techniques: + - SGD with Momentum + - Weight Decay + Training Resources: 8x V100 GPUs + Architecture: + - RegNet + Paper: + URL: https://arxiv.org/abs/2003.13678 + Title: 'Designing Network Design Spaces' + README: configs/regnet/README.md + Code: + URL: https://github.com/open-mmlab/mmdetection/blob/v2.1.0/mmdet/models/backbones/regnet.py#L11 + Version: v2.1.0 + +Models: + - Name: mask_rcnn_regnetx-3.2GF_fpn_1x_coco + In Collection: RegNet + Config: configs/regnet/mask_rcnn_regnetx-3.2GF_fpn_1x_coco.py + Metadata: + Training Memory (GB): 5.0 + Epochs: 12 + Results: + - Task: Object Detection + Dataset: COCO + Metrics: + box AP: 40.3 + - Task: Instance Segmentation + Dataset: COCO + Metrics: + mask AP: 36.6 + Weights: https://download.openmmlab.com/mmdetection/v2.0/regnet/mask_rcnn_regnetx-3.2GF_fpn_1x_coco/mask_rcnn_regnetx-3.2GF_fpn_1x_coco_20200520_163141-2a9d1814.pth + + - Name: mask_rcnn_regnetx-4GF_fpn_1x_coco + In Collection: RegNet + Config: configs/regnet/mask_rcnn_regnetx-4GF_fpn_1x_coco.py + Metadata: + Training Memory (GB): 5.5 + Epochs: 12 + Results: + - Task: Object Detection + Dataset: COCO + Metrics: + box AP: 41.5 + - Task: Instance Segmentation + Dataset: COCO + Metrics: + mask AP: 37.4 + Weights: https://download.openmmlab.com/mmdetection/v2.0/regnet/mask_rcnn_regnetx-4GF_fpn_1x_coco/mask_rcnn_regnetx-4GF_fpn_1x_coco_20200517_180217-32e9c92d.pth + + - Name: mask_rcnn_regnetx-6.4GF_fpn_1x_coco + In Collection: RegNet + Config: configs/regnet/mask_rcnn_regnetx-6.4GF_fpn_1x_coco.py + Metadata: + Training Memory (GB): 6.1 + Epochs: 12 + Results: + - Task: Object Detection + Dataset: COCO + Metrics: + box AP: 41.0 + - Task: Instance Segmentation + Dataset: COCO + Metrics: + mask AP: 37.1 + Weights: https://download.openmmlab.com/mmdetection/v2.0/regnet/mask_rcnn_regnetx-6.4GF_fpn_1x_coco/mask_rcnn_regnetx-6.4GF_fpn_1x_coco_20200517_180439-3a7aae83.pth + + - Name: mask_rcnn_regnetx-8GF_fpn_1x_coco + In Collection: RegNet + Config: configs/regnet/mask_rcnn_regnetx-8GF_fpn_1x_coco.py + Metadata: + Training Memory (GB): 6.4 + Epochs: 12 + Results: + - Task: Object Detection + Dataset: COCO + Metrics: + box AP: 41.7 + - Task: Instance Segmentation + Dataset: COCO + Metrics: + mask AP: 37.5 + Weights: https://download.openmmlab.com/mmdetection/v2.0/regnet/mask_rcnn_regnetx-8GF_fpn_1x_coco/mask_rcnn_regnetx-8GF_fpn_1x_coco_20200517_180515-09daa87e.pth + + - Name: mask_rcnn_regnetx-12GF_fpn_1x_coco + In Collection: RegNet + Config: configs/regnet/mask_rcnn_regnetx-12GF_fpn_1x_coco.py + Metadata: + Training Memory (GB): 7.4 + Epochs: 12 + Results: + - Task: Object Detection + Dataset: COCO + Metrics: + box AP: 42.2 + - Task: Instance Segmentation + Dataset: COCO + Metrics: + mask AP: 38 + Weights: https://download.openmmlab.com/mmdetection/v2.0/regnet/mask_rcnn_regnetx-12GF_fpn_1x_coco/mask_rcnn_regnetx-12GF_fpn_1x_coco_20200517_180552-b538bd8b.pth + + - Name: mask_rcnn_regnetx-3.2GF_fpn_mdconv_c3-c5_1x_coco + In Collection: RegNet + Config: configs/regnet/mask_rcnn_regnetx-3.2GF_fpn_mdconv_c3-c5_1x_coco.py + Metadata: + Training Memory (GB): 5.0 + Epochs: 12 + Results: + - Task: Object Detection + Dataset: COCO + Metrics: + box AP: 40.3 + - Task: Instance Segmentation + Dataset: COCO + Metrics: + mask AP: 36.6 + Weights: https://download.openmmlab.com/mmdetection/v2.0/regnet/mask_rcnn_regnetx-3.2GF_fpn_mdconv_c3-c5_1x_coco/mask_rcnn_regnetx-3.2GF_fpn_mdconv_c3-c5_1x_coco_20200520_172726-75f40794.pth + + - Name: faster_rcnn_regnetx-3.2GF_fpn_1x_coco + In Collection: RegNet + Config: configs/regnet/faster_rcnn_regnetx-3.2GF_fpn_1x_coco.py + Metadata: + Training Memory (GB): 4.5 + Epochs: 12 + Results: + - Task: Object Detection + Dataset: COCO + Metrics: + box AP: 39.9 + Weights: https://download.openmmlab.com/mmdetection/v2.0/regnet/faster_rcnn_regnetx-3.2GF_fpn_1x_coco/faster_rcnn_regnetx-3.2GF_fpn_1x_coco_20200517_175927-126fd9bf.pth + + - Name: faster_rcnn_regnetx-3.2GF_fpn_2x_coco + In Collection: RegNet + Config: configs/regnet/faster_rcnn_regnetx-3.2GF_fpn_2x_coco.py + Metadata: + Training Memory (GB): 4.5 + Epochs: 24 + Results: + - Task: Object Detection + Dataset: COCO + Metrics: + box AP: 41.1 + Weights: https://download.openmmlab.com/mmdetection/v2.0/regnet/faster_rcnn_regnetx-3.2GF_fpn_2x_coco/faster_rcnn_regnetx-3.2GF_fpn_2x_coco_20200520_223955-e2081918.pth + + - Name: retinanet_regnetx-800MF_fpn_1x_coco + In Collection: RegNet + Config: configs/regnet/retinanet_regnetx-800MF_fpn_1x_coco.py + Metadata: + Training Memory (GB): 2.5 + Epochs: 12 + Results: + - Task: Object Detection + Dataset: COCO + Metrics: + box AP: 35.6 + Weights: https://download.openmmlab.com/mmdetection/v2.0/regnet/retinanet_regnetx-800MF_fpn_1x_coco/retinanet_regnetx-800MF_fpn_1x_coco_20200517_191403-f6f91d10.pth + + - Name: retinanet_regnetx-1.6GF_fpn_1x_coco + In Collection: RegNet + Config: configs/regnet/retinanet_regnetx-1.6GF_fpn_1x_coco.py + Metadata: + Training Memory (GB): 3.3 + Epochs: 12 + Results: + - Task: Object Detection + Dataset: COCO + Metrics: + box AP: 37.3 + Weights: https://download.openmmlab.com/mmdetection/v2.0/regnet/retinanet_regnetx-1.6GF_fpn_1x_coco/retinanet_regnetx-1.6GF_fpn_1x_coco_20200517_191403-37009a9d.pth + + - Name: retinanet_regnetx-3.2GF_fpn_1x_coco + In Collection: RegNet + Config: configs/regnet/retinanet_regnetx-3.2GF_fpn_1x_coco.py + Metadata: + Training Memory (GB): 4.2 + Epochs: 12 + Results: + - Task: Object Detection + Dataset: COCO + Metrics: + box AP: 39.1 + Weights: https://download.openmmlab.com/mmdetection/v2.0/regnet/retinanet_regnetx-3.2GF_fpn_1x_coco/retinanet_regnetx-3.2GF_fpn_1x_coco_20200520_163141-cb1509e8.pth + + - Name: faster_rcnn_regnetx-400MF_fpn_mstrain_3x_coco + In Collection: RegNet + Config: configs/regnet/faster_rcnn_regnetx-400MF_fpn_mstrain_3x_coco.py + Metadata: + Training Memory (GB): 2.3 + Epochs: 36 + Results: + - Task: Object Detection + Dataset: COCO + Metrics: + box AP: 37.1 + Weights: https://download.openmmlab.com/mmdetection/v2.0/regnet/faster_rcnn_regnetx-400MF_fpn_mstrain_3x_coco/faster_rcnn_regnetx-400MF_fpn_mstrain_3x_coco_20210526_095112-e1967c37.pth + + - Name: faster_rcnn_regnetx-800MF_fpn_mstrain_3x_coco + In Collection: RegNet + Config: configs/regnet/faster_rcnn_regnetx-800MF_fpn_mstrain_3x_coco.py + Metadata: + Training Memory (GB): 2.8 + Epochs: 36 + Results: + - Task: Object Detection + Dataset: COCO + Metrics: + box AP: 38.8 + Weights: https://download.openmmlab.com/mmdetection/v2.0/regnet/faster_rcnn_regnetx-800MF_fpn_mstrain_3x_coco/faster_rcnn_regnetx-800MF_fpn_mstrain_3x_coco_20210526_095118-a2c70b20.pth + + - Name: faster_rcnn_regnetx-1.6GF_fpn_mstrain_3x_coco + In Collection: RegNet + Config: configs/regnet/faster_rcnn_regnetx-1.6GF_fpn_mstrain_3x_coco.py + Metadata: + Training Memory (GB): 3.4 + Epochs: 36 + Results: + - Task: Object Detection + Dataset: COCO + Metrics: + box AP: 40.5 + Weights: https://download.openmmlab.com/mmdetection/v2.0/regnet/faster_rcnn_regnetx-1.6GF_fpn_mstrain_3x_coco/faster_rcnn_regnetx-1_20210526_095325-94aa46cc.pth + + - Name: faster_rcnn_regnetx-3.2GF_fpn_mstrain_3x_coco + In Collection: RegNet + Config: configs/regnet/faster_rcnn_regnetx-3.2GF_fpn_mstrain_3x_coco.py + Metadata: + Training Memory (GB): 4.4 + Epochs: 36 + Results: + - Task: Object Detection + Dataset: COCO + Metrics: + box AP: 42.3 + Weights: https://download.openmmlab.com/mmdetection/v2.0/regnet/faster_rcnn_regnetx-3.2GF_fpn_mstrain_3x_coco/faster_rcnn_regnetx-3_20210526_095152-e16a5227.pth + + - Name: faster_rcnn_regnetx-4GF_fpn_mstrain_3x_coco + In Collection: RegNet + Config: configs/regnet/faster_rcnn_regnetx-4GF_fpn_mstrain_3x_coco.py + Metadata: + Training Memory (GB): 4.9 + Epochs: 36 + Results: + - Task: Object Detection + Dataset: COCO + Metrics: + box AP: 42.8 + Weights: https://download.openmmlab.com/mmdetection/v2.0/regnet/faster_rcnn_regnetx-4GF_fpn_mstrain_3x_coco/faster_rcnn_regnetx-4GF_fpn_mstrain_3x_coco_20210526_095201-65eaf841.pth + + - Name: mask_rcnn_regnetx-3.2GF_fpn_mstrain_3x_coco + In Collection: RegNet + Config: configs/regnet/mask_rcnn_regnetx-3.2GF_fpn_mstrain_3x_coco.py + Metadata: + Training Memory (GB): 5.0 + Epochs: 36 + Results: + - Task: Object Detection + Dataset: COCO + Metrics: + box AP: 43.1 + - Task: Instance Segmentation + Dataset: COCO + Metrics: + mask AP: 38.7 + Weights: https://download.openmmlab.com/mmdetection/v2.0/regnet/mask_rcnn_regnetx-3.2GF_fpn_mstrain_3x_coco/mask_rcnn_regnetx-3.2GF_fpn_mstrain_3x_coco_20200521_202221-99879813.pth + + - Name: mask_rcnn_regnetx-400MF_fpn_mstrain-poly_3x_coco + In Collection: RegNet + Config: configs/regnet/mask_rcnn_regnetx-400MF_fpn_mstrain-poly_3x_coco.py + Metadata: + Training Memory (GB): 2.5 + Epochs: 36 + Results: + - Task: Object Detection + Dataset: COCO + Metrics: + box AP: 37.6 + - Task: Instance Segmentation + Dataset: COCO + Metrics: + mask AP: 34.4 + Weights: https://download.openmmlab.com/mmdetection/v2.0/regnet/mask_rcnn_regnetx-400MF_fpn_mstrain-poly_3x_coco/mask_rcnn_regnetx-400MF_fpn_mstrain-poly_3x_coco_20210601_235443-8aac57a4.pth + + - Name: mask_rcnn_regnetx-800MF_fpn_mstrain-poly_3x_coco + In Collection: RegNet + Config: configs/regnet/mask_rcnn_regnetx-800MF_fpn_mstrain-poly_3x_coco.py + Metadata: + Training Memory (GB): 2.9 + Epochs: 36 + Results: + - Task: Object Detection + Dataset: COCO + Metrics: + box AP: 39.5 + - Task: Instance Segmentation + Dataset: COCO + Metrics: + mask AP: 36.1 + Weights: https://download.openmmlab.com/mmdetection/v2.0/regnet/mask_rcnn_regnetx-800MF_fpn_mstrain-poly_3x_coco/mask_rcnn_regnetx-800MF_fpn_mstrain-poly_3x_coco_20210602_210641-715d51f5.pth + + - Name: mask_rcnn_regnetx-1.6GF_fpn_mstrain_3x_coco + In Collection: RegNet + Config: configs/regnet/mask_rcnn_regnetx-1.6GF_fpn_mstrain_3x_coco.py + Metadata: + Training Memory (GB): 3.6 + Epochs: 36 + Results: + - Task: Object Detection + Dataset: COCO + Metrics: + box AP: 40.9 + - Task: Instance Segmentation + Dataset: COCO + Metrics: + mask AP: 37.5 + Weights: https://download.openmmlab.com/mmdetection/v2.0/regnet/mask_rcnn_regnetx-1.6GF_fpn_mstrain-poly_3x_coco/mask_rcnn_regnetx-1_20210602_210641-6764cff5.pth + + - Name: mask_rcnn_regnetx-3.2GF_fpn_mstrain_3x_coco + In Collection: RegNet + Config: configs/regnet/mask_rcnn_regnetx-3.2GF_fpn_mstrain_3x_coco.py + Metadata: + Training Memory (GB): 5.0 + Epochs: 36 + Results: + - Task: Object Detection + Dataset: COCO + Metrics: + box AP: 43.1 + - Task: Instance Segmentation + Dataset: COCO + Metrics: + mask AP: 38.7 + Weights: https://download.openmmlab.com/mmdetection/v2.0/regnet/mask_rcnn_regnetx-1.6GF_fpn_mstrain-poly_3x_coco/mask_rcnn_regnetx-1_20210602_210641-6e63e19c.pth + + - Name: mask_rcnn_regnetx-4GF_fpn_mstrain_3x_coco + In Collection: RegNet + Config: configs/regnet/mask_rcnn_regnetx-4GF_fpn_mstrain_3x_coco.py + Metadata: + Training Memory (GB): 5.1 + Epochs: 36 + Results: + - Task: Object Detection + Dataset: COCO + Metrics: + box AP: 43.4 + - Task: Instance Segmentation + Dataset: COCO + Metrics: + mask AP: 39.2 + Weights: https://download.openmmlab.com/mmdetection/v2.0/regnet/mask_rcnn_regnetx-4GF_fpn_mstrain-poly_3x_coco/mask_rcnn_regnetx-4GF_fpn_mstrain-poly_3x_coco_20210602_032621-00f0331c.pth + + - Name: cascade_mask_rcnn_regnetx-400MF_fpn_mstrain_3x_coco + In Collection: RegNet + Config: configs/regnet/cascade_mask_rcnn_regnetx-400MF_fpn_mstrain_3x_coco.py + Metadata: + Training Memory (GB): 4.3 + Epochs: 36 + Results: + - Task: Object Detection + Dataset: COCO + Metrics: + box AP: 41.6 + - Task: Instance Segmentation + Dataset: COCO + Metrics: + mask AP: 36.4 + Weights: https://download.openmmlab.com/mmdetection/v2.0/regnet/cascade_mask_rcnn_regnetx-400MF_fpn_mstrain_3x_coco/cascade_mask_rcnn_regnetx-400MF_fpn_mstrain_3x_coco_20210715_211619-5142f449.pth + + - Name: cascade_mask_rcnn_regnetx-800MF_fpn_mstrain_3x_coco + In Collection: RegNet + Config: configs/regnet/cascade_mask_rcnn_regnetx-800MF_fpn_mstrain_3x_coco.py + Metadata: + Training Memory (GB): 4.8 + Epochs: 36 + Results: + - Task: Object Detection + Dataset: COCO + Metrics: + box AP: 42.8 + - Task: Instance Segmentation + Dataset: COCO + Metrics: + mask AP: 37.6 + Weights: https://download.openmmlab.com/mmdetection/v2.0/regnet/cascade_mask_rcnn_regnetx-800MF_fpn_mstrain_3x_coco/cascade_mask_rcnn_regnetx-800MF_fpn_mstrain_3x_coco_20210715_211616-dcbd13f4.pth + + - Name: cascade_mask_rcnn_regnetx-1.6GF_fpn_mstrain_3x_coco + In Collection: RegNet + Config: configs/regnet/cascade_mask_rcnn_regnetx-1.6GF_fpn_mstrain_3x_coco.py + Metadata: + Training Memory (GB): 5.4 + Epochs: 36 + Results: + - Task: Object Detection + Dataset: COCO + Metrics: + box AP: 44.5 + - Task: Instance Segmentation + Dataset: COCO + Metrics: + mask AP: 39.0 + Weights: https://download.openmmlab.com/mmdetection/v2.0/regnet/cascade_mask_rcnn_regnetx-1.6GF_fpn_mstrain_3x_coco/cascade_mask_rcnn_regnetx-1_20210715_211616-75f29a61.pth + + - Name: cascade_mask_rcnn_regnetx-3.2GF_fpn_mstrain_3x_coco + In Collection: RegNet + Config: configs/regnet/cascade_mask_rcnn_regnetx-3.2GF_fpn_mstrain_3x_coco.py + Metadata: + Training Memory (GB): 6.4 + Epochs: 36 + Results: + - Task: Object Detection + Dataset: COCO + Metrics: + box AP: 45.8 + - Task: Instance Segmentation + Dataset: COCO + Metrics: + mask AP: 40.0 + Weights: https://download.openmmlab.com/mmdetection/v2.0/regnet/cascade_mask_rcnn_regnetx-3.2GF_fpn_mstrain_3x_coco/cascade_mask_rcnn_regnetx-3_20210715_211616-b9c2c58b.pth + + - Name: cascade_mask_rcnn_regnetx-4GF_fpn_mstrain_3x_coco + In Collection: RegNet + Config: configs/regnet/cascade_mask_rcnn_regnetx-4GF_fpn_mstrain_3x_coco.py + Metadata: + Training Memory (GB): 6.9 + Epochs: 36 + Results: + - Task: Object Detection + Dataset: COCO + Metrics: + box AP: 45.8 + - Task: Instance Segmentation + Dataset: COCO + Metrics: + mask AP: 40.0 + Weights: https://download.openmmlab.com/mmdetection/v2.0/regnet/cascade_mask_rcnn_regnetx-4GF_fpn_mstrain_3x_coco/cascade_mask_rcnn_regnetx-4GF_fpn_mstrain_3x_coco_20210715_212034-cbb1be4c.pth diff --git a/configs/regnet/retinanet_regnetx-1.6GF_fpn_1x_coco.py b/configs/regnet/retinanet_regnetx-1.6GF_fpn_1x_coco.py index 4f2beb8..7395c1b 100644 --- a/configs/regnet/retinanet_regnetx-1.6GF_fpn_1x_coco.py +++ b/configs/regnet/retinanet_regnetx-1.6GF_fpn_1x_coco.py @@ -1,6 +1,5 @@ _base_ = './retinanet_regnetx-3.2GF_fpn_1x_coco.py' model = dict( - pretrained='open-mmlab://regnetx_1.6gf', backbone=dict( type='RegNet', arch='regnetx_1.6gf', @@ -8,7 +7,9 @@ frozen_stages=1, norm_cfg=dict(type='BN', requires_grad=True), norm_eval=True, - style='pytorch'), + style='pytorch', + init_cfg=dict( + type='Pretrained', checkpoint='open-mmlab://regnetx_1.6gf')), neck=dict( type='FPN', in_channels=[72, 168, 408, 912], diff --git a/configs/regnet/retinanet_regnetx-3.2GF_fpn_1x_coco.py b/configs/regnet/retinanet_regnetx-3.2GF_fpn_1x_coco.py index 8f483a1..f05307c 100644 --- a/configs/regnet/retinanet_regnetx-3.2GF_fpn_1x_coco.py +++ b/configs/regnet/retinanet_regnetx-3.2GF_fpn_1x_coco.py @@ -4,7 +4,6 @@ '../_base_/schedules/schedule_1x.py', '../_base_/default_runtime.py' ] model = dict( - pretrained='open-mmlab://regnetx_3.2gf', backbone=dict( _delete_=True, type='RegNet', @@ -13,7 +12,9 @@ frozen_stages=1, norm_cfg=dict(type='BN', requires_grad=True), norm_eval=True, - style='pytorch'), + style='pytorch', + init_cfg=dict( + type='Pretrained', checkpoint='open-mmlab://regnetx_3.2gf')), neck=dict( type='FPN', in_channels=[96, 192, 432, 1008], diff --git a/configs/regnet/retinanet_regnetx-800MF_fpn_1x_coco.py b/configs/regnet/retinanet_regnetx-800MF_fpn_1x_coco.py index fe1d659..f6f8989 100644 --- a/configs/regnet/retinanet_regnetx-800MF_fpn_1x_coco.py +++ b/configs/regnet/retinanet_regnetx-800MF_fpn_1x_coco.py @@ -1,6 +1,5 @@ _base_ = './retinanet_regnetx-3.2GF_fpn_1x_coco.py' model = dict( - pretrained='open-mmlab://regnetx_800mf', backbone=dict( type='RegNet', arch='regnetx_800mf', @@ -8,7 +7,9 @@ frozen_stages=1, norm_cfg=dict(type='BN', requires_grad=True), norm_eval=True, - style='pytorch'), + style='pytorch', + init_cfg=dict( + type='Pretrained', checkpoint='open-mmlab://regnetx_800mf')), neck=dict( type='FPN', in_channels=[64, 128, 288, 672], diff --git a/configs/reppoints/README.md b/configs/reppoints/README.md index 2ab22cd..fc58915 100644 --- a/configs/reppoints/README.md +++ b/configs/reppoints/README.md @@ -7,7 +7,7 @@ We provide code support and configuration files to reproduce the results in the ## Introduction -[ALGORITHM] + **RepPoints**, initially described in [arXiv](https://arxiv.org/abs/1904.11490), is a new representation method for visual objects, on which visual understanding tasks are typically centered. Visual object representation, aiming at both geometric description and appearance feature extraction, is conventionally achieved by `bounding box + RoIPool (RoIAlign)`. The bounding box representation is convenient to use; however, it provides only a rectangular localization of objects that lacks geometric precision and may consequently degrade feature quality. Our new representation, RepPoints, models objects by a `point set` instead of a `bounding box`, which learns to adaptively position themselves over an object in a manner that circumscribes the object’s `spatial extent` and enables `semantically aligned feature extraction`. This richer and more flexible representation maintains the convenience of bounding boxes while facilitating various visual understanding applications. This repo demonstrated the effectiveness of RepPoints for COCO object detection. @@ -36,14 +36,14 @@ The results on COCO 2017val are shown in the table below. | Method | Backbone | GN | Anchor | convert func | Lr schd | Mem (GB) | Inf time (fps) | box AP | Config | Download | |:---------:|:-------------:|:---:|:------:|:------------:|:-------:|:--------:|:--------------:|:------:|:------:|:--------:| -| BBox | R-50-FPN | Y | single | - | 1x | 3.9 | 15.9 | 36.4 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/reppoints/bbox_r50_grid_fpn_gn-neck+head_1x_coco.py) | [model](http://download.openmmlab.com/mmdetection/v2.0/reppoints/bbox_r50_grid_fpn_gn-neck%2Bhead_1x_coco/bbox_r50_grid_fpn_gn-neck%2Bhead_1x_coco_20200329-c98bfa96.pth) | [log](http://download.openmmlab.com/mmdetection/v2.0/reppoints/bbox_r50_grid_fpn_gn-neck%2Bhead_1x_coco/bbox_r50_grid_fpn_gn-neck%2Bhead_1x_coco_20200329_145916.log.json) | -| BBox | R-50-FPN | Y | none | - | 1x | 3.9 | 15.4 | 37.4 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/reppoints/bbox_r50_grid_center_fpn_gn-neck+Bhead_1x_coco.py) | [model](http://download.openmmlab.com/mmdetection/v2.0/reppoints/bbox_r50_grid_center_fpn_gn-neck%2Bhead_1x_coco/bbox_r50_grid_center_fpn_gn-neck%2Bhead_1x_coco_20200330-00f73d58.pth) | [log](http://download.openmmlab.com/mmdetection/v2.0/reppoints/bbox_r50_grid_center_fpn_gn-neck%2Bhead_1x_coco/bbox_r50_grid_center_fpn_gn-neck%2Bhead_1x_coco_20200330_233609.log.json) | -| RepPoints | R-50-FPN | N | none | moment | 1x | 3.3 | 18.5 | 37.0 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/reppoints/reppoints_moment_r50_fpn_1x_coco.py) | [model](http://download.openmmlab.com/mmdetection/v2.0/reppoints/reppoints_moment_r50_fpn_1x_coco/reppoints_moment_r50_fpn_1x_coco_20200330-b73db8d1.pth) | [log](http://download.openmmlab.com/mmdetection/v2.0/reppoints/reppoints_moment_r50_fpn_1x_coco/reppoints_moment_r50_fpn_1x_coco_20200330_233609.log.json) | -| RepPoints | R-50-FPN | Y | none | moment | 1x | 3.9 | 17.5 | 38.1 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/reppoints/reppoints_moment_r50_fpn_gn-neck%2Bhead_1x_coco.py) | [model](http://download.openmmlab.com/mmdetection/v2.0/reppoints/reppoints_moment_r50_fpn_gn-neck%2Bhead_1x_coco/reppoints_moment_r50_fpn_gn-neck%2Bhead_1x_coco_20200329-4b38409a.pth) | [log](http://download.openmmlab.com/mmdetection/v2.0/reppoints/reppoints_moment_r50_fpn_gn-neck%2Bhead_1x_coco/reppoints_moment_r50_fpn_gn-neck%2Bhead_1x_coco_20200329_145952.log.json) | -| RepPoints | R-50-FPN | Y | none | moment | 2x | 3.9 | - | 38.6 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/reppoints/reppoints_moment_r50_fpn_gn-neck+head_2x_coco.py) | [model](http://download.openmmlab.com/mmdetection/v2.0/reppoints/reppoints_moment_r50_fpn_gn-neck%2Bhead_2x_coco/reppoints_moment_r50_fpn_gn-neck%2Bhead_2x_coco_20200329-91babaa2.pth) | [log](http://download.openmmlab.com/mmdetection/v2.0/reppoints/reppoints_moment_r50_fpn_gn-neck%2Bhead_2x_coco/reppoints_moment_r50_fpn_gn-neck%2Bhead_2x_coco_20200329_150020.log.json) | -| RepPoints | R-101-FPN | Y | none | moment | 2x | 5.8 | 13.7 | 40.5 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/reppoints/reppoints_moment_r101_fpn_gn-neck+head_2x_coco.py) | [model](http://download.openmmlab.com/mmdetection/v2.0/reppoints/reppoints_moment_r101_fpn_gn-neck%2Bhead_2x_coco/reppoints_moment_r101_fpn_gn-neck%2Bhead_2x_coco_20200329-4fbc7310.pth) | [log](http://download.openmmlab.com/mmdetection/v2.0/reppoints/reppoints_moment_r101_fpn_gn-neck%2Bhead_2x_coco/reppoints_moment_r101_fpn_gn-neck%2Bhead_2x_coco_20200329_132205.log.json) | -| RepPoints | R-101-FPN-DCN | Y | none | moment | 2x | 5.9 | 12.1 | 42.9 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/reppoints/reppoints_moment_r101_fpn_dconv_c3-c5_gn-neck+head_2x_coco.py) | [model](http://download.openmmlab.com/mmdetection/v2.0/reppoints/reppoints_moment_r101_fpn_dconv_c3-c5_gn-neck%2Bhead_2x_coco/reppoints_moment_r101_fpn_dconv_c3-c5_gn-neck%2Bhead_2x_coco_20200329-3309fbf2.pth) | [log](http://download.openmmlab.com/mmdetection/v2.0/reppoints/reppoints_moment_r101_fpn_dconv_c3-c5_gn-neck%2Bhead_2x_coco/reppoints_moment_r101_fpn_dconv_c3-c5_gn-neck%2Bhead_2x_coco_20200329_132134.log.json) | -| RepPoints | X-101-FPN-DCN | Y | none | moment | 2x | 7.1 | 9.3 | 44.2 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/reppoints/reppoints_moment_x101_fpn_dconv_c3-c5_gn-neck+head_2x_coco.py) | [model](http://download.openmmlab.com/mmdetection/v2.0/reppoints/reppoints_moment_x101_fpn_dconv_c3-c5_gn-neck%2Bhead_2x_coco/reppoints_moment_x101_fpn_dconv_c3-c5_gn-neck%2Bhead_2x_coco_20200329-f87da1ea.pth) | [log](http://download.openmmlab.com/mmdetection/v2.0/reppoints/reppoints_moment_x101_fpn_dconv_c3-c5_gn-neck%2Bhead_2x_coco/reppoints_moment_x101_fpn_dconv_c3-c5_gn-neck%2Bhead_2x_coco_20200329_132201.log.json) | +| BBox | R-50-FPN | Y | single | - | 1x | 3.9 | 15.9 | 36.4 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/reppoints/bbox_r50_grid_fpn_gn-neck+head_1x_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/reppoints/bbox_r50_grid_fpn_gn-neck%2Bhead_1x_coco/bbox_r50_grid_fpn_gn-neck%2Bhead_1x_coco_20200329-c98bfa96.pth) | [log](https://download.openmmlab.com/mmdetection/v2.0/reppoints/bbox_r50_grid_fpn_gn-neck%2Bhead_1x_coco/bbox_r50_grid_fpn_gn-neck%2Bhead_1x_coco_20200329_145916.log.json) | +| BBox | R-50-FPN | Y | none | - | 1x | 3.9 | 15.4 | 37.4 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/reppoints/bbox_r50_grid_center_fpn_gn-neck+Bhead_1x_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/reppoints/bbox_r50_grid_center_fpn_gn-neck%2Bhead_1x_coco/bbox_r50_grid_center_fpn_gn-neck%2Bhead_1x_coco_20200330-00f73d58.pth) | [log](https://download.openmmlab.com/mmdetection/v2.0/reppoints/bbox_r50_grid_center_fpn_gn-neck%2Bhead_1x_coco/bbox_r50_grid_center_fpn_gn-neck%2Bhead_1x_coco_20200330_233609.log.json) | +| RepPoints | R-50-FPN | N | none | moment | 1x | 3.3 | 18.5 | 37.0 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/reppoints/reppoints_moment_r50_fpn_1x_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/reppoints/reppoints_moment_r50_fpn_1x_coco/reppoints_moment_r50_fpn_1x_coco_20200330-b73db8d1.pth) | [log](https://download.openmmlab.com/mmdetection/v2.0/reppoints/reppoints_moment_r50_fpn_1x_coco/reppoints_moment_r50_fpn_1x_coco_20200330_233609.log.json) | +| RepPoints | R-50-FPN | Y | none | moment | 1x | 3.9 | 17.5 | 38.1 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/reppoints/reppoints_moment_r50_fpn_gn-neck%2Bhead_1x_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/reppoints/reppoints_moment_r50_fpn_gn-neck%2Bhead_1x_coco/reppoints_moment_r50_fpn_gn-neck%2Bhead_1x_coco_20200329-4b38409a.pth) | [log](https://download.openmmlab.com/mmdetection/v2.0/reppoints/reppoints_moment_r50_fpn_gn-neck%2Bhead_1x_coco/reppoints_moment_r50_fpn_gn-neck%2Bhead_1x_coco_20200329_145952.log.json) | +| RepPoints | R-50-FPN | Y | none | moment | 2x | 3.9 | - | 38.6 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/reppoints/reppoints_moment_r50_fpn_gn-neck+head_2x_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/reppoints/reppoints_moment_r50_fpn_gn-neck%2Bhead_2x_coco/reppoints_moment_r50_fpn_gn-neck%2Bhead_2x_coco_20200329-91babaa2.pth) | [log](https://download.openmmlab.com/mmdetection/v2.0/reppoints/reppoints_moment_r50_fpn_gn-neck%2Bhead_2x_coco/reppoints_moment_r50_fpn_gn-neck%2Bhead_2x_coco_20200329_150020.log.json) | +| RepPoints | R-101-FPN | Y | none | moment | 2x | 5.8 | 13.7 | 40.5 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/reppoints/reppoints_moment_r101_fpn_gn-neck+head_2x_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/reppoints/reppoints_moment_r101_fpn_gn-neck%2Bhead_2x_coco/reppoints_moment_r101_fpn_gn-neck%2Bhead_2x_coco_20200329-4fbc7310.pth) | [log](https://download.openmmlab.com/mmdetection/v2.0/reppoints/reppoints_moment_r101_fpn_gn-neck%2Bhead_2x_coco/reppoints_moment_r101_fpn_gn-neck%2Bhead_2x_coco_20200329_132205.log.json) | +| RepPoints | R-101-FPN-DCN | Y | none | moment | 2x | 5.9 | 12.1 | 42.9 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/reppoints/reppoints_moment_r101_fpn_dconv_c3-c5_gn-neck+head_2x_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/reppoints/reppoints_moment_r101_fpn_dconv_c3-c5_gn-neck%2Bhead_2x_coco/reppoints_moment_r101_fpn_dconv_c3-c5_gn-neck%2Bhead_2x_coco_20200329-3309fbf2.pth) | [log](https://download.openmmlab.com/mmdetection/v2.0/reppoints/reppoints_moment_r101_fpn_dconv_c3-c5_gn-neck%2Bhead_2x_coco/reppoints_moment_r101_fpn_dconv_c3-c5_gn-neck%2Bhead_2x_coco_20200329_132134.log.json) | +| RepPoints | X-101-FPN-DCN | Y | none | moment | 2x | 7.1 | 9.3 | 44.2 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/reppoints/reppoints_moment_x101_fpn_dconv_c3-c5_gn-neck+head_2x_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/reppoints/reppoints_moment_x101_fpn_dconv_c3-c5_gn-neck%2Bhead_2x_coco/reppoints_moment_x101_fpn_dconv_c3-c5_gn-neck%2Bhead_2x_coco_20200329-f87da1ea.pth) | [log](https://download.openmmlab.com/mmdetection/v2.0/reppoints/reppoints_moment_x101_fpn_dconv_c3-c5_gn-neck%2Bhead_2x_coco/reppoints_moment_x101_fpn_dconv_c3-c5_gn-neck%2Bhead_2x_coco_20200329_132201.log.json) | **Notes:** diff --git a/configs/reppoints/metafile.yml b/configs/reppoints/metafile.yml new file mode 100644 index 0000000..cd4312c --- /dev/null +++ b/configs/reppoints/metafile.yml @@ -0,0 +1,181 @@ +Collections: + - Name: RepPoints + Metadata: + Training Data: COCO + Training Techniques: + - SGD with Momentum + - Weight Decay + Training Resources: 8x V100 GPUs + Architecture: + - Group Normalization + - FPN + - RepPoints + - ResNet + Paper: + URL: https://arxiv.org/abs/1904.11490 + Title: 'RepPoints: Point Set Representation for Object Detection' + README: configs/reppoints/README.md + Code: + URL: https://github.com/open-mmlab/mmdetection/blob/v2.0.0/mmdet/models/detectors/reppoints_detector.py#L9 + Version: v2.0.0 + +Models: + - Name: bbox_r50_grid_fpn_gn-neck+head_1x_coco + In Collection: RepPoints + Config: configs/reppoints/bbox_r50_grid_fpn_gn-neck+head_1x_coco.py + Metadata: + Training Memory (GB): 3.9 + inference time (ms/im): + - value: 62.89 + hardware: V100 + backend: PyTorch + batch size: 1 + mode: FP32 + resolution: (800, 1333) + Epochs: 12 + Results: + - Task: Object Detection + Dataset: COCO + Metrics: + box AP: 36.4 + Weights: https://download.openmmlab.com/mmdetection/v2.0/reppoints/bbox_r50_grid_fpn_gn-neck%2Bhead_1x_coco/bbox_r50_grid_fpn_gn-neck%2Bhead_1x_coco_20200329-c98bfa96.pth + + - Name: bbox_r50_grid_center_fpn_gn-neck+Bhead_1x_coco + In Collection: RepPoints + Config: configs/reppoints/bbox_r50_grid_center_fpn_gn-neck+Bhead_1x_coco.py + Metadata: + Training Memory (GB): 3.9 + inference time (ms/im): + - value: 64.94 + hardware: V100 + backend: PyTorch + batch size: 1 + mode: FP32 + resolution: (800, 1333) + Epochs: 12 + Results: + - Task: Object Detection + Dataset: COCO + Metrics: + box AP: 37.4 + Weights: https://download.openmmlab.com/mmdetection/v2.0/reppoints/bbox_r50_grid_center_fpn_gn-neck%2Bhead_1x_coco/bbox_r50_grid_center_fpn_gn-neck%2Bhead_1x_coco_20200330-00f73d58.pth + + - Name: reppoints_moment_r50_fpn_1x_coco + In Collection: RepPoints + Config: configs/reppoints/reppoints_moment_r50_fpn_1x_coco.py + Metadata: + Training Memory (GB): 3.3 + inference time (ms/im): + - value: 54.05 + hardware: V100 + backend: PyTorch + batch size: 1 + mode: FP32 + resolution: (800, 1333) + Epochs: 12 + Results: + - Task: Object Detection + Dataset: COCO + Metrics: + box AP: 37.0 + Weights: https://download.openmmlab.com/mmdetection/v2.0/reppoints/reppoints_moment_r50_fpn_1x_coco/reppoints_moment_r50_fpn_1x_coco_20200330-b73db8d1.pth + + - Name: reppoints_moment_r50_fpn_gn-neck%2Bhead_1x_coco + In Collection: RepPoints + Config: configs/reppoints/reppoints_moment_r50_fpn_gn-neck%2Bhead_1x_coco.py + Metadata: + Training Memory (GB): 3.9 + inference time (ms/im): + - value: 57.14 + hardware: V100 + backend: PyTorch + batch size: 1 + mode: FP32 + resolution: (800, 1333) + Epochs: 12 + Results: + - Task: Object Detection + Dataset: COCO + Metrics: + box AP: 38.1 + Weights: https://download.openmmlab.com/mmdetection/v2.0/reppoints/reppoints_moment_r50_fpn_gn-neck%2Bhead_1x_coco/reppoints_moment_r50_fpn_gn-neck%2Bhead_1x_coco_20200329-4b38409a.pth + + - Name: reppoints_moment_r50_fpn_gn-neck+head_2x_coco + In Collection: RepPoints + Config: configs/reppoints/reppoints_moment_r50_fpn_gn-neck+head_2x_coco.py + Metadata: + Training Memory (GB): 3.9 + inference time (ms/im): + - value: 57.14 + hardware: V100 + backend: PyTorch + batch size: 1 + mode: FP32 + resolution: (800, 1333) + Epochs: 24 + Results: + - Task: Object Detection + Dataset: COCO + Metrics: + box AP: 38.6 + Weights: https://download.openmmlab.com/mmdetection/v2.0/reppoints/reppoints_moment_r50_fpn_gn-neck%2Bhead_2x_coco/reppoints_moment_r50_fpn_gn-neck%2Bhead_2x_coco_20200329-91babaa2.pth + + - Name: reppoints_moment_r101_fpn_gn-neck+head_2x_coco + In Collection: RepPoints + Config: configs/reppoints/reppoints_moment_r101_fpn_gn-neck+head_2x_coco.py + Metadata: + Training Memory (GB): 5.8 + inference time (ms/im): + - value: 72.99 + hardware: V100 + backend: PyTorch + batch size: 1 + mode: FP32 + resolution: (800, 1333) + Epochs: 24 + Results: + - Task: Object Detection + Dataset: COCO + Metrics: + box AP: 40.5 + Weights: https://download.openmmlab.com/mmdetection/v2.0/reppoints/reppoints_moment_r101_fpn_gn-neck%2Bhead_2x_coco/reppoints_moment_r101_fpn_gn-neck%2Bhead_2x_coco_20200329-4fbc7310.pth + + - Name: reppoints_moment_r101_fpn_dconv_c3-c5_gn-neck+head_2x_coco + In Collection: RepPoints + Config: configs/reppoints/reppoints_moment_r101_fpn_dconv_c3-c5_gn-neck+head_2x_coco.py + Metadata: + Training Memory (GB): 5.9 + inference time (ms/im): + - value: 82.64 + hardware: V100 + backend: PyTorch + batch size: 1 + mode: FP32 + resolution: (800, 1333) + Epochs: 24 + Results: + - Task: Object Detection + Dataset: COCO + Metrics: + box AP: 42.9 + Weights: https://download.openmmlab.com/mmdetection/v2.0/reppoints/reppoints_moment_r101_fpn_dconv_c3-c5_gn-neck%2Bhead_2x_coco/reppoints_moment_r101_fpn_dconv_c3-c5_gn-neck%2Bhead_2x_coco_20200329-3309fbf2.pth + + - Name: reppoints_moment_x101_fpn_dconv_c3-c5_gn-neck+head_2x_coco + In Collection: RepPoints + Config: configs/reppoints/reppoints_moment_x101_fpn_dconv_c3-c5_gn-neck+head_2x_coco.py + Metadata: + Training Memory (GB): 7.1 + inference time (ms/im): + - value: 107.53 + hardware: V100 + backend: PyTorch + batch size: 1 + mode: FP32 + resolution: (800, 1333) + Epochs: 24 + Results: + - Task: Object Detection + Dataset: COCO + Metrics: + box AP: 44.2 + Weights: https://download.openmmlab.com/mmdetection/v2.0/reppoints/reppoints_moment_x101_fpn_dconv_c3-c5_gn-neck%2Bhead_2x_coco/reppoints_moment_x101_fpn_dconv_c3-c5_gn-neck%2Bhead_2x_coco_20200329-f87da1ea.pth diff --git a/configs/reppoints/reppoints_moment_r101_fpn_dconv_c3-c5_gn-neck+head_2x_coco.py b/configs/reppoints/reppoints_moment_r101_fpn_dconv_c3-c5_gn-neck+head_2x_coco.py index 241754c..e223d80 100644 --- a/configs/reppoints/reppoints_moment_r101_fpn_dconv_c3-c5_gn-neck+head_2x_coco.py +++ b/configs/reppoints/reppoints_moment_r101_fpn_dconv_c3-c5_gn-neck+head_2x_coco.py @@ -1,7 +1,8 @@ _base_ = './reppoints_moment_r50_fpn_gn-neck+head_2x_coco.py' model = dict( - pretrained='torchvision://resnet101', backbone=dict( depth=101, dcn=dict(type='DCN', deform_groups=1, fallback_on_stride=False), - stage_with_dcn=(False, True, True, True))) + stage_with_dcn=(False, True, True, True), + init_cfg=dict(type='Pretrained', + checkpoint='torchvision://resnet101'))) diff --git a/configs/reppoints/reppoints_moment_r101_fpn_gn-neck+head_2x_coco.py b/configs/reppoints/reppoints_moment_r101_fpn_gn-neck+head_2x_coco.py index 19efa0d..1185470 100644 --- a/configs/reppoints/reppoints_moment_r101_fpn_gn-neck+head_2x_coco.py +++ b/configs/reppoints/reppoints_moment_r101_fpn_gn-neck+head_2x_coco.py @@ -1,2 +1,6 @@ _base_ = './reppoints_moment_r50_fpn_gn-neck+head_2x_coco.py' -model = dict(pretrained='torchvision://resnet101', backbone=dict(depth=101)) +model = dict( + backbone=dict( + depth=101, + init_cfg=dict(type='Pretrained', + checkpoint='torchvision://resnet101'))) diff --git a/configs/reppoints/reppoints_moment_r50_fpn_1x_coco.py b/configs/reppoints/reppoints_moment_r50_fpn_1x_coco.py index 8df2a8f..158a906 100644 --- a/configs/reppoints/reppoints_moment_r50_fpn_1x_coco.py +++ b/configs/reppoints/reppoints_moment_r50_fpn_1x_coco.py @@ -4,7 +4,6 @@ ] model = dict( type='RepPointsDetector', - pretrained='torchvision://resnet50', backbone=dict( type='ResNet', depth=50, @@ -13,7 +12,8 @@ frozen_stages=1, norm_cfg=dict(type='BN', requires_grad=True), norm_eval=True, - style='pytorch'), + style='pytorch', + init_cfg=dict(type='Pretrained', checkpoint='torchvision://resnet50')), neck=dict( type='FPN', in_channels=[256, 512, 1024, 2048], diff --git a/configs/reppoints/reppoints_moment_x101_fpn_dconv_c3-c5_gn-neck+head_2x_coco.py b/configs/reppoints/reppoints_moment_x101_fpn_dconv_c3-c5_gn-neck+head_2x_coco.py index c33019d..c0a12d0 100644 --- a/configs/reppoints/reppoints_moment_x101_fpn_dconv_c3-c5_gn-neck+head_2x_coco.py +++ b/configs/reppoints/reppoints_moment_x101_fpn_dconv_c3-c5_gn-neck+head_2x_coco.py @@ -1,6 +1,5 @@ _base_ = './reppoints_moment_r50_fpn_gn-neck+head_2x_coco.py' model = dict( - pretrained='open-mmlab://resnext101_32x4d', backbone=dict( type='ResNeXt', depth=101, @@ -12,4 +11,6 @@ norm_cfg=dict(type='BN', requires_grad=True), style='pytorch', dcn=dict(type='DCN', deform_groups=1, fallback_on_stride=False), - stage_with_dcn=(False, True, True, True))) + stage_with_dcn=(False, True, True, True), + init_cfg=dict( + type='Pretrained', checkpoint='open-mmlab://resnext101_32x4d'))) diff --git a/configs/res2net/README.md b/configs/res2net/README.md index ca9f934..864a891 100644 --- a/configs/res2net/README.md +++ b/configs/res2net/README.md @@ -2,7 +2,7 @@ ## Introduction -[ALGORITHM] + We propose a novel building block for CNNs, namely Res2Net, by constructing hierarchical residual-like connections within one single residual block. The Res2Net represents multi-scale features at a granular level and increases the range of receptive fields for each network layer. @@ -35,31 +35,31 @@ Compared with other backbone networks, Res2Net requires fewer parameters and FLO | Backbone | Style | Lr schd | Mem (GB) | Inf time (fps) | box AP | Config | Download | | :-------------: | :-----: | :-----: | :------: | :------------: | :----: | :------: | :--------: | -|R2-101-FPN | pytorch | 2x | 7.4 | - | 43.0 |[config](https://github.com/open-mmlab/mmdetection/tree/master/configs/res2net/faster_rcnn_r2_101_fpn_2x_coco.py) | [model](http://download.openmmlab.com/mmdetection/v2.0/res2net/faster_rcnn_r2_101_fpn_2x_coco/faster_rcnn_r2_101_fpn_2x_coco-175f1da6.pth) | [log](http://download.openmmlab.com/mmdetection/v2.0/res2net/faster_rcnn_r2_101_fpn_2x_coco/faster_rcnn_r2_101_fpn_2x_coco_20200514_231734.log.json) | +|R2-101-FPN | pytorch | 2x | 7.4 | - | 43.0 |[config](https://github.com/open-mmlab/mmdetection/tree/master/configs/res2net/faster_rcnn_r2_101_fpn_2x_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/res2net/faster_rcnn_r2_101_fpn_2x_coco/faster_rcnn_r2_101_fpn_2x_coco-175f1da6.pth) | [log](https://download.openmmlab.com/mmdetection/v2.0/res2net/faster_rcnn_r2_101_fpn_2x_coco/faster_rcnn_r2_101_fpn_2x_coco_20200514_231734.log.json) | ### Mask R-CNN | Backbone | Style | Lr schd | Mem (GB) | Inf time (fps) | box AP | mask AP | Config | Download | | :-------------: | :-----: | :-----: | :------: | :------------: | :----: | :-----: | :------: | :--------: | -|R2-101-FPN | pytorch | 2x | 7.9 | - | 43.6 | 38.7 |[config](https://github.com/open-mmlab/mmdetection/tree/master/configs/res2net/mask_rcnn_r2_101_fpn_2x_coco.py) | [model](http://download.openmmlab.com/mmdetection/v2.0/res2net/mask_rcnn_r2_101_fpn_2x_coco/mask_rcnn_r2_101_fpn_2x_coco-17f061e8.pth) | [log](http://download.openmmlab.com/mmdetection/v2.0/res2net/mask_rcnn_r2_101_fpn_2x_coco/mask_rcnn_r2_101_fpn_2x_coco_20200515_002413.log.json) | +|R2-101-FPN | pytorch | 2x | 7.9 | - | 43.6 | 38.7 |[config](https://github.com/open-mmlab/mmdetection/tree/master/configs/res2net/mask_rcnn_r2_101_fpn_2x_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/res2net/mask_rcnn_r2_101_fpn_2x_coco/mask_rcnn_r2_101_fpn_2x_coco-17f061e8.pth) | [log](https://download.openmmlab.com/mmdetection/v2.0/res2net/mask_rcnn_r2_101_fpn_2x_coco/mask_rcnn_r2_101_fpn_2x_coco_20200515_002413.log.json) | ### Cascade R-CNN | Backbone | Style | Lr schd | Mem (GB) | Inf time (fps) | box AP | Config | Download | | :-------------: | :-----: | :-----: | :------: | :------------: | :----: | :------: | :--------: | -|R2-101-FPN | pytorch | 20e | 7.8 | - | 45.7 |[config](https://github.com/open-mmlab/mmdetection/tree/master/configs/res2net/cascade_rcnn_r2_101_fpn_20e_coco.py) | [model](http://download.openmmlab.com/mmdetection/v2.0/res2net/cascade_rcnn_r2_101_fpn_20e_coco/cascade_rcnn_r2_101_fpn_20e_coco-f4b7b7db.pth) | [log](http://download.openmmlab.com/mmdetection/v2.0/res2net/cascade_rcnn_r2_101_fpn_20e_coco/cascade_rcnn_r2_101_fpn_20e_coco_20200515_091644.log.json) | +|R2-101-FPN | pytorch | 20e | 7.8 | - | 45.7 |[config](https://github.com/open-mmlab/mmdetection/tree/master/configs/res2net/cascade_rcnn_r2_101_fpn_20e_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/res2net/cascade_rcnn_r2_101_fpn_20e_coco/cascade_rcnn_r2_101_fpn_20e_coco-f4b7b7db.pth) | [log](https://download.openmmlab.com/mmdetection/v2.0/res2net/cascade_rcnn_r2_101_fpn_20e_coco/cascade_rcnn_r2_101_fpn_20e_coco_20200515_091644.log.json) | ### Cascade Mask R-CNN | Backbone | Style | Lr schd | Mem (GB) | Inf time (fps) | box AP | mask AP | Config | Download | | :-------------: | :-----: | :-----: | :------: | :------------: | :----: | :-----: | :------: | :--------: | -R2-101-FPN | pytorch | 20e | 9.5 | - | 46.4 | 40.0 |[config](https://github.com/open-mmlab/mmdetection/tree/master/configs/res2net/cascade_mask_rcnn_r2_101_fpn_20e_coco.py) | [model](http://download.openmmlab.com/mmdetection/v2.0/res2net/cascade_mask_rcnn_r2_101_fpn_20e_coco/cascade_mask_rcnn_r2_101_fpn_20e_coco-8a7b41e1.pth) | [log](http://download.openmmlab.com/mmdetection/v2.0/res2net/cascade_mask_rcnn_r2_101_fpn_20e_coco/cascade_mask_rcnn_r2_101_fpn_20e_coco_20200515_091645.log.json) | +R2-101-FPN | pytorch | 20e | 9.5 | - | 46.4 | 40.0 |[config](https://github.com/open-mmlab/mmdetection/tree/master/configs/res2net/cascade_mask_rcnn_r2_101_fpn_20e_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/res2net/cascade_mask_rcnn_r2_101_fpn_20e_coco/cascade_mask_rcnn_r2_101_fpn_20e_coco-8a7b41e1.pth) | [log](https://download.openmmlab.com/mmdetection/v2.0/res2net/cascade_mask_rcnn_r2_101_fpn_20e_coco/cascade_mask_rcnn_r2_101_fpn_20e_coco_20200515_091645.log.json) | ### Hybrid Task Cascade (HTC) | Backbone | Style | Lr schd | Mem (GB) | Inf time (fps) | box AP | mask AP | Config | Download | | :-------------: | :-----: | :-----: | :------: | :------------: | :----: | :-----: | :------: | :--------: | -| R2-101-FPN | pytorch | 20e | - | - | 47.5 | 41.6 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/res2net/htc_r2_101_fpn_20e_coco.py) | [model](http://download.openmmlab.com/mmdetection/v2.0/res2net/htc_r2_101_fpn_20e_coco/htc_r2_101_fpn_20e_coco-3a8d2112.pth) | [log](http://download.openmmlab.com/mmdetection/v2.0/res2net/htc_r2_101_fpn_20e_coco/htc_r2_101_fpn_20e_coco_20200515_150029.log.json) | +| R2-101-FPN | pytorch | 20e | - | - | 47.5 | 41.6 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/res2net/htc_r2_101_fpn_20e_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/res2net/htc_r2_101_fpn_20e_coco/htc_r2_101_fpn_20e_coco-3a8d2112.pth) | [log](https://download.openmmlab.com/mmdetection/v2.0/res2net/htc_r2_101_fpn_20e_coco/htc_r2_101_fpn_20e_coco_20200515_150029.log.json) | - Res2Net ImageNet pretrained models are in [Res2Net-PretrainedModels](https://github.com/Res2Net/Res2Net-PretrainedModels). - More applications of Res2Net are in [Res2Net-Github](https://github.com/Res2Net/). diff --git a/configs/res2net/cascade_mask_rcnn_r2_101_fpn_20e_coco.py b/configs/res2net/cascade_mask_rcnn_r2_101_fpn_20e_coco.py index 50df4e2..6b6c001 100644 --- a/configs/res2net/cascade_mask_rcnn_r2_101_fpn_20e_coco.py +++ b/configs/res2net/cascade_mask_rcnn_r2_101_fpn_20e_coco.py @@ -1,4 +1,10 @@ _base_ = '../cascade_rcnn/cascade_mask_rcnn_r50_fpn_20e_coco.py' model = dict( - pretrained='open-mmlab://res2net101_v1d_26w_4s', - backbone=dict(type='Res2Net', depth=101, scales=4, base_width=26)) + backbone=dict( + type='Res2Net', + depth=101, + scales=4, + base_width=26, + init_cfg=dict( + type='Pretrained', + checkpoint='open-mmlab://res2net101_v1d_26w_4s'))) diff --git a/configs/res2net/cascade_rcnn_r2_101_fpn_20e_coco.py b/configs/res2net/cascade_rcnn_r2_101_fpn_20e_coco.py index 1cac759..10dddbb 100644 --- a/configs/res2net/cascade_rcnn_r2_101_fpn_20e_coco.py +++ b/configs/res2net/cascade_rcnn_r2_101_fpn_20e_coco.py @@ -1,4 +1,10 @@ _base_ = '../cascade_rcnn/cascade_rcnn_r50_fpn_20e_coco.py' model = dict( - pretrained='open-mmlab://res2net101_v1d_26w_4s', - backbone=dict(type='Res2Net', depth=101, scales=4, base_width=26)) + backbone=dict( + type='Res2Net', + depth=101, + scales=4, + base_width=26, + init_cfg=dict( + type='Pretrained', + checkpoint='open-mmlab://res2net101_v1d_26w_4s'))) diff --git a/configs/res2net/faster_rcnn_r2_101_fpn_2x_coco.py b/configs/res2net/faster_rcnn_r2_101_fpn_2x_coco.py index 85004e0..fc2221c 100644 --- a/configs/res2net/faster_rcnn_r2_101_fpn_2x_coco.py +++ b/configs/res2net/faster_rcnn_r2_101_fpn_2x_coco.py @@ -1,4 +1,10 @@ _base_ = '../faster_rcnn/faster_rcnn_r50_fpn_2x_coco.py' model = dict( - pretrained='open-mmlab://res2net101_v1d_26w_4s', - backbone=dict(type='Res2Net', depth=101, scales=4, base_width=26)) + backbone=dict( + type='Res2Net', + depth=101, + scales=4, + base_width=26, + init_cfg=dict( + type='Pretrained', + checkpoint='open-mmlab://res2net101_v1d_26w_4s'))) diff --git a/configs/res2net/htc_r2_101_fpn_20e_coco.py b/configs/res2net/htc_r2_101_fpn_20e_coco.py index 3c4cc75..22d0c5d 100644 --- a/configs/res2net/htc_r2_101_fpn_20e_coco.py +++ b/configs/res2net/htc_r2_101_fpn_20e_coco.py @@ -1,7 +1,13 @@ _base_ = '../htc/htc_r50_fpn_1x_coco.py' model = dict( - pretrained='open-mmlab://res2net101_v1d_26w_4s', - backbone=dict(type='Res2Net', depth=101, scales=4, base_width=26)) + backbone=dict( + type='Res2Net', + depth=101, + scales=4, + base_width=26, + init_cfg=dict( + type='Pretrained', + checkpoint='open-mmlab://res2net101_v1d_26w_4s'))) # learning policy lr_config = dict(step=[16, 19]) runner = dict(type='EpochBasedRunner', max_epochs=20) diff --git a/configs/res2net/mask_rcnn_r2_101_fpn_2x_coco.py b/configs/res2net/mask_rcnn_r2_101_fpn_2x_coco.py index a620188..33aef1a 100644 --- a/configs/res2net/mask_rcnn_r2_101_fpn_2x_coco.py +++ b/configs/res2net/mask_rcnn_r2_101_fpn_2x_coco.py @@ -1,4 +1,10 @@ _base_ = '../mask_rcnn/mask_rcnn_r50_fpn_2x_coco.py' model = dict( - pretrained='open-mmlab://res2net101_v1d_26w_4s', - backbone=dict(type='Res2Net', depth=101, scales=4, base_width=26)) + backbone=dict( + type='Res2Net', + depth=101, + scales=4, + base_width=26, + init_cfg=dict( + type='Pretrained', + checkpoint='open-mmlab://res2net101_v1d_26w_4s'))) diff --git a/configs/res2net/metafile.yml b/configs/res2net/metafile.yml new file mode 100644 index 0000000..71809f3 --- /dev/null +++ b/configs/res2net/metafile.yml @@ -0,0 +1,94 @@ +Collections: + - Name: Res2Net + Metadata: + Training Data: COCO + Training Techniques: + - SGD with Momentum + - Weight Decay + Training Resources: 8x V100 GPUs + Architecture: + - Res2Net + Paper: + URL: https://arxiv.org/abs/1904.01169 + Title: 'Res2Net for object detection and instance segmentation' + README: configs/res2net/README.md + Code: + URL: https://github.com/open-mmlab/mmdetection/blob/v2.1.0/mmdet/models/backbones/res2net.py#L239 + Version: v2.1.0 + +Models: + - Name: faster_rcnn_r2_101_fpn_2x_coco + In Collection: Res2Net + Config: configs/res2net/faster_rcnn_r2_101_fpn_2x_coco.py + Metadata: + Training Memory (GB): 7.4 + Epochs: 24 + Results: + - Task: Object Detection + Dataset: COCO + Metrics: + box AP: 43.0 + Weights: https://download.openmmlab.com/mmdetection/v2.0/res2net/faster_rcnn_r2_101_fpn_2x_coco/faster_rcnn_r2_101_fpn_2x_coco-175f1da6.pth + + - Name: mask_rcnn_r2_101_fpn_2x_coco + In Collection: Res2Net + Config: configs/res2net/mask_rcnn_r2_101_fpn_2x_coco.py + Metadata: + Training Memory (GB): 7.9 + Epochs: 24 + Results: + - Task: Object Detection + Dataset: COCO + Metrics: + box AP: 43.6 + - Task: Instance Segmentation + Dataset: COCO + Metrics: + mask AP: 38.7 + Weights: https://download.openmmlab.com/mmdetection/v2.0/res2net/mask_rcnn_r2_101_fpn_2x_coco/mask_rcnn_r2_101_fpn_2x_coco-17f061e8.pth + + - Name: cascade_rcnn_r2_101_fpn_20e_coco + In Collection: Res2Net + Config: configs/res2net/cascade_rcnn_r2_101_fpn_20e_coco.py + Metadata: + Training Memory (GB): 7.8 + Epochs: 20 + Results: + - Task: Object Detection + Dataset: COCO + Metrics: + box AP: 45.7 + Weights: https://download.openmmlab.com/mmdetection/v2.0/res2net/cascade_rcnn_r2_101_fpn_20e_coco/cascade_rcnn_r2_101_fpn_20e_coco-f4b7b7db.pth + + - Name: cascade_mask_rcnn_r2_101_fpn_20e_coco + In Collection: Res2Net + Config: configs/res2net/cascade_mask_rcnn_r2_101_fpn_20e_coco.py + Metadata: + Training Memory (GB): 9.5 + Epochs: 20 + Results: + - Task: Object Detection + Dataset: COCO + Metrics: + box AP: 46.4 + - Task: Instance Segmentation + Dataset: COCO + Metrics: + mask AP: 40.0 + Weights: https://download.openmmlab.com/mmdetection/v2.0/res2net/cascade_mask_rcnn_r2_101_fpn_20e_coco/cascade_mask_rcnn_r2_101_fpn_20e_coco-8a7b41e1.pth + + - Name: htc_r2_101_fpn_20e_coco + In Collection: Res2Net + Config: configs/res2net/htc_r2_101_fpn_20e_coco.py + Metadata: + Epochs: 20 + Results: + - Task: Object Detection + Dataset: COCO + Metrics: + box AP: 47.5 + - Task: Instance Segmentation + Dataset: COCO + Metrics: + mask AP: 41.6 + Weights: https://download.openmmlab.com/mmdetection/v2.0/res2net/htc_r2_101_fpn_20e_coco/htc_r2_101_fpn_20e_coco-3a8d2112.pth diff --git a/configs/resnest/README.md b/configs/resnest/README.md index d34d1c2..a23a39a 100644 --- a/configs/resnest/README.md +++ b/configs/resnest/README.md @@ -19,26 +19,26 @@ year={2020} | Backbone | Style | Lr schd | Mem (GB) | Inf time (fps) | box AP | Config | Download | | :-------------: | :-----: | :-----: | :------: | :------------: | :----: | :------: | :--------: | -|S-50-FPN | pytorch | 1x | 4.8 | - | 42.0 |[config](https://github.com/open-mmlab/mmdetection/tree/master/configs/resnest/faster_rcnn_s50_fpn_syncbn-backbone+head_mstrain-range_1x_coco.py) | [model](http://download.openmmlab.com/mmdetection/v2.0/resnest/faster_rcnn_s50_fpn_syncbn-backbone%2Bhead_mstrain-range_1x_coco/faster_rcnn_s50_fpn_syncbn-backbone%2Bhead_mstrain-range_1x_coco_20200926_125502-20289c16.pth) | [log](http://download.openmmlab.com/mmdetection/v2.0/resnest/faster_rcnn_s50_fpn_syncbn-backbone%2Bhead_mstrain-range_1x_coco/faster_rcnn_s50_fpn_syncbn-backbone%2Bhead_mstrain-range_1x_coco-20200926_125502.log.json) | -|S-101-FPN | pytorch | 1x | 7.1 | - | 44.5 |[config](https://github.com/open-mmlab/mmdetection/tree/master/configs/resnest/faster_rcnn_s101_fpn_syncbn-backbone+head_mstrain-range_1x_coco.py) | [model](http://download.openmmlab.com/mmdetection/v2.0/resnest/faster_rcnn_s101_fpn_syncbn-backbone%2Bhead_mstrain-range_1x_coco/faster_rcnn_s101_fpn_syncbn-backbone%2Bhead_mstrain-range_1x_coco_20201006_021058-421517f1.pth) | [log](http://download.openmmlab.com/mmdetection/v2.0/resnest/faster_rcnn_s101_fpn_syncbn-backbone%2Bhead_mstrain-range_1x_coco/faster_rcnn_s101_fpn_syncbn-backbone%2Bhead_mstrain-range_1x_coco-20201006_021058.log.json) | +|S-50-FPN | pytorch | 1x | 4.8 | - | 42.0 |[config](https://github.com/open-mmlab/mmdetection/tree/master/configs/resnest/faster_rcnn_s50_fpn_syncbn-backbone+head_mstrain-range_1x_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/resnest/faster_rcnn_s50_fpn_syncbn-backbone%2Bhead_mstrain-range_1x_coco/faster_rcnn_s50_fpn_syncbn-backbone%2Bhead_mstrain-range_1x_coco_20200926_125502-20289c16.pth) | [log](https://download.openmmlab.com/mmdetection/v2.0/resnest/faster_rcnn_s50_fpn_syncbn-backbone%2Bhead_mstrain-range_1x_coco/faster_rcnn_s50_fpn_syncbn-backbone%2Bhead_mstrain-range_1x_coco-20200926_125502.log.json) | +|S-101-FPN | pytorch | 1x | 7.1 | - | 44.5 |[config](https://github.com/open-mmlab/mmdetection/tree/master/configs/resnest/faster_rcnn_s101_fpn_syncbn-backbone+head_mstrain-range_1x_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/resnest/faster_rcnn_s101_fpn_syncbn-backbone%2Bhead_mstrain-range_1x_coco/faster_rcnn_s101_fpn_syncbn-backbone%2Bhead_mstrain-range_1x_coco_20201006_021058-421517f1.pth) | [log](https://download.openmmlab.com/mmdetection/v2.0/resnest/faster_rcnn_s101_fpn_syncbn-backbone%2Bhead_mstrain-range_1x_coco/faster_rcnn_s101_fpn_syncbn-backbone%2Bhead_mstrain-range_1x_coco-20201006_021058.log.json) | ### Mask R-CNN | Backbone | Style | Lr schd | Mem (GB) | Inf time (fps) | box AP | mask AP | Config | Download | | :-------------: | :-----: | :-----: | :------: | :------------: | :----: | :-----: | :------: | :--------: | -|S-50-FPN | pytorch | 1x | 5.5 | - | 42.6 | 38.1 |[config](https://github.com/open-mmlab/mmdetection/tree/master/configs/resnest/mask_rcnn_s50_fpn_syncbn-backbone+head_mstrain_1x_coco.py) | [model](http://download.openmmlab.com/mmdetection/v2.0/resnest/mask_rcnn_s50_fpn_syncbn-backbone%2Bhead_mstrain_1x_coco/mask_rcnn_s50_fpn_syncbn-backbone%2Bhead_mstrain_1x_coco_20200926_125503-8a2c3d47.pth) | [log](http://download.openmmlab.com/mmdetection/v2.0/resnest/mask_rcnn_s50_fpn_syncbn-backbone%2Bhead_mstrain_1x_coco/mask_rcnn_s50_fpn_syncbn-backbone%2Bhead_mstrain_1x_coco-20200926_125503.log.json) | -|S-101-FPN | pytorch | 1x | 7.8 | - | 45.2 | 40.2 |[config](https://github.com/open-mmlab/mmdetection/tree/master/configs/resnest/mask_rcnn_s101_fpn_syncbn-backbone+head_mstrain_1x_coco.py) | [model](http://download.openmmlab.com/mmdetection/v2.0/resnest/mask_rcnn_s101_fpn_syncbn-backbone%2Bhead_mstrain_1x_coco/mask_rcnn_s101_fpn_syncbn-backbone%2Bhead_mstrain_1x_coco_20201005_215831-af60cdf9.pth) | [log](http://download.openmmlab.com/mmdetection/v2.0/resnest/mask_rcnn_s101_fpn_syncbn-backbone%2Bhead_mstrain_1x_coco/mask_rcnn_s101_fpn_syncbn-backbone%2Bhead_mstrain_1x_coco-20201005_215831.log.json) | +|S-50-FPN | pytorch | 1x | 5.5 | - | 42.6 | 38.1 |[config](https://github.com/open-mmlab/mmdetection/tree/master/configs/resnest/mask_rcnn_s50_fpn_syncbn-backbone+head_mstrain_1x_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/resnest/mask_rcnn_s50_fpn_syncbn-backbone%2Bhead_mstrain_1x_coco/mask_rcnn_s50_fpn_syncbn-backbone%2Bhead_mstrain_1x_coco_20200926_125503-8a2c3d47.pth) | [log](https://download.openmmlab.com/mmdetection/v2.0/resnest/mask_rcnn_s50_fpn_syncbn-backbone%2Bhead_mstrain_1x_coco/mask_rcnn_s50_fpn_syncbn-backbone%2Bhead_mstrain_1x_coco-20200926_125503.log.json) | +|S-101-FPN | pytorch | 1x | 7.8 | - | 45.2 | 40.2 |[config](https://github.com/open-mmlab/mmdetection/tree/master/configs/resnest/mask_rcnn_s101_fpn_syncbn-backbone+head_mstrain_1x_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/resnest/mask_rcnn_s101_fpn_syncbn-backbone%2Bhead_mstrain_1x_coco/mask_rcnn_s101_fpn_syncbn-backbone%2Bhead_mstrain_1x_coco_20201005_215831-af60cdf9.pth) | [log](https://download.openmmlab.com/mmdetection/v2.0/resnest/mask_rcnn_s101_fpn_syncbn-backbone%2Bhead_mstrain_1x_coco/mask_rcnn_s101_fpn_syncbn-backbone%2Bhead_mstrain_1x_coco-20201005_215831.log.json) | ### Cascade R-CNN | Backbone | Style | Lr schd | Mem (GB) | Inf time (fps) | box AP | Config | Download | | :-------------: | :-----: | :-----: | :------: | :------------: | :----: | :------: | :--------: | -|S-50-FPN | pytorch | 1x | - | - | 44.5 |[config](https://github.com/open-mmlab/mmdetection/tree/master/configs/resnest/cascade_rcnn_s50_fpn_syncbn-backbone+head_mstrain-range_1x_coco.py) | [model](http://download.openmmlab.com/mmdetection/v2.0/resnest/cascade_rcnn_s50_fpn_syncbn-backbone%2Bhead_mstrain-range_1x_coco/cascade_rcnn_s50_fpn_syncbn-backbone%2Bhead_mstrain-range_1x_coco_20201122_213640-763cc7b5.pth) | [log](http://download.openmmlab.com/mmdetection/v2.0/resnest/cascade_rcnn_s101_fpn_syncbn-backbone%2Bhead_mstrain-range_1x_coco/cascade_rcnn_s101_fpn_syncbn-backbone%2Bhead_mstrain-range_1x_coco-20201005_113242.log.json) | -|S-101-FPN | pytorch | 1x | 8.4 | - | 46.8 |[config](https://github.com/open-mmlab/mmdetection/tree/master/configs/resnest/cascade_rcnn_s101_fpn_syncbn-backbone+head_mstrain-range_1x_coco.py) | [model](http://download.openmmlab.com/mmdetection/v2.0/resnest/cascade_rcnn_s101_fpn_syncbn-backbone%2Bhead_mstrain-range_1x_coco/cascade_rcnn_s101_fpn_syncbn-backbone%2Bhead_mstrain-range_1x_coco_20201005_113242-b9459f8f.pth) | [log](http://download.openmmlab.com/mmdetection/v2.0/resnest/cascade_rcnn_s50_fpn_syncbn-backbone%2Bhead_mstrain-range_1x_coco/cascade_rcnn_s50_fpn_syncbn-backbone%2Bhead_mstrain-range_1x_coco-20201122_213640.log.json) | +|S-50-FPN | pytorch | 1x | - | - | 44.5 |[config](https://github.com/open-mmlab/mmdetection/tree/master/configs/resnest/cascade_rcnn_s50_fpn_syncbn-backbone+head_mstrain-range_1x_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/resnest/cascade_rcnn_s50_fpn_syncbn-backbone%2Bhead_mstrain-range_1x_coco/cascade_rcnn_s50_fpn_syncbn-backbone%2Bhead_mstrain-range_1x_coco_20201122_213640-763cc7b5.pth) | [log](https://download.openmmlab.com/mmdetection/v2.0/resnest/cascade_rcnn_s101_fpn_syncbn-backbone%2Bhead_mstrain-range_1x_coco/cascade_rcnn_s101_fpn_syncbn-backbone%2Bhead_mstrain-range_1x_coco-20201005_113242.log.json) | +|S-101-FPN | pytorch | 1x | 8.4 | - | 46.8 |[config](https://github.com/open-mmlab/mmdetection/tree/master/configs/resnest/cascade_rcnn_s101_fpn_syncbn-backbone+head_mstrain-range_1x_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/resnest/cascade_rcnn_s101_fpn_syncbn-backbone%2Bhead_mstrain-range_1x_coco/cascade_rcnn_s101_fpn_syncbn-backbone%2Bhead_mstrain-range_1x_coco_20201005_113242-b9459f8f.pth) | [log](https://download.openmmlab.com/mmdetection/v2.0/resnest/cascade_rcnn_s50_fpn_syncbn-backbone%2Bhead_mstrain-range_1x_coco/cascade_rcnn_s50_fpn_syncbn-backbone%2Bhead_mstrain-range_1x_coco-20201122_213640.log.json) | ### Cascade Mask R-CNN | Backbone | Style | Lr schd | Mem (GB) | Inf time (fps) | box AP | mask AP | Config | Download | | :-------------: | :-----: | :-----: | :------: | :------------: | :----: | :-----: | :------: | :--------: | -|S-50-FPN | pytorch | 1x | - | - | 45.4 | 39.5 |[config](https://github.com/open-mmlab/mmdetection/tree/master/configs/resnest/cascade_mask_rcnn_s50_fpn_syncbn-backbone+head_mstrain_1x_coco.py) | [model](http://download.openmmlab.com/mmdetection/v2.0/resnest/cascade_mask_rcnn_s50_fpn_syncbn-backbone%2Bhead_mstrain_1x_coco/cascade_mask_rcnn_s50_fpn_syncbn-backbone%2Bhead_mstrain_1x_coco_20201122_104428-99eca4c7.pth) | [log](http://download.openmmlab.com/mmdetection/v2.0/resnest/cascade_mask_rcnn_s50_fpn_syncbn-backbone%2Bhead_mstrain_1x_coco/cascade_mask_rcnn_s50_fpn_syncbn-backbone%2Bhead_mstrain_1x_coco-20201122_104428.log.json) | -|S-101-FPN | pytorch | 1x | 10.5 | - | 47.7 | 41.4 |[config](https://github.com/open-mmlab/mmdetection/tree/master/configs/resnest/cascade_mask_rcnn_s101_fpn_syncbn-backbone+head_mstrain_1x_coco.py) | [model](http://download.openmmlab.com/mmdetection/v2.0/resnest/cascade_mask_rcnn_s101_fpn_syncbn-backbone%2Bhead_mstrain_1x_coco/cascade_mask_rcnn_s101_fpn_syncbn-backbone%2Bhead_mstrain_1x_coco_20201005_113243-42607475.pth) | [log](http://download.openmmlab.com/mmdetection/v2.0/resnest/cascade_mask_rcnn_s101_fpn_syncbn-backbone%2Bhead_mstrain_1x_coco/cascade_mask_rcnn_s101_fpn_syncbn-backbone%2Bhead_mstrain_1x_coco-20201005_113243.log.json) | +|S-50-FPN | pytorch | 1x | - | - | 45.4 | 39.5 |[config](https://github.com/open-mmlab/mmdetection/tree/master/configs/resnest/cascade_mask_rcnn_s50_fpn_syncbn-backbone+head_mstrain_1x_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/resnest/cascade_mask_rcnn_s50_fpn_syncbn-backbone%2Bhead_mstrain_1x_coco/cascade_mask_rcnn_s50_fpn_syncbn-backbone%2Bhead_mstrain_1x_coco_20201122_104428-99eca4c7.pth) | [log](https://download.openmmlab.com/mmdetection/v2.0/resnest/cascade_mask_rcnn_s50_fpn_syncbn-backbone%2Bhead_mstrain_1x_coco/cascade_mask_rcnn_s50_fpn_syncbn-backbone%2Bhead_mstrain_1x_coco-20201122_104428.log.json) | +|S-101-FPN | pytorch | 1x | 10.5 | - | 47.7 | 41.4 |[config](https://github.com/open-mmlab/mmdetection/tree/master/configs/resnest/cascade_mask_rcnn_s101_fpn_syncbn-backbone+head_mstrain_1x_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/resnest/cascade_mask_rcnn_s101_fpn_syncbn-backbone%2Bhead_mstrain_1x_coco/cascade_mask_rcnn_s101_fpn_syncbn-backbone%2Bhead_mstrain_1x_coco_20201005_113243-42607475.pth) | [log](https://download.openmmlab.com/mmdetection/v2.0/resnest/cascade_mask_rcnn_s101_fpn_syncbn-backbone%2Bhead_mstrain_1x_coco/cascade_mask_rcnn_s101_fpn_syncbn-backbone%2Bhead_mstrain_1x_coco-20201005_113243.log.json) | diff --git a/configs/resnest/cascade_mask_rcnn_s101_fpn_syncbn-backbone+head_mstrain_1x_coco.py b/configs/resnest/cascade_mask_rcnn_s101_fpn_syncbn-backbone+head_mstrain_1x_coco.py index 3995603..406f39d 100644 --- a/configs/resnest/cascade_mask_rcnn_s101_fpn_syncbn-backbone+head_mstrain_1x_coco.py +++ b/configs/resnest/cascade_mask_rcnn_s101_fpn_syncbn-backbone+head_mstrain_1x_coco.py @@ -1,4 +1,7 @@ _base_ = './cascade_mask_rcnn_s50_fpn_syncbn-backbone+head_mstrain_1x_coco.py' model = dict( - pretrained='open-mmlab://resnest101', - backbone=dict(stem_channels=128, depth=101)) + backbone=dict( + stem_channels=128, + depth=101, + init_cfg=dict(type='Pretrained', + checkpoint='open-mmlab://resnest101'))) diff --git a/configs/resnest/cascade_mask_rcnn_s50_fpn_syncbn-backbone+head_mstrain_1x_coco.py b/configs/resnest/cascade_mask_rcnn_s50_fpn_syncbn-backbone+head_mstrain_1x_coco.py index f2cf444..83d7537 100644 --- a/configs/resnest/cascade_mask_rcnn_s50_fpn_syncbn-backbone+head_mstrain_1x_coco.py +++ b/configs/resnest/cascade_mask_rcnn_s50_fpn_syncbn-backbone+head_mstrain_1x_coco.py @@ -1,7 +1,6 @@ _base_ = '../cascade_rcnn/cascade_mask_rcnn_r50_fpn_1x_coco.py' norm_cfg = dict(type='SyncBN', requires_grad=True) model = dict( - pretrained='open-mmlab://resnest50', backbone=dict( type='ResNeSt', stem_channels=64, @@ -14,7 +13,8 @@ frozen_stages=1, norm_cfg=norm_cfg, norm_eval=False, - style='pytorch'), + style='pytorch', + init_cfg=dict(type='Pretrained', checkpoint='open-mmlab://resnest50')), roi_head=dict( bbox_head=[ dict( diff --git a/configs/resnest/cascade_rcnn_s101_fpn_syncbn-backbone+head_mstrain-range_1x_coco.py b/configs/resnest/cascade_rcnn_s101_fpn_syncbn-backbone+head_mstrain-range_1x_coco.py index 53964a3..0a7476a 100644 --- a/configs/resnest/cascade_rcnn_s101_fpn_syncbn-backbone+head_mstrain-range_1x_coco.py +++ b/configs/resnest/cascade_rcnn_s101_fpn_syncbn-backbone+head_mstrain-range_1x_coco.py @@ -1,4 +1,7 @@ _base_ = './cascade_rcnn_s50_fpn_syncbn-backbone+head_mstrain-range_1x_coco.py' model = dict( - pretrained='open-mmlab://resnest101', - backbone=dict(stem_channels=128, depth=101)) + backbone=dict( + stem_channels=128, + depth=101, + init_cfg=dict(type='Pretrained', + checkpoint='open-mmlab://resnest101'))) diff --git a/configs/resnest/cascade_rcnn_s50_fpn_syncbn-backbone+head_mstrain-range_1x_coco.py b/configs/resnest/cascade_rcnn_s50_fpn_syncbn-backbone+head_mstrain-range_1x_coco.py index 78a154b..6ed7730 100644 --- a/configs/resnest/cascade_rcnn_s50_fpn_syncbn-backbone+head_mstrain-range_1x_coco.py +++ b/configs/resnest/cascade_rcnn_s50_fpn_syncbn-backbone+head_mstrain-range_1x_coco.py @@ -1,7 +1,6 @@ _base_ = '../cascade_rcnn/cascade_rcnn_r50_fpn_1x_coco.py' norm_cfg = dict(type='SyncBN', requires_grad=True) model = dict( - pretrained='open-mmlab://resnest50', backbone=dict( type='ResNeSt', stem_channels=64, @@ -14,7 +13,8 @@ frozen_stages=1, norm_cfg=norm_cfg, norm_eval=False, - style='pytorch'), + style='pytorch', + init_cfg=dict(type='Pretrained', checkpoint='open-mmlab://resnest50')), roi_head=dict( bbox_head=[ dict( diff --git a/configs/resnest/faster_rcnn_s101_fpn_syncbn-backbone+head_mstrain-range_1x_coco.py b/configs/resnest/faster_rcnn_s101_fpn_syncbn-backbone+head_mstrain-range_1x_coco.py index 1915ab1..40a2f1f 100644 --- a/configs/resnest/faster_rcnn_s101_fpn_syncbn-backbone+head_mstrain-range_1x_coco.py +++ b/configs/resnest/faster_rcnn_s101_fpn_syncbn-backbone+head_mstrain-range_1x_coco.py @@ -1,4 +1,7 @@ _base_ = './faster_rcnn_s50_fpn_syncbn-backbone+head_mstrain-range_1x_coco.py' model = dict( - pretrained='open-mmlab://resnest101', - backbone=dict(stem_channels=128, depth=101)) + backbone=dict( + stem_channels=128, + depth=101, + init_cfg=dict(type='Pretrained', + checkpoint='open-mmlab://resnest101'))) diff --git a/configs/resnest/faster_rcnn_s50_fpn_syncbn-backbone+head_mstrain-range_1x_coco.py b/configs/resnest/faster_rcnn_s50_fpn_syncbn-backbone+head_mstrain-range_1x_coco.py index 422fbca..eb1ecd2 100644 --- a/configs/resnest/faster_rcnn_s50_fpn_syncbn-backbone+head_mstrain-range_1x_coco.py +++ b/configs/resnest/faster_rcnn_s50_fpn_syncbn-backbone+head_mstrain-range_1x_coco.py @@ -1,7 +1,6 @@ _base_ = '../faster_rcnn/faster_rcnn_r50_fpn_1x_coco.py' norm_cfg = dict(type='SyncBN', requires_grad=True) model = dict( - pretrained='open-mmlab://resnest50', backbone=dict( type='ResNeSt', stem_channels=64, @@ -14,7 +13,8 @@ frozen_stages=1, norm_cfg=norm_cfg, norm_eval=False, - style='pytorch'), + style='pytorch', + init_cfg=dict(type='Pretrained', checkpoint='open-mmlab://resnest50')), roi_head=dict( bbox_head=dict( type='Shared4Conv1FCBBoxHead', diff --git a/configs/resnest/mask_rcnn_s101_fpn_syncbn-backbone+head_mstrain_1x_coco.py b/configs/resnest/mask_rcnn_s101_fpn_syncbn-backbone+head_mstrain_1x_coco.py index 89e077d..c882ba1 100644 --- a/configs/resnest/mask_rcnn_s101_fpn_syncbn-backbone+head_mstrain_1x_coco.py +++ b/configs/resnest/mask_rcnn_s101_fpn_syncbn-backbone+head_mstrain_1x_coco.py @@ -1,4 +1,7 @@ _base_ = './mask_rcnn_s50_fpn_syncbn-backbone+head_mstrain_1x_coco.py' model = dict( - pretrained='open-mmlab://resnest101', - backbone=dict(stem_channels=128, depth=101)) + backbone=dict( + stem_channels=128, + depth=101, + init_cfg=dict(type='Pretrained', + checkpoint='open-mmlab://resnest101'))) diff --git a/configs/resnest/mask_rcnn_s50_fpn_syncbn-backbone+head_mstrain_1x_coco.py b/configs/resnest/mask_rcnn_s50_fpn_syncbn-backbone+head_mstrain_1x_coco.py index 29f21fd..4e50dea 100644 --- a/configs/resnest/mask_rcnn_s50_fpn_syncbn-backbone+head_mstrain_1x_coco.py +++ b/configs/resnest/mask_rcnn_s50_fpn_syncbn-backbone+head_mstrain_1x_coco.py @@ -1,7 +1,6 @@ _base_ = '../mask_rcnn/mask_rcnn_r50_fpn_1x_coco.py' norm_cfg = dict(type='SyncBN', requires_grad=True) model = dict( - pretrained='open-mmlab://resnest50', backbone=dict( type='ResNeSt', stem_channels=64, @@ -14,7 +13,8 @@ frozen_stages=1, norm_cfg=norm_cfg, norm_eval=False, - style='pytorch'), + style='pytorch', + init_cfg=dict(type='Pretrained', checkpoint='open-mmlab://resnest50')), roi_head=dict( bbox_head=dict( type='Shared4Conv1FCBBoxHead', diff --git a/configs/resnest/metafile.yml b/configs/resnest/metafile.yml new file mode 100644 index 0000000..d7f68e5 --- /dev/null +++ b/configs/resnest/metafile.yml @@ -0,0 +1,136 @@ +Collections: + - Name: ResNeSt + Metadata: + Training Data: COCO + Training Techniques: + - SGD with Momentum + - Weight Decay + Training Resources: 8x V100 GPUs + Architecture: + - ResNeSt + Paper: + URL: https://arxiv.org/abs/2004.08955 + Title: 'ResNeSt: Split-Attention Networks' + README: configs/renest/README.md + Code: + URL: https://github.com/open-mmlab/mmdetection/blob/v2.7.0/mmdet/models/backbones/resnest.py#L273 + Version: v2.7.0 + +Models: + - Name: faster_rcnn_s50_fpn_syncbn-backbone+head_mstrain-range_1x_coco + In Collection: ResNeSt + Config: configs/resnest/faster_rcnn_s50_fpn_syncbn-backbone+head_mstrain-range_1x_coco.py + Metadata: + Training Memory (GB): 4.8 + Epochs: 12 + Results: + - Task: Object Detection + Dataset: COCO + Metrics: + box AP: 42.0 + Weights: https://download.openmmlab.com/mmdetection/v2.0/resnest/faster_rcnn_s50_fpn_syncbn-backbone%2Bhead_mstrain-range_1x_coco/faster_rcnn_s50_fpn_syncbn-backbone%2Bhead_mstrain-range_1x_coco_20200926_125502-20289c16.pth + + - Name: faster_rcnn_s101_fpn_syncbn-backbone+head_mstrain-range_1x_coco + In Collection: ResNeSt + Config: configs/resnest/faster_rcnn_s101_fpn_syncbn-backbone+head_mstrain-range_1x_coco.py + Metadata: + Training Memory (GB): 7.1 + Epochs: 12 + Results: + - Task: Object Detection + Dataset: COCO + Metrics: + box AP: 44.5 + Weights: https://download.openmmlab.com/mmdetection/v2.0/resnest/faster_rcnn_s101_fpn_syncbn-backbone%2Bhead_mstrain-range_1x_coco/faster_rcnn_s101_fpn_syncbn-backbone%2Bhead_mstrain-range_1x_coco_20201006_021058-421517f1.pth + + - Name: mask_rcnn_s50_fpn_syncbn-backbone+head_mstrain_1x_coco + In Collection: ResNeSt + Config: configs/resnest/mask_rcnn_s50_fpn_syncbn-backbone+head_mstrain_1x_coco.py + Metadata: + Training Memory (GB): 5.5 + Epochs: 12 + Results: + - Task: Object Detection + Dataset: COCO + Metrics: + box AP: 42.6 + - Task: Instance Segmentation + Dataset: COCO + Metrics: + mask AP: 38.1 + Weights: https://download.openmmlab.com/mmdetection/v2.0/resnest/mask_rcnn_s50_fpn_syncbn-backbone%2Bhead_mstrain_1x_coco/mask_rcnn_s50_fpn_syncbn-backbone%2Bhead_mstrain_1x_coco_20200926_125503-8a2c3d47.pth + + - Name: mask_rcnn_s101_fpn_syncbn-backbone+head_mstrain_1x_coco + In Collection: ResNeSt + Config: configs/resnest/mask_rcnn_s101_fpn_syncbn-backbone+head_mstrain_1x_coco.py + Metadata: + Training Memory (GB): 7.8 + Epochs: 12 + Results: + - Task: Object Detection + Dataset: COCO + Metrics: + box AP: 45.2 + - Task: Instance Segmentation + Dataset: COCO + Metrics: + mask AP: 40.2 + Weights: https://download.openmmlab.com/mmdetection/v2.0/resnest/mask_rcnn_s101_fpn_syncbn-backbone%2Bhead_mstrain_1x_coco/mask_rcnn_s101_fpn_syncbn-backbone%2Bhead_mstrain_1x_coco_20201005_215831-af60cdf9.pth + + - Name: cascade_rcnn_s50_fpn_syncbn-backbone+head_mstrain-range_1x_coco + In Collection: ResNeSt + Config: configs/resnest/cascade_rcnn_s50_fpn_syncbn-backbone+head_mstrain-range_1x_coco.py + Metadata: + Epochs: 12 + Results: + - Task: Object Detection + Dataset: COCO + Metrics: + box AP: 44.5 + Weights: https://download.openmmlab.com/mmdetection/v2.0/resnest/cascade_rcnn_s50_fpn_syncbn-backbone%2Bhead_mstrain-range_1x_coco/cascade_rcnn_s50_fpn_syncbn-backbone%2Bhead_mstrain-range_1x_coco_20201122_213640-763cc7b5.pth + + - Name: cascade_rcnn_s101_fpn_syncbn-backbone+head_mstrain-range_1x_coco + In Collection: ResNeSt + Config: configs/resnest/cascade_rcnn_s101_fpn_syncbn-backbone+head_mstrain-range_1x_coco.py + Metadata: + Training Memory (GB): 8.4 + Epochs: 12 + Results: + - Task: Object Detection + Dataset: COCO + Metrics: + box AP: 46.8 + Weights: https://download.openmmlab.com/mmdetection/v2.0/resnest/cascade_rcnn_s101_fpn_syncbn-backbone%2Bhead_mstrain-range_1x_coco/cascade_rcnn_s101_fpn_syncbn-backbone%2Bhead_mstrain-range_1x_coco_20201005_113242-b9459f8f.pth + + - Name: cascade_mask_rcnn_s50_fpn_syncbn-backbone+head_mstrain_1x_coco + In Collection: ResNeSt + Config: configs/resnest/cascade_mask_rcnn_s50_fpn_syncbn-backbone+head_mstrain_1x_coco.py + Metadata: + Epochs: 12 + Results: + - Task: Object Detection + Dataset: COCO + Metrics: + box AP: 45.4 + - Task: Instance Segmentation + Dataset: COCO + Metrics: + mask AP: 39.5 + Weights: https://download.openmmlab.com/mmdetection/v2.0/resnest/cascade_mask_rcnn_s50_fpn_syncbn-backbone%2Bhead_mstrain_1x_coco/cascade_mask_rcnn_s50_fpn_syncbn-backbone%2Bhead_mstrain_1x_coco_20201122_104428-99eca4c7.pth + + - Name: cascade_mask_rcnn_s101_fpn_syncbn-backbone+head_mstrain_1x_coco + In Collection: ResNeSt + Config: configs/resnest/cascade_mask_rcnn_s101_fpn_syncbn-backbone+head_mstrain_1x_coco.py + Metadata: + Training Memory (GB): 10.5 + Epochs: 12 + Results: + - Task: Object Detection + Dataset: COCO + Metrics: + box AP: 47.7 + - Task: Instance Segmentation + Dataset: COCO + Metrics: + mask AP: 41.4 + Weights: https://download.openmmlab.com/mmdetection/v2.0/resnest/cascade_mask_rcnn_s101_fpn_syncbn-backbone%2Bhead_mstrain_1x_coco/cascade_mask_rcnn_s101_fpn_syncbn-backbone%2Bhead_mstrain_1x_coco_20201005_113243-42607475.pth diff --git a/configs/retinanet/README.md b/configs/retinanet/README.md index 6b66534..e5f099e 100644 --- a/configs/retinanet/README.md +++ b/configs/retinanet/README.md @@ -2,7 +2,7 @@ ## Introduction -[ALGORITHM] + ```latex @inproceedings{lin2017focal, @@ -17,13 +17,24 @@ | Backbone | Style | Lr schd | Mem (GB) | Inf time (fps) | box AP | Config | Download | | :-------------: | :-----: | :-----: | :------: | :------------: | :----: | :------: | :--------: | -| R-50-FPN | caffe | 1x | 3.5 | 18.6 | 36.3 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/retinanet/retinanet_r50_caffe_fpn_1x_coco.py) | [model](http://download.openmmlab.com/mmdetection/v2.0/retinanet/retinanet_r50_caffe_fpn_1x_coco/retinanet_r50_caffe_fpn_1x_coco_20200531-f11027c5.pth) | [log](http://download.openmmlab.com/mmdetection/v2.0/retinanet/retinanet_r50_caffe_fpn_1x_coco/retinanet_r50_caffe_fpn_1x_coco_20200531_012518.log.json) | -| R-50-FPN | pytorch | 1x | 3.8 | 19.0 | 36.5 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/retinanet/retinanet_r50_fpn_1x_coco.py) | [model](http://download.openmmlab.com/mmdetection/v2.0/retinanet/retinanet_r50_fpn_1x_coco/retinanet_r50_fpn_1x_coco_20200130-c2398f9e.pth) | [log](http://download.openmmlab.com/mmdetection/v2.0/retinanet/retinanet_r50_fpn_1x_coco/retinanet_r50_fpn_1x_coco_20200130_002941.log.json) | -| R-50-FPN | pytorch | 2x | - | - | 37.4 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/retinanet/retinanet_r50_fpn_2x_coco.py) | [model](http://download.openmmlab.com/mmdetection/v2.0/retinanet/retinanet_r50_fpn_2x_coco/retinanet_r50_fpn_2x_coco_20200131-fdb43119.pth) | [log](http://download.openmmlab.com/mmdetection/v2.0/retinanet/retinanet_r50_fpn_2x_coco/retinanet_r50_fpn_2x_coco_20200131_114738.log.json) | -| R-101-FPN | caffe | 1x | 5.5 | 14.7 | 38.5 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/retinanet/retinanet_r101_caffe_fpn_1x_coco.py) | [model](http://download.openmmlab.com/mmdetection/v2.0/retinanet/retinanet_r101_caffe_fpn_1x_coco/retinanet_r101_caffe_fpn_1x_coco_20200531-b428fa0f.pth) | [log](http://download.openmmlab.com/mmdetection/v2.0/retinanet/retinanet_r101_caffe_fpn_1x_coco/retinanet_r101_caffe_fpn_1x_coco_20200531_012536.log.json) | -| R-101-FPN | pytorch | 1x | 5.7 | 15.0 | 38.5 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/retinanet/retinanet_r101_fpn_1x_coco.py) | [model](http://download.openmmlab.com/mmdetection/v2.0/retinanet/retinanet_r101_fpn_1x_coco/retinanet_r101_fpn_1x_coco_20200130-7a93545f.pth) | [log](http://download.openmmlab.com/mmdetection/v2.0/retinanet/retinanet_r101_fpn_1x_coco/retinanet_r101_fpn_1x_coco_20200130_003055.log.json) | -| R-101-FPN | pytorch | 2x | - | - | 38.9 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/retinanet/retinanet_r101_fpn_2x_coco.py) | [model](http://download.openmmlab.com/mmdetection/v2.0/retinanet/retinanet_r101_fpn_2x_coco/retinanet_r101_fpn_2x_coco_20200131-5560aee8.pth) | [log](http://download.openmmlab.com/mmdetection/v2.0/retinanet/retinanet_r101_fpn_2x_coco/retinanet_r101_fpn_2x_coco_20200131_114859.log.json) | -| X-101-32x4d-FPN | pytorch | 1x | 7.0 | 12.1 | 39.9 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/retinanet/retinanet_x101_32x4d_fpn_1x_coco.py) | [model](http://download.openmmlab.com/mmdetection/v2.0/retinanet/retinanet_x101_32x4d_fpn_1x_coco/retinanet_x101_32x4d_fpn_1x_coco_20200130-5c8b7ec4.pth) | [log](http://download.openmmlab.com/mmdetection/v2.0/retinanet/retinanet_x101_32x4d_fpn_1x_coco/retinanet_x101_32x4d_fpn_1x_coco_20200130_003004.log.json) | -| X-101-32x4d-FPN | pytorch | 2x | - | - | 40.1 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/retinanet/retinanet_x101_32x4d_fpn_2x_coco.py) | [model](http://download.openmmlab.com/mmdetection/v2.0/retinanet/retinanet_x101_32x4d_fpn_2x_coco/retinanet_x101_32x4d_fpn_2x_coco_20200131-237fc5e1.pth) | [log](http://download.openmmlab.com/mmdetection/v2.0/retinanet/retinanet_x101_32x4d_fpn_2x_coco/retinanet_x101_32x4d_fpn_2x_coco_20200131_114812.log.json) | -| X-101-64x4d-FPN | pytorch | 1x | 10.0 | 8.7 | 41.0 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/retinanet/retinanet_x101_64x4d_fpn_1x_coco.py) | [model](http://download.openmmlab.com/mmdetection/v2.0/retinanet/retinanet_x101_64x4d_fpn_1x_coco/retinanet_x101_64x4d_fpn_1x_coco_20200130-366f5af1.pth) | [log](http://download.openmmlab.com/mmdetection/v2.0/retinanet/retinanet_x101_64x4d_fpn_1x_coco/retinanet_x101_64x4d_fpn_1x_coco_20200130_003008.log.json) | -| X-101-64x4d-FPN | pytorch | 2x | - | - | 40.8 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/retinanet/retinanet_x101_64x4d_fpn_2x_coco.py) | [model](http://download.openmmlab.com/mmdetection/v2.0/retinanet/retinanet_x101_64x4d_fpn_2x_coco/retinanet_x101_64x4d_fpn_2x_coco_20200131-bca068ab.pth) | [log](http://download.openmmlab.com/mmdetection/v2.0/retinanet/retinanet_x101_64x4d_fpn_2x_coco/retinanet_x101_64x4d_fpn_2x_coco_20200131_114833.log.json) | +| R-50-FPN | caffe | 1x | 3.5 | 18.6 | 36.3 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/retinanet/retinanet_r50_caffe_fpn_1x_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/retinanet/retinanet_r50_caffe_fpn_1x_coco/retinanet_r50_caffe_fpn_1x_coco_20200531-f11027c5.pth) | [log](https://download.openmmlab.com/mmdetection/v2.0/retinanet/retinanet_r50_caffe_fpn_1x_coco/retinanet_r50_caffe_fpn_1x_coco_20200531_012518.log.json) | +| R-50-FPN | pytorch | 1x | 3.8 | 19.0 | 36.5 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/retinanet/retinanet_r50_fpn_1x_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/retinanet/retinanet_r50_fpn_1x_coco/retinanet_r50_fpn_1x_coco_20200130-c2398f9e.pth) | [log](https://download.openmmlab.com/mmdetection/v2.0/retinanet/retinanet_r50_fpn_1x_coco/retinanet_r50_fpn_1x_coco_20200130_002941.log.json) | +| R-50-FPN | pytorch | 2x | - | - | 37.4 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/retinanet/retinanet_r50_fpn_2x_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/retinanet/retinanet_r50_fpn_2x_coco/retinanet_r50_fpn_2x_coco_20200131-fdb43119.pth) | [log](https://download.openmmlab.com/mmdetection/v2.0/retinanet/retinanet_r50_fpn_2x_coco/retinanet_r50_fpn_2x_coco_20200131_114738.log.json) | +| R-101-FPN | caffe | 1x | 5.5 | 14.7 | 38.5 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/retinanet/retinanet_r101_caffe_fpn_1x_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/retinanet/retinanet_r101_caffe_fpn_1x_coco/retinanet_r101_caffe_fpn_1x_coco_20200531-b428fa0f.pth) | [log](https://download.openmmlab.com/mmdetection/v2.0/retinanet/retinanet_r101_caffe_fpn_1x_coco/retinanet_r101_caffe_fpn_1x_coco_20200531_012536.log.json) | +| R-101-FPN | pytorch | 1x | 5.7 | 15.0 | 38.5 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/retinanet/retinanet_r101_fpn_1x_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/retinanet/retinanet_r101_fpn_1x_coco/retinanet_r101_fpn_1x_coco_20200130-7a93545f.pth) | [log](https://download.openmmlab.com/mmdetection/v2.0/retinanet/retinanet_r101_fpn_1x_coco/retinanet_r101_fpn_1x_coco_20200130_003055.log.json) | +| R-101-FPN | pytorch | 2x | - | - | 38.9 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/retinanet/retinanet_r101_fpn_2x_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/retinanet/retinanet_r101_fpn_2x_coco/retinanet_r101_fpn_2x_coco_20200131-5560aee8.pth) | [log](https://download.openmmlab.com/mmdetection/v2.0/retinanet/retinanet_r101_fpn_2x_coco/retinanet_r101_fpn_2x_coco_20200131_114859.log.json) | +| X-101-32x4d-FPN | pytorch | 1x | 7.0 | 12.1 | 39.9 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/retinanet/retinanet_x101_32x4d_fpn_1x_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/retinanet/retinanet_x101_32x4d_fpn_1x_coco/retinanet_x101_32x4d_fpn_1x_coco_20200130-5c8b7ec4.pth) | [log](https://download.openmmlab.com/mmdetection/v2.0/retinanet/retinanet_x101_32x4d_fpn_1x_coco/retinanet_x101_32x4d_fpn_1x_coco_20200130_003004.log.json) | +| X-101-32x4d-FPN | pytorch | 2x | - | - | 40.1 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/retinanet/retinanet_x101_32x4d_fpn_2x_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/retinanet/retinanet_x101_32x4d_fpn_2x_coco/retinanet_x101_32x4d_fpn_2x_coco_20200131-237fc5e1.pth) | [log](https://download.openmmlab.com/mmdetection/v2.0/retinanet/retinanet_x101_32x4d_fpn_2x_coco/retinanet_x101_32x4d_fpn_2x_coco_20200131_114812.log.json) | +| X-101-64x4d-FPN | pytorch | 1x | 10.0 | 8.7 | 41.0 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/retinanet/retinanet_x101_64x4d_fpn_1x_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/retinanet/retinanet_x101_64x4d_fpn_1x_coco/retinanet_x101_64x4d_fpn_1x_coco_20200130-366f5af1.pth) | [log](https://download.openmmlab.com/mmdetection/v2.0/retinanet/retinanet_x101_64x4d_fpn_1x_coco/retinanet_x101_64x4d_fpn_1x_coco_20200130_003008.log.json) | +| X-101-64x4d-FPN | pytorch | 2x | - | - | 40.8 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/retinanet/retinanet_x101_64x4d_fpn_2x_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/retinanet/retinanet_x101_64x4d_fpn_2x_coco/retinanet_x101_64x4d_fpn_2x_coco_20200131-bca068ab.pth) | [log](https://download.openmmlab.com/mmdetection/v2.0/retinanet/retinanet_x101_64x4d_fpn_2x_coco/retinanet_x101_64x4d_fpn_2x_coco_20200131_114833.log.json) | + +## Pre-trained Models + +We also train some models with longer schedules and multi-scale training. The users could finetune them for downstream tasks. + +| Backbone | Style | Lr schd | Mem (GB) | box AP | Config | Download | +| :----------------: | :-----: | :-----: | :------: | :----: | :------: | :--------: | +| R-50-FPN | pytorch| 3x | 3.5 | 39.5 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/retinanet/retinanet_r50_fpn_mstrain_640-800_3x_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/retinanet/retinanet_r50_fpn_mstrain_3x_coco/retinanet_r50_fpn_mstrain_3x_coco_20210718_220633-88476508.pth) | [log](https://download.openmmlab.com/mmdetection/v2.0/retinanet/retinanet_r50_fpn_mstrain_3x_coco/retinanet_r50_fpn_mstrain_3x_coco_20210718_220633-88476508.log.json) +| R-101-FPN | caffe | 3x | 5.4 | 40.7 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/retinanet/retinanet_r101_caffe_fpn_mstrain_3x_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/retinanet/retinanet_r101_caffe_fpn_mstrain_3x_coco/retinanet_r101_caffe_fpn_mstrain_3x_coco_20210721_063439-88a8a944.pth) | [log](https://download.openmmlab.com/mmdetection/v2.0/retinanet/retinanet_r101_caffe_fpn_mstrain_3x_coco/retinanet_r101_caffe_fpn_mstrain_3x_coco_20210721_063439-88a8a944.log.json) +| R-101-FPN | pytorch| 3x | 5.4 | 41 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/retinanet/retinanet_r101_fpn_mstrain_640-800_3x_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/retinanet/retinanet_r101_fpn_mstrain_3x_coco/retinanet_r101_fpn_mstrain_3x_coco_20210720_214650-7ee888e0.pth) | [log](https://download.openmmlab.com/mmdetection/v2.0/retinanet/retinanet_r101_fpn_mstrain_3x_coco/retinanet_r101_fpn_mstrain_3x_coco_20210720_214650-7ee888e0.log.json) +| X-101-64x4d-FPN | pytorch| 3x | 9.8 | 41.6 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/retinanet/retinanet_x101_64x4d_fpn_mstrain_640-800_3x_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/retinanet/retinanet_x101_64x4d_fpn_mstrain_3x_coco/retinanet_x101_64x4d_fpn_mstrain_3x_coco_20210719_051838-022c2187.pth) | [log](https://download.openmmlab.com/mmdetection/v2.0/retinanet/retinanet_x101_64x4d_fpn_mstrain_3x_coco/retinanet_x101_64x4d_fpn_mstrain_3x_coco_20210719_051838-022c2187.log.json) diff --git a/configs/retinanet/metafile.yml b/configs/retinanet/metafile.yml new file mode 100644 index 0000000..efff090 --- /dev/null +++ b/configs/retinanet/metafile.yml @@ -0,0 +1,261 @@ +Collections: + - Name: RetinaNet + Metadata: + Training Data: COCO + Training Techniques: + - SGD with Momentum + - Weight Decay + Training Resources: 8x V100 GPUs + Architecture: + - Focal Loss + - FPN + - ResNet + Paper: + URL: https://arxiv.org/abs/1708.02002 + Title: 'Focal Loss for Dense Object Detection' + README: configs/retinanet/README.md + Code: + URL: https://github.com/open-mmlab/mmdetection/blob/v2.0.0/mmdet/models/detectors/retinanet.py#L6 + Version: v2.0.0 + +Models: + - Name: retinanet_r50_caffe_fpn_1x_coco + In Collection: RetinaNet + Config: configs/retinanet/retinanet_r50_caffe_fpn_1x_coco.py + Metadata: + Training Memory (GB): 3.5 + inference time (ms/im): + - value: 53.76 + hardware: V100 + backend: PyTorch + batch size: 1 + mode: FP32 + resolution: (800, 1333) + Epochs: 12 + Results: + - Task: Object Detection + Dataset: COCO + Metrics: + box AP: 36.3 + Weights: https://download.openmmlab.com/mmdetection/v2.0/retinanet/retinanet_r50_caffe_fpn_1x_coco/retinanet_r50_caffe_fpn_1x_coco_20200531-f11027c5.pth + + - Name: retinanet_r50_fpn_1x_coco + In Collection: RetinaNet + Config: configs/retinanet/retinanet_r50_fpn_1x_coco.py + Metadata: + Training Memory (GB): 3.8 + inference time (ms/im): + - value: 52.63 + hardware: V100 + backend: PyTorch + batch size: 1 + mode: FP32 + resolution: (800, 1333) + Epochs: 12 + Results: + - Task: Object Detection + Dataset: COCO + Metrics: + box AP: 36.5 + Weights: https://download.openmmlab.com/mmdetection/v2.0/retinanet/retinanet_r50_fpn_1x_coco/retinanet_r50_fpn_1x_coco_20200130-c2398f9e.pth + + - Name: retinanet_r50_fpn_2x_coco + In Collection: RetinaNet + Config: configs/retinanet/retinanet_r50_fpn_2x_coco.py + Metadata: + Epochs: 24 + Results: + - Task: Object Detection + Dataset: COCO + Metrics: + box AP: 37.4 + Weights: https://download.openmmlab.com/mmdetection/v2.0/retinanet/retinanet_r50_fpn_2x_coco/retinanet_r50_fpn_2x_coco_20200131-fdb43119.pth + + - Name: retinanet_r50_fpn_mstrain_3x_coco + In Collection: RetinaNet + Config: configs/retinanet/retinanet_r50_fpn_mstrain_640-800_3x_coco.py + Metadata: + Epochs: 36 + Results: + - Task: Object Detection + Dataset: COCO + Metrics: + box AP: 39.5 + Weights: https://download.openmmlab.com/mmdetection/v2.0/retinanet/retinanet_r50_fpn_mstrain_3x_coco/retinanet_r50_fpn_mstrain_3x_coco_20210718_220633-88476508.pth + + - Name: retinanet_r101_caffe_fpn_1x_coco + In Collection: RetinaNet + Config: configs/retinanet/retinanet_r101_caffe_fpn_1x_coco.py + Metadata: + Training Memory (GB): 5.5 + inference time (ms/im): + - value: 68.03 + hardware: V100 + backend: PyTorch + batch size: 1 + mode: FP32 + resolution: (800, 1333) + Epochs: 12 + Results: + - Task: Object Detection + Dataset: COCO + Metrics: + box AP: 38.5 + Weights: https://download.openmmlab.com/mmdetection/v2.0/retinanet/retinanet_r101_caffe_fpn_1x_coco/retinanet_r101_caffe_fpn_1x_coco_20200531-b428fa0f.pth + + - Name: retinanet_r101_caffe_fpn_mstrain_3x_coco + In Collection: RetinaNet + Config: configs/retinanet/retinanet_r101_caffe_fpn_1x_coco.py + Metadata: + Epochs: 36 + Results: + - Task: Object Detection + Dataset: COCO + Metrics: + box AP: 40.7 + Weights: https://download.openmmlab.com/mmdetection/v2.0/retinanet/retinanet_r101_caffe_fpn_mstrain_3x_coco/retinanet_r101_caffe_fpn_mstrain_3x_coco_20210721_063439-88a8a944.pth + + - Name: retinanet_r101_fpn_1x_coco + In Collection: RetinaNet + Config: configs/retinanet/retinanet_r101_fpn_1x_coco.py + Metadata: + Training Memory (GB): 5.7 + inference time (ms/im): + - value: 66.67 + hardware: V100 + backend: PyTorch + batch size: 1 + mode: FP32 + resolution: (800, 1333) + Epochs: 12 + Results: + - Task: Object Detection + Dataset: COCO + Metrics: + box AP: 38.5 + Weights: https://download.openmmlab.com/mmdetection/v2.0/retinanet/retinanet_r101_fpn_1x_coco/retinanet_r101_fpn_1x_coco_20200130-7a93545f.pth + + - Name: retinanet_r101_fpn_2x_coco + In Collection: RetinaNet + Config: configs/retinanet/retinanet_r101_fpn_2x_coco.py + Metadata: + Training Memory (GB): 5.7 + inference time (ms/im): + - value: 66.67 + hardware: V100 + backend: PyTorch + batch size: 1 + mode: FP32 + resolution: (800, 1333) + Epochs: 24 + Results: + - Task: Object Detection + Dataset: COCO + Metrics: + box AP: 38.9 + Weights: https://download.openmmlab.com/mmdetection/v2.0/retinanet/retinanet_r101_fpn_2x_coco/retinanet_r101_fpn_2x_coco_20200131-5560aee8.pth + + - Name: retinanet_r101_fpn_mstrain_3x_coco + In Collection: RetinaNet + Config: configs/retinanet/retinanet_r101_fpn_2x_coco.py + Metadata: + Epochs: 36 + Results: + - Task: Object Detection + Dataset: COCO + Metrics: + box AP: 41 + Weights: https://download.openmmlab.com/mmdetection/v2.0/retinanet/retinanet_r101_fpn_mstrain_3x_coco/retinanet_r101_fpn_mstrain_3x_coco_20210720_214650-7ee888e0.pth + + + - Name: retinanet_x101_32x4d_fpn_1x_coco + In Collection: RetinaNet + Config: configs/retinanet/retinanet_x101_32x4d_fpn_1x_coco.py + Metadata: + Training Memory (GB): 7.0 + inference time (ms/im): + - value: 82.64 + hardware: V100 + backend: PyTorch + batch size: 1 + mode: FP32 + resolution: (800, 1333) + Epochs: 12 + Results: + - Task: Object Detection + Dataset: COCO + Metrics: + box AP: 39.9 + Weights: https://download.openmmlab.com/mmdetection/v2.0/retinanet/retinanet_x101_32x4d_fpn_1x_coco/retinanet_x101_32x4d_fpn_1x_coco_20200130-5c8b7ec4.pth + + - Name: retinanet_x101_32x4d_fpn_2x_coco + In Collection: RetinaNet + Config: configs/retinanet/retinanet_x101_32x4d_fpn_2x_coco.py + Metadata: + Training Memory (GB): 7.0 + inference time (ms/im): + - value: 82.64 + hardware: V100 + backend: PyTorch + batch size: 1 + mode: FP32 + resolution: (800, 1333) + Epochs: 24 + Results: + - Task: Object Detection + Dataset: COCO + Metrics: + box AP: 40.1 + Weights: https://download.openmmlab.com/mmdetection/v2.0/retinanet/retinanet_x101_32x4d_fpn_2x_coco/retinanet_x101_32x4d_fpn_2x_coco_20200131-237fc5e1.pth + + - Name: retinanet_x101_64x4d_fpn_1x_coco + In Collection: RetinaNet + Config: configs/retinanet/retinanet_x101_64x4d_fpn_1x_coco.py + Metadata: + Training Memory (GB): 10.0 + inference time (ms/im): + - value: 114.94 + hardware: V100 + backend: PyTorch + batch size: 1 + mode: FP32 + resolution: (800, 1333) + Epochs: 12 + Results: + - Task: Object Detection + Dataset: COCO + Metrics: + box AP: 41.0 + Weights: https://download.openmmlab.com/mmdetection/v2.0/retinanet/retinanet_x101_64x4d_fpn_1x_coco/retinanet_x101_64x4d_fpn_1x_coco_20200130-366f5af1.pth + + - Name: retinanet_x101_64x4d_fpn_2x_coco + In Collection: RetinaNet + Config: configs/retinanet/retinanet_x101_64x4d_fpn_2x_coco.py + Metadata: + Training Memory (GB): 10.0 + inference time (ms/im): + - value: 114.94 + hardware: V100 + backend: PyTorch + batch size: 1 + mode: FP32 + resolution: (800, 1333) + Epochs: 24 + Results: + - Task: Object Detection + Dataset: COCO + Metrics: + box AP: 40.8 + Weights: https://download.openmmlab.com/mmdetection/v2.0/retinanet/retinanet_x101_64x4d_fpn_2x_coco/retinanet_x101_64x4d_fpn_2x_coco_20200131-bca068ab.pth + + - Name: retinanet_x101_64x4d_fpn_mstrain_3x_coco + In Collection: RetinaNet + Config: configs/retinanet/retinanet_x101_64x4d_fpn_mstrain_640-800_3x_coco.py + Metadata: + Epochs: 36 + Results: + - Task: Object Detection + Dataset: COCO + Metrics: + box AP: 41.6 + Weights: https://download.openmmlab.com/mmdetection/v2.0/retinanet/retinanet_x101_64x4d_fpn_mstrain_3x_coco/retinanet_x101_64x4d_fpn_mstrain_3x_coco_20210719_051838-022c2187.pth diff --git a/configs/retinanet/retinanet_r101_caffe_fpn_1x_coco.py b/configs/retinanet/retinanet_r101_caffe_fpn_1x_coco.py index 21d227b..56eaae2 100644 --- a/configs/retinanet/retinanet_r101_caffe_fpn_1x_coco.py +++ b/configs/retinanet/retinanet_r101_caffe_fpn_1x_coco.py @@ -1,4 +1,7 @@ _base_ = './retinanet_r50_caffe_fpn_1x_coco.py' model = dict( - pretrained='open-mmlab://detectron2/resnet101_caffe', - backbone=dict(depth=101)) + backbone=dict( + depth=101, + init_cfg=dict( + type='Pretrained', + checkpoint='open-mmlab://detectron2/resnet101_caffe'))) diff --git a/configs/retinanet/retinanet_r101_caffe_fpn_mstrain_3x_coco.py b/configs/retinanet/retinanet_r101_caffe_fpn_mstrain_3x_coco.py new file mode 100644 index 0000000..b87295e --- /dev/null +++ b/configs/retinanet/retinanet_r101_caffe_fpn_mstrain_3x_coco.py @@ -0,0 +1,7 @@ +_base_ = './retinanet_r50_caffe_fpn_mstrain_1x_coco.py' +# learning policy +model = dict( + pretrained='open-mmlab://detectron2/resnet101_caffe', + backbone=dict(depth=101)) +lr_config = dict(step=[28, 34]) +runner = dict(type='EpochBasedRunner', max_epochs=36) diff --git a/configs/retinanet/retinanet_r101_fpn_1x_coco.py b/configs/retinanet/retinanet_r101_fpn_1x_coco.py index 1e6f463..a7f0600 100644 --- a/configs/retinanet/retinanet_r101_fpn_1x_coco.py +++ b/configs/retinanet/retinanet_r101_fpn_1x_coco.py @@ -1,2 +1,6 @@ _base_ = './retinanet_r50_fpn_1x_coco.py' -model = dict(pretrained='torchvision://resnet101', backbone=dict(depth=101)) +model = dict( + backbone=dict( + depth=101, + init_cfg=dict(type='Pretrained', + checkpoint='torchvision://resnet101'))) diff --git a/configs/retinanet/retinanet_r101_fpn_2x_coco.py b/configs/retinanet/retinanet_r101_fpn_2x_coco.py index c12088a..721112a 100644 --- a/configs/retinanet/retinanet_r101_fpn_2x_coco.py +++ b/configs/retinanet/retinanet_r101_fpn_2x_coco.py @@ -1,2 +1,6 @@ _base_ = './retinanet_r50_fpn_2x_coco.py' -model = dict(pretrained='torchvision://resnet101', backbone=dict(depth=101)) +model = dict( + backbone=dict( + depth=101, + init_cfg=dict(type='Pretrained', + checkpoint='torchvision://resnet101'))) diff --git a/configs/retinanet/retinanet_r101_fpn_mstrain_640-800_3x_coco.py b/configs/retinanet/retinanet_r101_fpn_mstrain_640-800_3x_coco.py new file mode 100644 index 0000000..6bbcac4 --- /dev/null +++ b/configs/retinanet/retinanet_r101_fpn_mstrain_640-800_3x_coco.py @@ -0,0 +1,6 @@ +_base_ = [ + '../_base_/models/retinanet_r50_fpn.py', '../common/mstrain_3x_coco.py' +] +# optimizer +model = dict(pretrained='torchvision://resnet101', backbone=dict(depth=101)) +optimizer = dict(type='SGD', lr=0.01, momentum=0.9, weight_decay=0.0001) diff --git a/configs/retinanet/retinanet_r50_caffe_fpn_1x_coco.py b/configs/retinanet/retinanet_r50_caffe_fpn_1x_coco.py index 028c1a3..04c9af5 100644 --- a/configs/retinanet/retinanet_r50_caffe_fpn_1x_coco.py +++ b/configs/retinanet/retinanet_r50_caffe_fpn_1x_coco.py @@ -1,8 +1,12 @@ _base_ = './retinanet_r50_fpn_1x_coco.py' model = dict( - pretrained='open-mmlab://detectron2/resnet50_caffe', backbone=dict( - norm_cfg=dict(requires_grad=False), norm_eval=True, style='caffe')) + norm_cfg=dict(requires_grad=False), + norm_eval=True, + style='caffe', + init_cfg=dict( + type='Pretrained', + checkpoint='open-mmlab://detectron2/resnet50_caffe'))) # use caffe img_norm img_norm_cfg = dict( mean=[103.530, 116.280, 123.675], std=[1.0, 1.0, 1.0], to_rgb=False) diff --git a/configs/retinanet/retinanet_r50_caffe_fpn_mstrain_1x_coco.py b/configs/retinanet/retinanet_r50_caffe_fpn_mstrain_1x_coco.py index f2a0dec..4d7b8f2 100644 --- a/configs/retinanet/retinanet_r50_caffe_fpn_mstrain_1x_coco.py +++ b/configs/retinanet/retinanet_r50_caffe_fpn_mstrain_1x_coco.py @@ -1,8 +1,12 @@ _base_ = './retinanet_r50_fpn_1x_coco.py' model = dict( - pretrained='open-mmlab://detectron2/resnet50_caffe', backbone=dict( - norm_cfg=dict(requires_grad=False), norm_eval=True, style='caffe')) + norm_cfg=dict(requires_grad=False), + norm_eval=True, + style='caffe', + init_cfg=dict( + type='Pretrained', + checkpoint='open-mmlab://detectron2/resnet50_caffe'))) # use caffe img_norm img_norm_cfg = dict( mean=[103.530, 116.280, 123.675], std=[1.0, 1.0, 1.0], to_rgb=False) diff --git a/configs/retinanet/retinanet_r50_fpn_mstrain_640-800_3x_coco.py b/configs/retinanet/retinanet_r50_fpn_mstrain_640-800_3x_coco.py new file mode 100644 index 0000000..02a2c29 --- /dev/null +++ b/configs/retinanet/retinanet_r50_fpn_mstrain_640-800_3x_coco.py @@ -0,0 +1,5 @@ +_base_ = [ + '../_base_/models/retinanet_r50_fpn.py', '../common/mstrain_3x_coco.py' +] +# optimizer +optimizer = dict(type='SGD', lr=0.01, momentum=0.9, weight_decay=0.0001) diff --git a/configs/retinanet/retinanet_x101_32x4d_fpn_1x_coco.py b/configs/retinanet/retinanet_x101_32x4d_fpn_1x_coco.py index 9927f8f..765a4c2 100644 --- a/configs/retinanet/retinanet_x101_32x4d_fpn_1x_coco.py +++ b/configs/retinanet/retinanet_x101_32x4d_fpn_1x_coco.py @@ -1,6 +1,5 @@ _base_ = './retinanet_r50_fpn_1x_coco.py' model = dict( - pretrained='open-mmlab://resnext101_32x4d', backbone=dict( type='ResNeXt', depth=101, @@ -10,4 +9,6 @@ out_indices=(0, 1, 2, 3), frozen_stages=1, norm_cfg=dict(type='BN', requires_grad=True), - style='pytorch')) + style='pytorch', + init_cfg=dict( + type='Pretrained', checkpoint='open-mmlab://resnext101_32x4d'))) diff --git a/configs/retinanet/retinanet_x101_32x4d_fpn_2x_coco.py b/configs/retinanet/retinanet_x101_32x4d_fpn_2x_coco.py index cd78b6d..14de96f 100644 --- a/configs/retinanet/retinanet_x101_32x4d_fpn_2x_coco.py +++ b/configs/retinanet/retinanet_x101_32x4d_fpn_2x_coco.py @@ -1,6 +1,5 @@ _base_ = './retinanet_r50_fpn_2x_coco.py' model = dict( - pretrained='open-mmlab://resnext101_32x4d', backbone=dict( type='ResNeXt', depth=101, @@ -10,4 +9,6 @@ out_indices=(0, 1, 2, 3), frozen_stages=1, norm_cfg=dict(type='BN', requires_grad=True), - style='pytorch')) + style='pytorch', + init_cfg=dict( + type='Pretrained', checkpoint='open-mmlab://resnext101_32x4d'))) diff --git a/configs/retinanet/retinanet_x101_64x4d_fpn_1x_coco.py b/configs/retinanet/retinanet_x101_64x4d_fpn_1x_coco.py index cc40f26..948cd18 100644 --- a/configs/retinanet/retinanet_x101_64x4d_fpn_1x_coco.py +++ b/configs/retinanet/retinanet_x101_64x4d_fpn_1x_coco.py @@ -1,6 +1,5 @@ _base_ = './retinanet_r50_fpn_1x_coco.py' model = dict( - pretrained='open-mmlab://resnext101_64x4d', backbone=dict( type='ResNeXt', depth=101, @@ -10,4 +9,6 @@ out_indices=(0, 1, 2, 3), frozen_stages=1, norm_cfg=dict(type='BN', requires_grad=True), - style='pytorch')) + style='pytorch', + init_cfg=dict( + type='Pretrained', checkpoint='open-mmlab://resnext101_64x4d'))) diff --git a/configs/retinanet/retinanet_x101_64x4d_fpn_2x_coco.py b/configs/retinanet/retinanet_x101_64x4d_fpn_2x_coco.py index eac05a6..ad04b6e 100644 --- a/configs/retinanet/retinanet_x101_64x4d_fpn_2x_coco.py +++ b/configs/retinanet/retinanet_x101_64x4d_fpn_2x_coco.py @@ -1,6 +1,5 @@ _base_ = './retinanet_r50_fpn_2x_coco.py' model = dict( - pretrained='open-mmlab://resnext101_64x4d', backbone=dict( type='ResNeXt', depth=101, @@ -10,4 +9,6 @@ out_indices=(0, 1, 2, 3), frozen_stages=1, norm_cfg=dict(type='BN', requires_grad=True), - style='pytorch')) + style='pytorch', + init_cfg=dict( + type='Pretrained', checkpoint='open-mmlab://resnext101_64x4d'))) diff --git a/configs/retinanet/retinanet_x101_64x4d_fpn_mstrain_640-800_3x_coco.py b/configs/retinanet/retinanet_x101_64x4d_fpn_mstrain_640-800_3x_coco.py new file mode 100644 index 0000000..f6ab512 --- /dev/null +++ b/configs/retinanet/retinanet_x101_64x4d_fpn_mstrain_640-800_3x_coco.py @@ -0,0 +1,8 @@ +_base_ = [ + '../_base_/models/retinanet_r50_fpn.py', '../common/mstrain_3x_coco.py' +] +# optimizer +model = dict( + pretrained='open-mmlab://resnext101_64x4d', + backbone=dict(type='ResNeXt', depth=101, groups=64, base_width=4)) +optimizer = dict(type='SGD', lr=0.01) diff --git a/configs/rpn/README.md b/configs/rpn/README.md index 4f6f712..03b293d 100644 --- a/configs/rpn/README.md +++ b/configs/rpn/README.md @@ -2,7 +2,7 @@ ## Introduction -[ALGORITHM] + ```latex @inproceedings{ren2015faster, @@ -17,13 +17,13 @@ | Backbone | Style | Lr schd | Mem (GB) | Inf time (fps) | AR1000 | Config | Download | | :-------------: | :-----: | :-----: | :------: | :------------: | :----: | :------: | :--------: | -| R-50-FPN | caffe | 1x | 3.5 | 22.6 | 58.7 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/rpn/rpn_r50_caffe_fpn_1x_coco.py) | [model](http://download.openmmlab.com/mmdetection/v2.0/rpn/rpn_r50_caffe_fpn_1x_coco/rpn_r50_caffe_fpn_1x_coco_20200531-5b903a37.pth) | [log](http://download.openmmlab.com/mmdetection/v2.0/rpn/rpn_r50_caffe_fpn_1x_coco/rpn_r50_caffe_fpn_1x_coco_20200531_012334.log.json) | -| R-50-FPN | pytorch | 1x | 3.8 | 22.3 | 58.2 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/rpn/rpn_r50_fpn_1x_coco.py) | [model](http://download.openmmlab.com/mmdetection/v2.0/rpn/rpn_r50_fpn_1x_coco/rpn_r50_fpn_1x_coco_20200218-5525fa2e.pth) | [log](http://download.openmmlab.com/mmdetection/v2.0/rpn/rpn_r50_fpn_1x_coco/rpn_r50_fpn_1x_coco_20200218_151240.log.json) | -| R-50-FPN | pytorch | 2x | - | - | 58.6 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/rpn/rpn_r50_fpn_2x_coco.py) | [model](http://download.openmmlab.com/mmdetection/v2.0/rpn/rpn_r50_fpn_2x_coco/rpn_r50_fpn_2x_coco_20200131-0728c9b3.pth) | [log](http://download.openmmlab.com/mmdetection/v2.0/rpn/rpn_r50_fpn_2x_coco/rpn_r50_fpn_2x_coco_20200131_190631.log.json) | -| R-101-FPN | caffe | 1x | 5.4 | 17.3 | 60.0 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/rpn/rpn_r101_caffe_fpn_1x_coco.py) | [model](http://download.openmmlab.com/mmdetection/v2.0/rpn/rpn_r101_caffe_fpn_1x_coco/rpn_r101_caffe_fpn_1x_coco_20200531-0629a2e2.pth) | [log](http://download.openmmlab.com/mmdetection/v2.0/rpn/rpn_r101_caffe_fpn_1x_coco/rpn_r101_caffe_fpn_1x_coco_20200531_012345.log.json) | -| R-101-FPN | pytorch | 1x | 5.8 | 16.5 | 59.7 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/rpn/rpn_r101_fpn_1x_coco.py) | [model](http://download.openmmlab.com/mmdetection/v2.0/rpn/rpn_r101_fpn_1x_coco/rpn_r101_fpn_1x_coco_20200131-2ace2249.pth) | [log](http://download.openmmlab.com/mmdetection/v2.0/rpn/rpn_r101_fpn_1x_coco/rpn_r101_fpn_1x_coco_20200131_191000.log.json) | -| R-101-FPN | pytorch | 2x | - | - | 60.2 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/rpn/rpn_r101_fpn_2x_coco.py) | [model](http://download.openmmlab.com/mmdetection/v2.0/rpn/rpn_r101_fpn_2x_coco/rpn_r101_fpn_2x_coco_20200131-24e3db1a.pth) | [log](http://download.openmmlab.com/mmdetection/v2.0/rpn/rpn_r101_fpn_2x_coco/rpn_r101_fpn_2x_coco_20200131_191106.log.json) | -| X-101-32x4d-FPN | pytorch | 1x | 7.0 | 13.0 | 60.6 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/rpn/rpn_x101_32x4d_fpn_1x_coco.py) | [model](http://download.openmmlab.com/mmdetection/v2.0/rpn/rpn_x101_32x4d_fpn_1x_coco/rpn_x101_32x4d_fpn_1x_coco_20200219-b02646c6.pth) | [log](http://download.openmmlab.com/mmdetection/v2.0/rpn/rpn_x101_32x4d_fpn_1x_coco/rpn_x101_32x4d_fpn_1x_coco_20200219_012037.log.json) | -| X-101-32x4d-FPN | pytorch | 2x | - | - | 61.1 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/rpn/rpn_x101_32x4d_fpn_2x_coco.py) | [model](http://download.openmmlab.com/mmdetection/v2.0/rpn/rpn_x101_32x4d_fpn_2x_coco/rpn_x101_32x4d_fpn_2x_coco_20200208-d22bd0bb.pth) | [log](http://download.openmmlab.com/mmdetection/v2.0/rpn/rpn_x101_32x4d_fpn_2x_coco/rpn_x101_32x4d_fpn_2x_coco_20200208_200752.log.json) | -| X-101-64x4d-FPN | pytorch | 1x | 10.1 | 9.1 | 61.0 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/rpn/rpn_x101_64x4d_fpn_1x_coco.py) | [model](http://download.openmmlab.com/mmdetection/v2.0/rpn/rpn_x101_64x4d_fpn_1x_coco/rpn_x101_64x4d_fpn_1x_coco_20200208-cde6f7dd.pth) | [log](http://download.openmmlab.com/mmdetection/v2.0/rpn/rpn_x101_64x4d_fpn_1x_coco/rpn_x101_64x4d_fpn_1x_coco_20200208_200752.log.json) | -| X-101-64x4d-FPN | pytorch | 2x | - | - | 61.5 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/rpn/rpn_x101_64x4d_fpn_2x_coco.py) | [model](http://download.openmmlab.com/mmdetection/v2.0/rpn/rpn_x101_64x4d_fpn_2x_coco/rpn_x101_64x4d_fpn_2x_coco_20200208-c65f524f.pth) | [log](http://download.openmmlab.com/mmdetection/v2.0/rpn/rpn_x101_64x4d_fpn_2x_coco/rpn_x101_64x4d_fpn_2x_coco_20200208_200752.log.json) | +| R-50-FPN | caffe | 1x | 3.5 | 22.6 | 58.7 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/rpn/rpn_r50_caffe_fpn_1x_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/rpn/rpn_r50_caffe_fpn_1x_coco/rpn_r50_caffe_fpn_1x_coco_20200531-5b903a37.pth) | [log](https://download.openmmlab.com/mmdetection/v2.0/rpn/rpn_r50_caffe_fpn_1x_coco/rpn_r50_caffe_fpn_1x_coco_20200531_012334.log.json) | +| R-50-FPN | pytorch | 1x | 3.8 | 22.3 | 58.2 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/rpn/rpn_r50_fpn_1x_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/rpn/rpn_r50_fpn_1x_coco/rpn_r50_fpn_1x_coco_20200218-5525fa2e.pth) | [log](https://download.openmmlab.com/mmdetection/v2.0/rpn/rpn_r50_fpn_1x_coco/rpn_r50_fpn_1x_coco_20200218_151240.log.json) | +| R-50-FPN | pytorch | 2x | - | - | 58.6 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/rpn/rpn_r50_fpn_2x_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/rpn/rpn_r50_fpn_2x_coco/rpn_r50_fpn_2x_coco_20200131-0728c9b3.pth) | [log](https://download.openmmlab.com/mmdetection/v2.0/rpn/rpn_r50_fpn_2x_coco/rpn_r50_fpn_2x_coco_20200131_190631.log.json) | +| R-101-FPN | caffe | 1x | 5.4 | 17.3 | 60.0 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/rpn/rpn_r101_caffe_fpn_1x_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/rpn/rpn_r101_caffe_fpn_1x_coco/rpn_r101_caffe_fpn_1x_coco_20200531-0629a2e2.pth) | [log](https://download.openmmlab.com/mmdetection/v2.0/rpn/rpn_r101_caffe_fpn_1x_coco/rpn_r101_caffe_fpn_1x_coco_20200531_012345.log.json) | +| R-101-FPN | pytorch | 1x | 5.8 | 16.5 | 59.7 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/rpn/rpn_r101_fpn_1x_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/rpn/rpn_r101_fpn_1x_coco/rpn_r101_fpn_1x_coco_20200131-2ace2249.pth) | [log](https://download.openmmlab.com/mmdetection/v2.0/rpn/rpn_r101_fpn_1x_coco/rpn_r101_fpn_1x_coco_20200131_191000.log.json) | +| R-101-FPN | pytorch | 2x | - | - | 60.2 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/rpn/rpn_r101_fpn_2x_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/rpn/rpn_r101_fpn_2x_coco/rpn_r101_fpn_2x_coco_20200131-24e3db1a.pth) | [log](https://download.openmmlab.com/mmdetection/v2.0/rpn/rpn_r101_fpn_2x_coco/rpn_r101_fpn_2x_coco_20200131_191106.log.json) | +| X-101-32x4d-FPN | pytorch | 1x | 7.0 | 13.0 | 60.6 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/rpn/rpn_x101_32x4d_fpn_1x_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/rpn/rpn_x101_32x4d_fpn_1x_coco/rpn_x101_32x4d_fpn_1x_coco_20200219-b02646c6.pth) | [log](https://download.openmmlab.com/mmdetection/v2.0/rpn/rpn_x101_32x4d_fpn_1x_coco/rpn_x101_32x4d_fpn_1x_coco_20200219_012037.log.json) | +| X-101-32x4d-FPN | pytorch | 2x | - | - | 61.1 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/rpn/rpn_x101_32x4d_fpn_2x_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/rpn/rpn_x101_32x4d_fpn_2x_coco/rpn_x101_32x4d_fpn_2x_coco_20200208-d22bd0bb.pth) | [log](https://download.openmmlab.com/mmdetection/v2.0/rpn/rpn_x101_32x4d_fpn_2x_coco/rpn_x101_32x4d_fpn_2x_coco_20200208_200752.log.json) | +| X-101-64x4d-FPN | pytorch | 1x | 10.1 | 9.1 | 61.0 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/rpn/rpn_x101_64x4d_fpn_1x_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/rpn/rpn_x101_64x4d_fpn_1x_coco/rpn_x101_64x4d_fpn_1x_coco_20200208-cde6f7dd.pth) | [log](https://download.openmmlab.com/mmdetection/v2.0/rpn/rpn_x101_64x4d_fpn_1x_coco/rpn_x101_64x4d_fpn_1x_coco_20200208_200752.log.json) | +| X-101-64x4d-FPN | pytorch | 2x | - | - | 61.5 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/rpn/rpn_x101_64x4d_fpn_2x_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/rpn/rpn_x101_64x4d_fpn_2x_coco/rpn_x101_64x4d_fpn_2x_coco_20200208-c65f524f.pth) | [log](https://download.openmmlab.com/mmdetection/v2.0/rpn/rpn_x101_64x4d_fpn_2x_coco/rpn_x101_64x4d_fpn_2x_coco_20200208_200752.log.json) | diff --git a/configs/rpn/rpn_r101_caffe_fpn_1x_coco.py b/configs/rpn/rpn_r101_caffe_fpn_1x_coco.py index e616fdf..27be946 100644 --- a/configs/rpn/rpn_r101_caffe_fpn_1x_coco.py +++ b/configs/rpn/rpn_r101_caffe_fpn_1x_coco.py @@ -1,4 +1,7 @@ _base_ = './rpn_r50_caffe_fpn_1x_coco.py' model = dict( - pretrained='open-mmlab://detectron2/resnet101_caffe', - backbone=dict(depth=101)) + backbone=dict( + depth=101, + init_cfg=dict( + type='Pretrained', + checkpoint='open-mmlab://detectron2/resnet101_caffe'))) diff --git a/configs/rpn/rpn_r101_fpn_1x_coco.py b/configs/rpn/rpn_r101_fpn_1x_coco.py index b2af611..962728f 100644 --- a/configs/rpn/rpn_r101_fpn_1x_coco.py +++ b/configs/rpn/rpn_r101_fpn_1x_coco.py @@ -1,2 +1,6 @@ _base_ = './rpn_r50_fpn_1x_coco.py' -model = dict(pretrained='torchvision://resnet101', backbone=dict(depth=101)) +model = dict( + backbone=dict( + depth=101, + init_cfg=dict(type='Pretrained', + checkpoint='torchvision://resnet101'))) diff --git a/configs/rpn/rpn_r101_fpn_2x_coco.py b/configs/rpn/rpn_r101_fpn_2x_coco.py index 6908d30..ac7671c 100644 --- a/configs/rpn/rpn_r101_fpn_2x_coco.py +++ b/configs/rpn/rpn_r101_fpn_2x_coco.py @@ -1,2 +1,6 @@ _base_ = './rpn_r50_fpn_2x_coco.py' -model = dict(pretrained='torchvision://resnet101', backbone=dict(depth=101)) +model = dict( + backbone=dict( + depth=101, + init_cfg=dict(type='Pretrained', + checkpoint='torchvision://resnet101'))) diff --git a/configs/rpn/rpn_r50_caffe_fpn_1x_coco.py b/configs/rpn/rpn_r50_caffe_fpn_1x_coco.py index 398f3c1..68c36fa 100644 --- a/configs/rpn/rpn_r50_caffe_fpn_1x_coco.py +++ b/configs/rpn/rpn_r50_caffe_fpn_1x_coco.py @@ -1,8 +1,12 @@ _base_ = './rpn_r50_fpn_1x_coco.py' model = dict( - pretrained='open-mmlab://detectron2/resnet50_caffe', backbone=dict( - norm_cfg=dict(requires_grad=False), norm_eval=True, style='caffe')) + norm_cfg=dict(requires_grad=False), + norm_eval=True, + style='caffe', + init_cfg=dict( + type='Pretrained', + checkpoint='open-mmlab://detectron2/resnet50_caffe'))) # use caffe img_norm img_norm_cfg = dict( mean=[103.530, 116.280, 123.675], std=[1.0, 1.0, 1.0], to_rgb=False) diff --git a/configs/rpn/rpn_x101_32x4d_fpn_1x_coco.py b/configs/rpn/rpn_x101_32x4d_fpn_1x_coco.py index 83bd700..d0c7394 100644 --- a/configs/rpn/rpn_x101_32x4d_fpn_1x_coco.py +++ b/configs/rpn/rpn_x101_32x4d_fpn_1x_coco.py @@ -1,6 +1,5 @@ _base_ = './rpn_r50_fpn_1x_coco.py' model = dict( - pretrained='open-mmlab://resnext101_32x4d', backbone=dict( type='ResNeXt', depth=101, @@ -10,4 +9,6 @@ out_indices=(0, 1, 2, 3), frozen_stages=1, norm_cfg=dict(type='BN', requires_grad=True), - style='pytorch')) + style='pytorch', + init_cfg=dict( + type='Pretrained', checkpoint='open-mmlab://resnext101_32x4d'))) diff --git a/configs/rpn/rpn_x101_32x4d_fpn_2x_coco.py b/configs/rpn/rpn_x101_32x4d_fpn_2x_coco.py index 979afb9..c6880b7 100644 --- a/configs/rpn/rpn_x101_32x4d_fpn_2x_coco.py +++ b/configs/rpn/rpn_x101_32x4d_fpn_2x_coco.py @@ -1,6 +1,5 @@ _base_ = './rpn_r50_fpn_2x_coco.py' model = dict( - pretrained='open-mmlab://resnext101_32x4d', backbone=dict( type='ResNeXt', depth=101, @@ -10,4 +9,6 @@ out_indices=(0, 1, 2, 3), frozen_stages=1, norm_cfg=dict(type='BN', requires_grad=True), - style='pytorch')) + style='pytorch', + init_cfg=dict( + type='Pretrained', checkpoint='open-mmlab://resnext101_32x4d'))) diff --git a/configs/rpn/rpn_x101_64x4d_fpn_1x_coco.py b/configs/rpn/rpn_x101_64x4d_fpn_1x_coco.py index bb7f0a6..96e691a 100644 --- a/configs/rpn/rpn_x101_64x4d_fpn_1x_coco.py +++ b/configs/rpn/rpn_x101_64x4d_fpn_1x_coco.py @@ -1,6 +1,5 @@ _base_ = './rpn_r50_fpn_1x_coco.py' model = dict( - pretrained='open-mmlab://resnext101_64x4d', backbone=dict( type='ResNeXt', depth=101, @@ -10,4 +9,6 @@ out_indices=(0, 1, 2, 3), frozen_stages=1, norm_cfg=dict(type='BN', requires_grad=True), - style='pytorch')) + style='pytorch', + init_cfg=dict( + type='Pretrained', checkpoint='open-mmlab://resnext101_64x4d'))) diff --git a/configs/rpn/rpn_x101_64x4d_fpn_2x_coco.py b/configs/rpn/rpn_x101_64x4d_fpn_2x_coco.py index 8c766f0..4182a39 100644 --- a/configs/rpn/rpn_x101_64x4d_fpn_2x_coco.py +++ b/configs/rpn/rpn_x101_64x4d_fpn_2x_coco.py @@ -1,6 +1,5 @@ _base_ = './rpn_r50_fpn_2x_coco.py' model = dict( - pretrained='open-mmlab://resnext101_64x4d', backbone=dict( type='ResNeXt', depth=101, @@ -10,4 +9,6 @@ out_indices=(0, 1, 2, 3), frozen_stages=1, norm_cfg=dict(type='BN', requires_grad=True), - style='pytorch')) + style='pytorch', + init_cfg=dict( + type='Pretrained', checkpoint='open-mmlab://resnext101_64x4d'))) diff --git a/configs/sabl/README.md b/configs/sabl/README.md index 34b8367..c3bf169 100644 --- a/configs/sabl/README.md +++ b/configs/sabl/README.md @@ -2,7 +2,7 @@ ## Introduction -[ALGORITHM] + We provide config files to reproduce the object detection results in the ECCV 2020 Spotlight paper for [Side-Aware Boundary Localization for More Precise Object Detection](https://arxiv.org/abs/1912.04260). @@ -22,16 +22,16 @@ Single-scale testing (1333x800) is adopted in all results. | Method | Backbone | Lr schd | ms-train | box AP | Config | Download | | :----------------: | :-------: | :-----: | :------: | :----: | :----------------------------------------------------------------------------------------------------------------: | :---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------: | -| SABL Faster R-CNN | R-50-FPN | 1x | N | 39.9 | [config](https://github.com/open-mmlab/mmdetection/blob/master/configs/sabl/sabl_faster_rcnn_r50_fpn_1x_coco.py) | [model](http://download.openmmlab.com/mmdetection/v2.0/sabl/sabl_faster_rcnn_r50_fpn_1x_coco/sabl_faster_rcnn_r50_fpn_1x_coco-e867595b.pth) | [log](http://download.openmmlab.com/mmdetection/v2.0/sabl/sabl_faster_rcnn_r50_fpn_1x_coco/20200830_130324.log.json) | -| SABL Faster R-CNN | R-101-FPN | 1x | N | 41.7 | [config](https://github.com/open-mmlab/mmdetection/blob/master/configs/sabl/sabl_faster_rcnn_r101_fpn_1x_coco.py) | [model](http://download.openmmlab.com/mmdetection/v2.0/sabl/sabl_faster_rcnn_r101_fpn_1x_coco/sabl_faster_rcnn_r101_fpn_1x_coco-f804c6c1.pth) | [log](http://download.openmmlab.com/mmdetection/v2.0/sabl/sabl_faster_rcnn_r101_fpn_1x_coco/20200830_183949.log.json) | -| SABL Cascade R-CNN | R-50-FPN | 1x | N | 41.6 | [config](https://github.com/open-mmlab/mmdetection/blob/master/configs/sabl/sabl_cascade_rcnn_r50_fpn_1x_coco.py) | [model](http://download.openmmlab.com/mmdetection/v2.0/sabl/sabl_cascade_rcnn_r50_fpn_1x_coco/sabl_cascade_rcnn_r50_fpn_1x_coco-e1748e5e.pth) | [log](http://download.openmmlab.com/mmdetection/v2.0/sabl/sabl_cascade_rcnn_r50_fpn_1x_coco/20200831_033726.log.json) | -| SABL Cascade R-CNN | R-101-FPN | 1x | N | 43.0 | [config](https://github.com/open-mmlab/mmdetection/blob/master/configs/sabl/sabl_cascade_rcnn_r101_fpn_1x_coco.py) | [model](http://download.openmmlab.com/mmdetection/v2.0/sabl/sabl_cascade_rcnn_r101_fpn_1x_coco/sabl_cascade_rcnn_r101_fpn_1x_coco-2b83e87c.pth) | [log](http://download.openmmlab.com/mmdetection/v2.0/sabl/sabl_cascade_rcnn_r101_fpn_1x_coco/20200831_141745.log.json) | +| SABL Faster R-CNN | R-50-FPN | 1x | N | 39.9 | [config](https://github.com/open-mmlab/mmdetection/blob/master/configs/sabl/sabl_faster_rcnn_r50_fpn_1x_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/sabl/sabl_faster_rcnn_r50_fpn_1x_coco/sabl_faster_rcnn_r50_fpn_1x_coco-e867595b.pth) | [log](https://download.openmmlab.com/mmdetection/v2.0/sabl/sabl_faster_rcnn_r50_fpn_1x_coco/20200830_130324.log.json) | +| SABL Faster R-CNN | R-101-FPN | 1x | N | 41.7 | [config](https://github.com/open-mmlab/mmdetection/blob/master/configs/sabl/sabl_faster_rcnn_r101_fpn_1x_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/sabl/sabl_faster_rcnn_r101_fpn_1x_coco/sabl_faster_rcnn_r101_fpn_1x_coco-f804c6c1.pth) | [log](https://download.openmmlab.com/mmdetection/v2.0/sabl/sabl_faster_rcnn_r101_fpn_1x_coco/20200830_183949.log.json) | +| SABL Cascade R-CNN | R-50-FPN | 1x | N | 41.6 | [config](https://github.com/open-mmlab/mmdetection/blob/master/configs/sabl/sabl_cascade_rcnn_r50_fpn_1x_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/sabl/sabl_cascade_rcnn_r50_fpn_1x_coco/sabl_cascade_rcnn_r50_fpn_1x_coco-e1748e5e.pth) | [log](https://download.openmmlab.com/mmdetection/v2.0/sabl/sabl_cascade_rcnn_r50_fpn_1x_coco/20200831_033726.log.json) | +| SABL Cascade R-CNN | R-101-FPN | 1x | N | 43.0 | [config](https://github.com/open-mmlab/mmdetection/blob/master/configs/sabl/sabl_cascade_rcnn_r101_fpn_1x_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/sabl/sabl_cascade_rcnn_r101_fpn_1x_coco/sabl_cascade_rcnn_r101_fpn_1x_coco-2b83e87c.pth) | [log](https://download.openmmlab.com/mmdetection/v2.0/sabl/sabl_cascade_rcnn_r101_fpn_1x_coco/20200831_141745.log.json) | | Method | Backbone | GN | Lr schd | ms-train | box AP | Config | Download | | :------------: | :-------: | :---: | :-----: | :---------: | :----: | :---------------------------------------------------------------------------------------------------------------------------: | :------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------: | -| SABL RetinaNet | R-50-FPN | N | 1x | N | 37.7 | [config](https://github.com/open-mmlab/mmdetection/blob/master/configs/sabl/sabl_retinanet_r50_fpn_1x_coco.py) | [model](http://download.openmmlab.com/mmdetection/v2.0/sabl/sabl_retinanet_r50_fpn_1x_coco/sabl_retinanet_r50_fpn_1x_coco-6c54fd4f.pth) | [log](http://download.openmmlab.com/mmdetection/v2.0/sabl/sabl_retinanet_r50_fpn_1x_coco/20200830_053451.log.json) | -| SABL RetinaNet | R-50-FPN | Y | 1x | N | 38.8 | [config](https://github.com/open-mmlab/mmdetection/blob/master/configs/sabl/sabl_retinanet_r50_fpn_gn_1x_coco.py) | [model](http://download.openmmlab.com/mmdetection/v2.0/sabl/sabl_retinanet_r50_fpn_gn_1x_coco/sabl_retinanet_r50_fpn_gn_1x_coco-e16dfcf1.pth) | [log](http://download.openmmlab.com/mmdetection/v2.0/sabl/sabl_retinanet_r50_fpn_gn_1x_coco/20200831_141955.log.json) | -| SABL RetinaNet | R-101-FPN | N | 1x | N | 39.7 | [config](https://github.com/open-mmlab/mmdetection/blob/master/configs/sabl/sabl_retinanet_r101_fpn_1x_coco.py) | [model](http://download.openmmlab.com/mmdetection/v2.0/sabl/sabl_retinanet_r101_fpn_1x_coco/sabl_retinanet_r101_fpn_1x_coco-42026904.pth) | [log](http://download.openmmlab.com/mmdetection/v2.0/sabl/sabl_retinanet_r101_fpn_1x_coco/20200831_034256.log.json) | -| SABL RetinaNet | R-101-FPN | Y | 1x | N | 40.5 | [config](https://github.com/open-mmlab/mmdetection/blob/master/configs/sabl/sabl_retinanet_r101_fpn_gn_1x_coco.py) | [model](http://download.openmmlab.com/mmdetection/v2.0/sabl/sabl_retinanet_r101_fpn_gn_1x_coco/sabl_retinanet_r101_fpn_gn_1x_coco-40a893e8.pth) | [log](http://download.openmmlab.com/mmdetection/v2.0/sabl/sabl_retinanet_r101_fpn_gn_1x_coco/20200830_201422.log.json) | -| SABL RetinaNet | R-101-FPN | Y | 2x | Y (640~800) | 42.9 | [config](https://github.com/open-mmlab/mmdetection/blob/master/configs/sabl/sabl_retinanet_r101_fpn_gn_2x_ms_640_800_coco.py) | [model](http://download.openmmlab.com/mmdetection/v2.0/sabl/sabl_retinanet_r101_fpn_gn_2x_ms_640_800_coco/sabl_retinanet_r101_fpn_gn_2x_ms_640_800_coco-1e63382c.pth) | [log](http://download.openmmlab.com/mmdetection/v2.0/sabl/sabl_retinanet_r101_fpn_gn_2x_ms_640_800_coco/20200830_144807.log.json) | -| SABL RetinaNet | R-101-FPN | Y | 2x | Y (480~960) | 43.6 | [config](https://github.com/open-mmlab/mmdetection/blob/master/configs/sabl/sabl_retinanet_r101_fpn_gn_2x_ms_480_960_coco.py) | [model](http://download.openmmlab.com/mmdetection/v2.0/sabl/sabl_retinanet_r101_fpn_gn_2x_ms_480_960_coco/sabl_retinanet_r101_fpn_gn_2x_ms_480_960_coco-5342f857.pth) | [log](http://download.openmmlab.com/mmdetection/v2.0/sabl/sabl_retinanet_r101_fpn_gn_2x_ms_480_960_coco/20200830_164537.log.json) | +| SABL RetinaNet | R-50-FPN | N | 1x | N | 37.7 | [config](https://github.com/open-mmlab/mmdetection/blob/master/configs/sabl/sabl_retinanet_r50_fpn_1x_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/sabl/sabl_retinanet_r50_fpn_1x_coco/sabl_retinanet_r50_fpn_1x_coco-6c54fd4f.pth) | [log](https://download.openmmlab.com/mmdetection/v2.0/sabl/sabl_retinanet_r50_fpn_1x_coco/20200830_053451.log.json) | +| SABL RetinaNet | R-50-FPN | Y | 1x | N | 38.8 | [config](https://github.com/open-mmlab/mmdetection/blob/master/configs/sabl/sabl_retinanet_r50_fpn_gn_1x_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/sabl/sabl_retinanet_r50_fpn_gn_1x_coco/sabl_retinanet_r50_fpn_gn_1x_coco-e16dfcf1.pth) | [log](https://download.openmmlab.com/mmdetection/v2.0/sabl/sabl_retinanet_r50_fpn_gn_1x_coco/20200831_141955.log.json) | +| SABL RetinaNet | R-101-FPN | N | 1x | N | 39.7 | [config](https://github.com/open-mmlab/mmdetection/blob/master/configs/sabl/sabl_retinanet_r101_fpn_1x_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/sabl/sabl_retinanet_r101_fpn_1x_coco/sabl_retinanet_r101_fpn_1x_coco-42026904.pth) | [log](https://download.openmmlab.com/mmdetection/v2.0/sabl/sabl_retinanet_r101_fpn_1x_coco/20200831_034256.log.json) | +| SABL RetinaNet | R-101-FPN | Y | 1x | N | 40.5 | [config](https://github.com/open-mmlab/mmdetection/blob/master/configs/sabl/sabl_retinanet_r101_fpn_gn_1x_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/sabl/sabl_retinanet_r101_fpn_gn_1x_coco/sabl_retinanet_r101_fpn_gn_1x_coco-40a893e8.pth) | [log](https://download.openmmlab.com/mmdetection/v2.0/sabl/sabl_retinanet_r101_fpn_gn_1x_coco/20200830_201422.log.json) | +| SABL RetinaNet | R-101-FPN | Y | 2x | Y (640~800) | 42.9 | [config](https://github.com/open-mmlab/mmdetection/blob/master/configs/sabl/sabl_retinanet_r101_fpn_gn_2x_ms_640_800_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/sabl/sabl_retinanet_r101_fpn_gn_2x_ms_640_800_coco/sabl_retinanet_r101_fpn_gn_2x_ms_640_800_coco-1e63382c.pth) | [log](https://download.openmmlab.com/mmdetection/v2.0/sabl/sabl_retinanet_r101_fpn_gn_2x_ms_640_800_coco/20200830_144807.log.json) | +| SABL RetinaNet | R-101-FPN | Y | 2x | Y (480~960) | 43.6 | [config](https://github.com/open-mmlab/mmdetection/blob/master/configs/sabl/sabl_retinanet_r101_fpn_gn_2x_ms_480_960_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/sabl/sabl_retinanet_r101_fpn_gn_2x_ms_480_960_coco/sabl_retinanet_r101_fpn_gn_2x_ms_480_960_coco-5342f857.pth) | [log](https://download.openmmlab.com/mmdetection/v2.0/sabl/sabl_retinanet_r101_fpn_gn_2x_ms_480_960_coco/20200830_164537.log.json) | diff --git a/configs/sabl/metafile.yml b/configs/sabl/metafile.yml new file mode 100644 index 0000000..23c51cf --- /dev/null +++ b/configs/sabl/metafile.yml @@ -0,0 +1,140 @@ +Collections: + - Name: SABL + Metadata: + Training Data: COCO + Training Techniques: + - SGD with Momentum + - Weight Decay + Training Resources: 8x V100 GPUs + Architecture: + - FPN + - ResNet + - SABL + Paper: + URL: https://arxiv.org/abs/1912.04260 + Title: 'Side-Aware Boundary Localization for More Precise Object Detection' + README: configs/sabl/README.md + Code: + URL: https://github.com/open-mmlab/mmdetection/blob/v2.4.0/mmdet/models/roi_heads/bbox_heads/sabl_head.py#L14 + Version: v2.4.0 + +Models: + - Name: sabl_faster_rcnn_r50_fpn_1x_coco + In Collection: SABL + Config: configs/sabl/sabl_faster_rcnn_r50_fpn_1x_coco.py + Metadata: + Epochs: 12 + Results: + - Task: Object Detection + Dataset: COCO + Metrics: + box AP: 39.9 + Weights: https://download.openmmlab.com/mmdetection/v2.0/sabl/sabl_faster_rcnn_r50_fpn_1x_coco/sabl_faster_rcnn_r50_fpn_1x_coco-e867595b.pth + + - Name: sabl_faster_rcnn_r101_fpn_1x_coco + In Collection: SABL + Config: configs/sabl/sabl_faster_rcnn_r101_fpn_1x_coco.py + Metadata: + Epochs: 12 + Results: + - Task: Object Detection + Dataset: COCO + Metrics: + box AP: 41.7 + Weights: https://download.openmmlab.com/mmdetection/v2.0/sabl/sabl_faster_rcnn_r101_fpn_1x_coco/sabl_faster_rcnn_r101_fpn_1x_coco-f804c6c1.pth + + - Name: sabl_cascade_rcnn_r50_fpn_1x_coco + In Collection: SABL + Config: configs/sabl/sabl_cascade_rcnn_r50_fpn_1x_coco.py + Metadata: + Epochs: 12 + Results: + - Task: Object Detection + Dataset: COCO + Metrics: + box AP: 41.6 + Weights: https://download.openmmlab.com/mmdetection/v2.0/sabl/sabl_cascade_rcnn_r50_fpn_1x_coco/sabl_cascade_rcnn_r50_fpn_1x_coco-e1748e5e.pth + + - Name: sabl_cascade_rcnn_r101_fpn_1x_coco + In Collection: SABL + Config: configs/sabl/sabl_cascade_rcnn_r101_fpn_1x_coco.py + Metadata: + Epochs: 12 + Results: + - Task: Object Detection + Dataset: COCO + Metrics: + box AP: 43.0 + Weights: https://download.openmmlab.com/mmdetection/v2.0/sabl/sabl_cascade_rcnn_r101_fpn_1x_coco/sabl_cascade_rcnn_r101_fpn_1x_coco-2b83e87c.pth + + - Name: sabl_retinanet_r50_fpn_1x_coco + In Collection: SABL + Config: configs/sabl/sabl_retinanet_r50_fpn_1x_coco.py + Metadata: + Epochs: 12 + Results: + - Task: Object Detection + Dataset: COCO + Metrics: + box AP: 37.7 + Weights: https://download.openmmlab.com/mmdetection/v2.0/sabl/sabl_retinanet_r50_fpn_1x_coco/sabl_retinanet_r50_fpn_1x_coco-6c54fd4f.pth + + - Name: sabl_retinanet_r50_fpn_gn_1x_coco + In Collection: SABL + Config: configs/sabl/sabl_retinanet_r50_fpn_gn_1x_coco.py + Metadata: + Epochs: 12 + Results: + - Task: Object Detection + Dataset: COCO + Metrics: + box AP: 38.8 + Weights: https://download.openmmlab.com/mmdetection/v2.0/sabl/sabl_retinanet_r50_fpn_gn_1x_coco/sabl_retinanet_r50_fpn_gn_1x_coco-e16dfcf1.pth + + - Name: sabl_retinanet_r101_fpn_1x_coco + In Collection: SABL + Config: configs/sabl/sabl_retinanet_r101_fpn_1x_coco.py + Metadata: + Epochs: 12 + Results: + - Task: Object Detection + Dataset: COCO + Metrics: + box AP: 39.7 + Weights: https://download.openmmlab.com/mmdetection/v2.0/sabl/sabl_retinanet_r101_fpn_1x_coco/sabl_retinanet_r101_fpn_1x_coco-42026904.pth + + - Name: sabl_retinanet_r101_fpn_gn_1x_coco + In Collection: SABL + Config: configs/sabl/sabl_retinanet_r101_fpn_gn_1x_coco.py + Metadata: + Epochs: 12 + Results: + - Task: Object Detection + Dataset: COCO + Metrics: + box AP: 40.5 + Weights: https://download.openmmlab.com/mmdetection/v2.0/sabl/sabl_retinanet_r101_fpn_gn_1x_coco/sabl_retinanet_r101_fpn_gn_1x_coco-40a893e8.pth + + - Name: sabl_retinanet_r101_fpn_gn_2x_ms_640_800_coco + In Collection: SABL + Config: configs/sabl/sabl_retinanet_r101_fpn_gn_2x_ms_640_800_coco.py + Metadata: + Epochs: 24 + Results: + - Task: Object Detection + Dataset: COCO + Metrics: + box AP: 42.9 + Weights: https://download.openmmlab.com/mmdetection/v2.0/sabl/sabl_retinanet_r101_fpn_gn_2x_ms_640_800_coco/sabl_retinanet_r101_fpn_gn_2x_ms_640_800_coco-1e63382c.pth + + - Name: sabl_retinanet_r101_fpn_gn_2x_ms_480_960_coco + In Collection: SABL + Config: configs/sabl/sabl_retinanet_r101_fpn_gn_2x_ms_480_960_coco.py + Metadata: + Epochs: 24 + Results: + - Task: Object Detection + Dataset: COCO + Metrics: + box AP: 43.6 + Weights: https://download.openmmlab.com/mmdetection/v2.0/sabl/sabl_retinanet_r101_fpn_gn_2x_ms_480_960_coco/sabl_retinanet_r101_fpn_gn_2x_ms_480_960_coco-5342f857.pth diff --git a/configs/sabl/sabl_cascade_rcnn_r101_fpn_1x_coco.py b/configs/sabl/sabl_cascade_rcnn_r101_fpn_1x_coco.py index 0322006..64fe230 100644 --- a/configs/sabl/sabl_cascade_rcnn_r101_fpn_1x_coco.py +++ b/configs/sabl/sabl_cascade_rcnn_r101_fpn_1x_coco.py @@ -5,8 +5,10 @@ ] # model settings model = dict( - pretrained='torchvision://resnet101', - backbone=dict(depth=101), + backbone=dict( + depth=101, + init_cfg=dict(type='Pretrained', + checkpoint='torchvision://resnet101')), roi_head=dict(bbox_head=[ dict( type='SABLHead', diff --git a/configs/sabl/sabl_faster_rcnn_r101_fpn_1x_coco.py b/configs/sabl/sabl_faster_rcnn_r101_fpn_1x_coco.py index 4c797ca..e48d425 100644 --- a/configs/sabl/sabl_faster_rcnn_r101_fpn_1x_coco.py +++ b/configs/sabl/sabl_faster_rcnn_r101_fpn_1x_coco.py @@ -4,8 +4,10 @@ '../_base_/schedules/schedule_1x.py', '../_base_/default_runtime.py' ] model = dict( - pretrained='torchvision://resnet101', - backbone=dict(depth=101), + backbone=dict( + depth=101, + init_cfg=dict(type='Pretrained', + checkpoint='torchvision://resnet101')), roi_head=dict( bbox_head=dict( _delete_=True, diff --git a/configs/sabl/sabl_retinanet_r101_fpn_1x_coco.py b/configs/sabl/sabl_retinanet_r101_fpn_1x_coco.py index ed3a96c..b08e916 100644 --- a/configs/sabl/sabl_retinanet_r101_fpn_1x_coco.py +++ b/configs/sabl/sabl_retinanet_r101_fpn_1x_coco.py @@ -5,8 +5,10 @@ ] # model settings model = dict( - pretrained='torchvision://resnet101', - backbone=dict(depth=101), + backbone=dict( + depth=101, + init_cfg=dict(type='Pretrained', + checkpoint='torchvision://resnet101')), bbox_head=dict( _delete_=True, type='SABLRetinaHead', diff --git a/configs/sabl/sabl_retinanet_r101_fpn_gn_1x_coco.py b/configs/sabl/sabl_retinanet_r101_fpn_gn_1x_coco.py index ec78263..fc30d63 100644 --- a/configs/sabl/sabl_retinanet_r101_fpn_gn_1x_coco.py +++ b/configs/sabl/sabl_retinanet_r101_fpn_gn_1x_coco.py @@ -6,8 +6,10 @@ # model settings norm_cfg = dict(type='GN', num_groups=32, requires_grad=True) model = dict( - pretrained='torchvision://resnet101', - backbone=dict(depth=101), + backbone=dict( + depth=101, + init_cfg=dict(type='Pretrained', + checkpoint='torchvision://resnet101')), bbox_head=dict( _delete_=True, type='SABLRetinaHead', diff --git a/configs/sabl/sabl_retinanet_r101_fpn_gn_2x_ms_480_960_coco.py b/configs/sabl/sabl_retinanet_r101_fpn_gn_2x_ms_480_960_coco.py index 2a47c60..e8fe166 100644 --- a/configs/sabl/sabl_retinanet_r101_fpn_gn_2x_ms_480_960_coco.py +++ b/configs/sabl/sabl_retinanet_r101_fpn_gn_2x_ms_480_960_coco.py @@ -6,8 +6,10 @@ # model settings norm_cfg = dict(type='GN', num_groups=32, requires_grad=True) model = dict( - pretrained='torchvision://resnet101', - backbone=dict(depth=101), + backbone=dict( + depth=101, + init_cfg=dict(type='Pretrained', + checkpoint='torchvision://resnet101')), bbox_head=dict( _delete_=True, type='SABLRetinaHead', diff --git a/configs/sabl/sabl_retinanet_r101_fpn_gn_2x_ms_640_800_coco.py b/configs/sabl/sabl_retinanet_r101_fpn_gn_2x_ms_640_800_coco.py index f26062f..30c4339 100644 --- a/configs/sabl/sabl_retinanet_r101_fpn_gn_2x_ms_640_800_coco.py +++ b/configs/sabl/sabl_retinanet_r101_fpn_gn_2x_ms_640_800_coco.py @@ -6,8 +6,10 @@ # model settings norm_cfg = dict(type='GN', num_groups=32, requires_grad=True) model = dict( - pretrained='torchvision://resnet101', - backbone=dict(depth=101), + backbone=dict( + depth=101, + init_cfg=dict(type='Pretrained', + checkpoint='torchvision://resnet101')), bbox_head=dict( _delete_=True, type='SABLRetinaHead', diff --git a/configs/scnet/README.md b/configs/scnet/README.md index 1749df0..3757be1 100644 --- a/configs/scnet/README.md +++ b/configs/scnet/README.md @@ -2,7 +2,7 @@ ## Introduction -[ALGORITHM] + We provide the code for reproducing experiment results of [SCNet](https://arxiv.org/abs/2012.10150). diff --git a/configs/scnet/metafile.yml b/configs/scnet/metafile.yml new file mode 100644 index 0000000..bcdf7d7 --- /dev/null +++ b/configs/scnet/metafile.yml @@ -0,0 +1,116 @@ +Collections: + - Name: SCNet + Metadata: + Training Data: COCO + Training Techniques: + - SGD with Momentum + - Weight Decay + Training Resources: 8x V100 GPUs + Architecture: + - FPN + - ResNet + - SCNet + Paper: + URL: https://arxiv.org/abs/2012.10150 + Title: 'SCNet: Training Inference Sample Consistency for Instance Segmentation' + README: configs/scnet/README.md + Code: + URL: https://github.com/open-mmlab/mmdetection/blob/v2.9.0/mmdet/models/detectors/scnet.py#L6 + Version: v2.9.0 + +Models: + - Name: scnet_r50_fpn_1x_coco + In Collection: SCNet + Config: configs/scnet/scnet_r50_fpn_1x_coco.py + Metadata: + Training Memory (GB): 7.0 + inference time (ms/im): + - value: 161.29 + hardware: V100 + backend: PyTorch + batch size: 1 + mode: FP32 + resolution: (800, 1333) + Epochs: 12 + Results: + - Task: Object Detection + Dataset: COCO + Metrics: + box AP: 43.5 + - Task: Instance Segmentation + Dataset: COCO + Metrics: + mask AP: 39.2 + Weights: https://drive.google.com/file/d/1K5_8-P0EC43WZFtoO3q9_JE-df8pEc7J/view?usp=sharing + + - Name: scnet_r50_fpn_20e_coco + In Collection: SCNet + Config: configs/scnet/scnet_r50_fpn_20e_coco.py + Metadata: + Training Memory (GB): 7.0 + inference time (ms/im): + - value: 161.29 + hardware: V100 + backend: PyTorch + batch size: 1 + mode: FP32 + resolution: (800, 1333) + Epochs: 20 + Results: + - Task: Object Detection + Dataset: COCO + Metrics: + box AP: 44.5 + - Task: Instance Segmentation + Dataset: COCO + Metrics: + mask AP: 40.0 + Weights: https://drive.google.com/file/d/15VGLCt5-IO5TbzB4Kw6ZyoF6QH0Q511A/view?usp=sharing + + - Name: scnet_r101_fpn_20e_coco + In Collection: SCNet + Config: configs/scnet/scnet_r101_fpn_20e_coco.py + Metadata: + Training Memory (GB): 8.9 + inference time (ms/im): + - value: 172.41 + hardware: V100 + backend: PyTorch + batch size: 1 + mode: FP32 + resolution: (800, 1333) + Epochs: 20 + Results: + - Task: Object Detection + Dataset: COCO + Metrics: + box AP: 45.8 + - Task: Instance Segmentation + Dataset: COCO + Metrics: + mask AP: 40.9 + Weights: https://drive.google.com/file/d/1aeCGHsOBdfIqVBnBPp0JUE_RSIau3583/view?usp=sharing + + - Name: scnet_x101_64x4d_fpn_20e_coco + In Collection: SCNet + Config: configs/scnet/scnet_x101_64x4d_fpn_20e_coco.py + Metadata: + Training Memory (GB): 13.2 + inference time (ms/im): + - value: 204.08 + hardware: V100 + backend: PyTorch + batch size: 1 + mode: FP32 + resolution: (800, 1333) + Epochs: 20 + Results: + - Task: Object Detection + Dataset: COCO + Metrics: + box AP: 47.5 + - Task: Instance Segmentation + Dataset: COCO + Metrics: + mask AP: 42.3 + Weights: https://drive.google.com/file/d/1YjgutUKz4TTPpqSWGKUTkZJ8_X-kyCfY/view?usp=sharing diff --git a/configs/scnet/scnet_r101_fpn_20e_coco.py b/configs/scnet/scnet_r101_fpn_20e_coco.py index cef0668..ebba529 100644 --- a/configs/scnet/scnet_r101_fpn_20e_coco.py +++ b/configs/scnet/scnet_r101_fpn_20e_coco.py @@ -1,2 +1,6 @@ _base_ = './scnet_r50_fpn_20e_coco.py' -model = dict(pretrained='torchvision://resnet101', backbone=dict(depth=101)) +model = dict( + backbone=dict( + depth=101, + init_cfg=dict(type='Pretrained', + checkpoint='torchvision://resnet101'))) diff --git a/configs/scnet/scnet_r50_fpn_1x_coco.py b/configs/scnet/scnet_r50_fpn_1x_coco.py index e4215a6..fe03b0d 100644 --- a/configs/scnet/scnet_r50_fpn_1x_coco.py +++ b/configs/scnet/scnet_r50_fpn_1x_coco.py @@ -94,8 +94,8 @@ in_channels=256, conv_out_channels=256, num_classes=183, - ignore_label=255, - loss_weight=0.2, + loss_seg=dict( + type='CrossEntropyLoss', ignore_index=255, loss_weight=0.2), conv_to_res=True), glbctx_head=dict( type='GlobalContextHead', diff --git a/configs/scnet/scnet_x101_64x4d_fpn_20e_coco.py b/configs/scnet/scnet_x101_64x4d_fpn_20e_coco.py index a0ff32b..1e54b03 100644 --- a/configs/scnet/scnet_x101_64x4d_fpn_20e_coco.py +++ b/configs/scnet/scnet_x101_64x4d_fpn_20e_coco.py @@ -1,6 +1,5 @@ _base_ = './scnet_r50_fpn_20e_coco.py' model = dict( - pretrained='open-mmlab://resnext101_64x4d', backbone=dict( type='ResNeXt', depth=101, @@ -11,4 +10,6 @@ frozen_stages=1, norm_cfg=dict(type='BN', requires_grad=True), norm_eval=True, - style='pytorch')) + style='pytorch', + init_cfg=dict( + type='Pretrained', checkpoint='open-mmlab://resnext101_64x4d'))) diff --git a/configs/scratch/README.md b/configs/scratch/README.md index a338dc5..6b30447 100644 --- a/configs/scratch/README.md +++ b/configs/scratch/README.md @@ -2,7 +2,7 @@ ## Introduction -[ALGORITHM] + ```latex @article{he2018rethinking, @@ -17,8 +17,8 @@ | Model | Backbone | Style | Lr schd | box AP | mask AP | Config | Download | |:------------:|:---------:|:-------:|:-------:|:------:|:-------:|:------:|:--------:| -| Faster R-CNN | R-50-FPN | pytorch | 6x | 40.7 | | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/scratch/faster_rcnn_r50_fpn_gn-all_scratch_6x_coco.py) | [model](http://download.openmmlab.com/mmdetection/v2.0/scratch/faster_rcnn_r50_fpn_gn-all_scratch_6x_coco/scratch_faster_rcnn_r50_fpn_gn_6x_bbox_mAP-0.407_20200201_193013-90813d01.pth) | [log](http://download.openmmlab.com/mmdetection/v2.0/scratch/faster_rcnn_r50_fpn_gn-all_scratch_6x_coco/scratch_faster_rcnn_r50_fpn_gn_6x_20200201_193013.log.json) | -| Mask R-CNN | R-50-FPN | pytorch | 6x | 41.2 | 37.4 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/scratch/mask_rcnn_r50_fpn_gn-all_scratch_6x_coco.py) | [model](http://download.openmmlab.com/mmdetection/v2.0/scratch/mask_rcnn_r50_fpn_gn-all_scratch_6x_coco/scratch_mask_rcnn_r50_fpn_gn_6x_bbox_mAP-0.412__segm_mAP-0.374_20200201_193051-1e190a40.pth) | [log](http://download.openmmlab.com/mmdetection/v2.0/scratch/mask_rcnn_r50_fpn_gn-all_scratch_6x_coco/scratch_mask_rcnn_r50_fpn_gn_6x_20200201_193051.log.json) | +| Faster R-CNN | R-50-FPN | pytorch | 6x | 40.7 | | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/scratch/faster_rcnn_r50_fpn_gn-all_scratch_6x_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/scratch/faster_rcnn_r50_fpn_gn-all_scratch_6x_coco/scratch_faster_rcnn_r50_fpn_gn_6x_bbox_mAP-0.407_20200201_193013-90813d01.pth) | [log](https://download.openmmlab.com/mmdetection/v2.0/scratch/faster_rcnn_r50_fpn_gn-all_scratch_6x_coco/scratch_faster_rcnn_r50_fpn_gn_6x_20200201_193013.log.json) | +| Mask R-CNN | R-50-FPN | pytorch | 6x | 41.2 | 37.4 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/scratch/mask_rcnn_r50_fpn_gn-all_scratch_6x_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/scratch/mask_rcnn_r50_fpn_gn-all_scratch_6x_coco/scratch_mask_rcnn_r50_fpn_gn_6x_bbox_mAP-0.412__segm_mAP-0.374_20200201_193051-1e190a40.pth) | [log](https://download.openmmlab.com/mmdetection/v2.0/scratch/mask_rcnn_r50_fpn_gn-all_scratch_6x_coco/scratch_mask_rcnn_r50_fpn_gn_6x_20200201_193051.log.json) | Note: diff --git a/configs/scratch/faster_rcnn_r50_fpn_gn-all_scratch_6x_coco.py b/configs/scratch/faster_rcnn_r50_fpn_gn-all_scratch_6x_coco.py index 636f3f6..55aa3a6 100644 --- a/configs/scratch/faster_rcnn_r50_fpn_gn-all_scratch_6x_coco.py +++ b/configs/scratch/faster_rcnn_r50_fpn_gn-all_scratch_6x_coco.py @@ -5,9 +5,11 @@ ] norm_cfg = dict(type='GN', num_groups=32, requires_grad=True) model = dict( - pretrained=None, backbone=dict( - frozen_stages=-1, zero_init_residual=False, norm_cfg=norm_cfg), + frozen_stages=-1, + zero_init_residual=False, + norm_cfg=norm_cfg, + init_cfg=None), neck=dict(norm_cfg=norm_cfg), roi_head=dict( bbox_head=dict( diff --git a/configs/scratch/mask_rcnn_r50_fpn_gn-all_scratch_6x_coco.py b/configs/scratch/mask_rcnn_r50_fpn_gn-all_scratch_6x_coco.py index 6277a97..cc52cb8 100644 --- a/configs/scratch/mask_rcnn_r50_fpn_gn-all_scratch_6x_coco.py +++ b/configs/scratch/mask_rcnn_r50_fpn_gn-all_scratch_6x_coco.py @@ -5,9 +5,11 @@ ] norm_cfg = dict(type='GN', num_groups=32, requires_grad=True) model = dict( - pretrained=None, backbone=dict( - frozen_stages=-1, zero_init_residual=False, norm_cfg=norm_cfg), + frozen_stages=-1, + zero_init_residual=False, + norm_cfg=norm_cfg, + init_cfg=None), neck=dict(norm_cfg=norm_cfg), roi_head=dict( bbox_head=dict( diff --git a/configs/scratch/metafile.yml b/configs/scratch/metafile.yml new file mode 100644 index 0000000..65025fa --- /dev/null +++ b/configs/scratch/metafile.yml @@ -0,0 +1,48 @@ +Collections: + - Name: Rethinking ImageNet Pre-training + Metadata: + Training Data: COCO + Training Techniques: + - SGD with Momentum + - Weight Decay + Training Resources: 8x V100 GPUs + Architecture: + - FPN + - RPN + - ResNet + Paper: + URL: https://arxiv.org/abs/1811.08883 + Title: 'Rethinking ImageNet Pre-training' + README: configs/scratch/README.md + Code: + URL: https://github.com/open-mmlab/mmdetection/blob/v2.0.0/configs/scratch/faster_rcnn_r50_fpn_gn-all_scratch_6x_coco.py + Version: v2.0.0 + +Models: + - Name: faster_rcnn_r50_fpn_gn-all_scratch_6x_coco + In Collection: Rethinking ImageNet Pre-training + Config: configs/scratch/faster_rcnn_r50_fpn_gn-all_scratch_6x_coco.py + Metadata: + Epochs: 72 + Results: + - Task: Object Detection + Dataset: COCO + Metrics: + box AP: 40.7 + Weights: https://download.openmmlab.com/mmdetection/v2.0/scratch/faster_rcnn_r50_fpn_gn-all_scratch_6x_coco/scratch_faster_rcnn_r50_fpn_gn_6x_bbox_mAP-0.407_20200201_193013-90813d01.pth + + - Name: mask_rcnn_r50_fpn_gn-all_scratch_6x_coco + In Collection: Rethinking ImageNet Pre-training + Config: configs/scratch/mask_rcnn_r50_fpn_gn-all_scratch_6x_coco.py + Metadata: + Epochs: 72 + Results: + - Task: Object Detection + Dataset: COCO + Metrics: + box AP: 41.2 + - Task: Instance Segmentation + Dataset: COCO + Metrics: + mask AP: 37.4 + Weights: https://download.openmmlab.com/mmdetection/v2.0/scratch/mask_rcnn_r50_fpn_gn-all_scratch_6x_coco/scratch_mask_rcnn_r50_fpn_gn_6x_bbox_mAP-0.412__segm_mAP-0.374_20200201_193051-1e190a40.pth diff --git a/configs/seesaw_loss/README.md b/configs/seesaw_loss/README.md new file mode 100644 index 0000000..47cbff8 --- /dev/null +++ b/configs/seesaw_loss/README.md @@ -0,0 +1,39 @@ +# Seesaw Loss for Long-Tailed Instance Segmentation (CVPR 2021) + +## Introduction + + + +We provide config files to reproduce the instance segmentation performance in the CVPR 2021 paper for [Seesaw Loss for Long-Tailed Instance Segmentation](https://arxiv.org/abs/2008.10032). + +```latex +@inproceedings{wang2021seesaw, + title={Seesaw Loss for Long-Tailed Instance Segmentation}, + author={Jiaqi Wang and Wenwei Zhang and Yuhang Zang and Yuhang Cao and Jiangmiao Pang and Tao Gong and Kai Chen and Ziwei Liu and Chen Change Loy and Dahua Lin}, + booktitle={Proceedings of the {IEEE} Conference on Computer Vision and Pattern Recognition}, + year={2021} +} +``` + + +* Please setup [LVIS dataset](../lvis/README.md) for MMDetection. + +* RFS indicates to use oversample strategy [here](../../docs/tutorials/customize_dataset.md#class-balanced-dataset) with oversample threshold `1e-3`. + +## Results and models of Seasaw Loss on LVIS v1 dataset + + +| Method | Backbone | Style | Lr schd | Data Sampler | Norm Mask | box AP | mask AP | Config | Download | +| :----------------: | :-------: | :-----: | :-----: | :----------: | :-------: | :----: | :-----: | :---------------------------------------------------------------------------------------------------------------------------------------------------------------------: | :------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------: | +| Mask R-CNN | R-50-FPN | pytorch | 2x | random | N | 25.6 | 25.0 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/seesaw_loss/mask_rcnn_r50_fpn_random_seesaw_loss_mstrain_2x_lvis_v1.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/seesaw_loss/mask_rcnn_r50_fpn_random_seesaw_loss_mstrain_2x_lvis_v1-a698dd3d.pth) | [log](https://download.openmmlab.com/mmdetection/v2.0/seesaw_loss/mask_rcnn_r50_fpn_random_seesaw_loss_mstrain_2x_lvis_v1.log.json) | +| Mask R-CNN | R-50-FPN | pytorch | 2x | random | Y | 25.6 | 25.4 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/seesaw_loss/mask_rcnn_r50_fpn_random_seesaw_loss_normed_mask_mstrain_2x_lvis_v1.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/seesaw_loss/mask_rcnn_r50_fpn_random_seesaw_loss_normed_mask_mstrain_2x_lvis_v1-a1c11314.pth) | [log](https://download.openmmlab.com/mmdetection/v2.0/seesaw_loss/mask_rcnn_r50_fpn_random_seesaw_loss_normed_mask_mstrain_2x_lvis_v1.log.json) | +| Mask R-CNN | R-101-FPN | pytorch | 2x | random | N | 27.4 | 26.7 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/mask_rcnn_r101_fpn_random_seesaw_loss_mstrain_2x_lvis_v1.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/seesaw_loss/mask_rcnn_r101_fpn_random_seesaw_loss_mstrain_2x_lvis_v1-8e6e6dd5.pth) | [log](https://download.openmmlab.com/mmdetection/v2.0/seesaw_loss/mask_rcnn_r101_fpn_random_seesaw_loss_mstrain_2x_lvis_v1.log.json) | +| Mask R-CNN | R-101-FPN | pytorch | 2x | random | Y | 27.2 | 27.3 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/seesaw_loss/mask_rcnn_r101_fpn_random_seesaw_loss_normed_mask_mstrain_2x_lvis_v1.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/seesaw_loss/mask_rcnn_r101_fpn_random_seesaw_loss_normed_mask_mstrain_2x_lvis_v1-a0b59c42.pth) | [log](https://download.openmmlab.com/mmdetection/v2.0/seesaw_loss/mask_rcnn_r101_fpn_random_seesaw_loss_normed_mask_mstrain_2x_lvis_v1.log.json) | +| Mask R-CNN | R-50-FPN | pytorch | 2x | RFS | N | 27.6 | 26.4 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/seesaw_loss/mask_rcnn_r50_fpn_sample1e-3_seesaw_loss_mstrain_2x_lvis_v1.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/seesaw_loss/mask_rcnn_r50_fpn_sample1e-3_seesaw_loss_mstrain_2x_lvis_v1-392a804b.pth) | [log](https://download.openmmlab.com/mmdetection/v2.0/seesaw_loss/mask_rcnn_r50_fpn_sample1e-3_seesaw_loss_mstrain_2x_lvis_v1.log.json) | +| Mask R-CNN | R-50-FPN | pytorch | 2x | RFS | Y | 27.6 | 26.8 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/seesaw_loss/mask_rcnn_r50_fpn_sample1e-3_seesaw_loss_normed_mask_mstrain_2x_lvis_v1.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/seesaw_loss/mask_rcnn_r50_fpn_sample1e-3_seesaw_loss_normed_mask_mstrain_2x_lvis_v1-cd0f6a12.pth) | [log](https://download.openmmlab.com/mmdetection/v2.0/seesaw_loss/mask_rcnn_r50_fpn_sample1e-3_seesaw_loss_normed_mask_mstrain_2x_lvis_v1.log.json) | +| Mask R-CNN | R-101-FPN | pytorch | 2x | RFS | N | 28.9 | 27.6 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/seesaw_loss/mask_rcnn_r101_fpn_sample1e-3_seesaw_loss_mstrain_2x_lvis_v1.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/seesaw_loss/mask_rcnn_r101_fpn_sample1e-3_seesaw_loss_mstrain_2x_lvis_v1-e68eb464.pth) | [log](https://download.openmmlab.com/mmdetection/v2.0/seesaw_loss/mask_rcnn_r101_fpn_sample1e-3_seesaw_loss_mstrain_2x_lvis_v1.log.json) | +| Mask R-CNN | R-101-FPN | pytorch | 2x | RFS | Y | 28.9 | 28.2 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/seesaw_loss/mask_rcnn_r101_fpn_sample1e-3_seesaw_loss_normed_mask_mstrain_2x_lvis_v1.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/seesaw_loss/mask_rcnn_r101_fpn_sample1e-3_seesaw_loss_normed_mask_mstrain_2x_lvis_v1-1d817139.pth) | [log](https://download.openmmlab.com/mmdetection/v2.0/seesaw_loss/mask_rcnn_r101_fpn_sample1e-3_seesaw_loss_normed_mask_mstrain_2x_lvis_v1.log.json) | +| Cascade Mask R-CNN | R-101-FPN | pytorch | 2x | random | N | 33.1 | 29.2 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/seesaw_loss/cascade_mask_rcnn_r101_fpn_random_seesaw_loss_mstrain_2x_lvis_v1.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/seesaw_loss/cascade_mask_rcnn_r101_fpn_random_seesaw_loss_mstrain_2x_lvis_v1-71e2215e.pth) | [log](https://download.openmmlab.com/mmdetection/v2.0/seesaw_loss/cascade_mask_rcnn_r101_fpn_random_seesaw_loss_mstrain_2x_lvis_v1.log.json) | +| Cascade Mask R-CNN | R-101-FPN | pytorch | 2x | random | Y | 33.0 | 30.0 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/seesaw_loss/cascade_mask_rcnn_r101_fpn_random_seesaw_loss_normed_mask_mstrain_2x_lvis_v1.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/seesaw_loss/cascade_mask_rcnn_r101_fpn_random_seesaw_loss_normed_mask_mstrain_2x_lvis_v1-8b5a6745.pth) | [log](https://download.openmmlab.com/mmdetection/v2.0/seesaw_loss/cascade_mask_rcnn_r101_fpn_random_seesaw_loss_normed_mask_mstrain_2x_lvis_v1.log.json) | +| Cascade Mask R-CNN | R-101-FPN | pytorch | 2x | RFS | N | 30.0 | 29.3 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/seesaw_loss/cascade_mask_rcnn_r101_fpn_sample1e-3_seesaw_loss_mstrain_2x_lvis_v1.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/seesaw_loss/cascade_mask_rcnn_r101_fpn_sample1e-3_seesaw_loss_mstrain_2x_lvis_v1-5d8ca2a4.pth) | [log](https://download.openmmlab.com/mmdetection/v2.0/seesaw_loss/cascade_mask_rcnn_r101_fpn_sample1e-3_seesaw_loss_mstrain_2x_lvis_v1.log.json) | +| Cascade Mask R-CNN | R-101-FPN | pytorch | 2x | RFS | Y | 32.8 | 30.1 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/seesaw_loss/cascade_mask_rcnn_r101_fpn_sample1e-3_seesaw_loss_normed_mask_mstrain_2x_lvis_v1.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/seesaw_loss/cascade_mask_rcnn_r101_fpn_sample1e-3_seesaw_loss_normed_mask_mstrain_2x_lvis_v1-c8551505.pth) | [log](https://download.openmmlab.com/mmdetection/v2.0/seesaw_loss/cascade_mask_rcnn_r101_fpn_sample1e-3_seesaw_loss_normed_mask_mstrain_2x_lvis_v1.log.json) | diff --git a/configs/seesaw_loss/cascade_mask_rcnn_r101_fpn_random_seesaw_loss_mstrain_2x_lvis_v1.py b/configs/seesaw_loss/cascade_mask_rcnn_r101_fpn_random_seesaw_loss_mstrain_2x_lvis_v1.py new file mode 100644 index 0000000..beeb0d1 --- /dev/null +++ b/configs/seesaw_loss/cascade_mask_rcnn_r101_fpn_random_seesaw_loss_mstrain_2x_lvis_v1.py @@ -0,0 +1,132 @@ +_base_ = [ + '../_base_/models/cascade_mask_rcnn_r50_fpn.py', + '../_base_/datasets/coco_instance.py', + '../_base_/schedules/schedule_2x.py', '../_base_/default_runtime.py' +] + +model = dict( + backbone=dict( + depth=101, + init_cfg=dict(type='Pretrained', + checkpoint='torchvision://resnet101')), + roi_head=dict( + bbox_head=[ + dict( + type='Shared2FCBBoxHead', + in_channels=256, + fc_out_channels=1024, + roi_feat_size=7, + num_classes=1203, + bbox_coder=dict( + type='DeltaXYWHBBoxCoder', + target_means=[0., 0., 0., 0.], + target_stds=[0.1, 0.1, 0.2, 0.2]), + reg_class_agnostic=True, + cls_predictor_cfg=dict(type='NormedLinear', tempearture=20), + loss_cls=dict( + type='SeesawLoss', + p=0.8, + q=2.0, + num_classes=1203, + loss_weight=1.0), + loss_bbox=dict(type='SmoothL1Loss', beta=1.0, + loss_weight=1.0)), + dict( + type='Shared2FCBBoxHead', + in_channels=256, + fc_out_channels=1024, + roi_feat_size=7, + num_classes=1203, + bbox_coder=dict( + type='DeltaXYWHBBoxCoder', + target_means=[0., 0., 0., 0.], + target_stds=[0.05, 0.05, 0.1, 0.1]), + reg_class_agnostic=True, + cls_predictor_cfg=dict(type='NormedLinear', tempearture=20), + loss_cls=dict( + type='SeesawLoss', + p=0.8, + q=2.0, + num_classes=1203, + loss_weight=1.0), + loss_bbox=dict(type='SmoothL1Loss', beta=1.0, + loss_weight=1.0)), + dict( + type='Shared2FCBBoxHead', + in_channels=256, + fc_out_channels=1024, + roi_feat_size=7, + num_classes=1203, + bbox_coder=dict( + type='DeltaXYWHBBoxCoder', + target_means=[0., 0., 0., 0.], + target_stds=[0.033, 0.033, 0.067, 0.067]), + reg_class_agnostic=True, + cls_predictor_cfg=dict(type='NormedLinear', tempearture=20), + loss_cls=dict( + type='SeesawLoss', + p=0.8, + q=2.0, + num_classes=1203, + loss_weight=1.0), + loss_bbox=dict(type='SmoothL1Loss', beta=1.0, loss_weight=1.0)) + ], + mask_head=dict(num_classes=1203)), + test_cfg=dict( + rcnn=dict( + score_thr=0.0001, + # LVIS allows up to 300 + max_per_img=300))) +img_norm_cfg = dict( + mean=[123.675, 116.28, 103.53], std=[58.395, 57.12, 57.375], to_rgb=True) +train_pipeline = [ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations', with_bbox=True, with_mask=True), + dict( + type='Resize', + img_scale=[(1333, 640), (1333, 672), (1333, 704), (1333, 736), + (1333, 768), (1333, 800)], + multiscale_mode='value', + keep_ratio=True), + dict(type='RandomFlip', flip_ratio=0.5), + dict(type='Normalize', **img_norm_cfg), + dict(type='Pad', size_divisor=32), + dict(type='DefaultFormatBundle'), + dict(type='Collect', keys=['img', 'gt_bboxes', 'gt_labels', 'gt_masks']), +] +test_pipeline = [ + dict(type='LoadImageFromFile'), + dict( + type='MultiScaleFlipAug', + img_scale=(1333, 800), + flip=False, + transforms=[ + dict(type='Resize', keep_ratio=True), + dict(type='RandomFlip'), + dict(type='Normalize', **img_norm_cfg), + dict(type='Pad', size_divisor=32), + dict(type='ImageToTensor', keys=['img']), + dict(type='Collect', keys=['img']), + ]) +] +dataset_type = 'LVISV1Dataset' +data_root = 'data/lvis_v1/' +data = dict( + samples_per_gpu=2, + workers_per_gpu=2, + train=dict( + type=dataset_type, + ann_file=data_root + 'annotations/lvis_v1_train.json', + img_prefix=data_root, + pipeline=train_pipeline), + val=dict( + type=dataset_type, + ann_file=data_root + 'annotations/lvis_v1_val.json', + img_prefix=data_root, + pipeline=test_pipeline), + test=dict( + type=dataset_type, + ann_file=data_root + 'annotations/lvis_v1_val.json', + img_prefix=data_root, + pipeline=test_pipeline)) +evaluation = dict(interval=24, metric=['bbox', 'segm']) diff --git a/configs/seesaw_loss/cascade_mask_rcnn_r101_fpn_random_seesaw_loss_normed_mask_mstrain_2x_lvis_v1.py b/configs/seesaw_loss/cascade_mask_rcnn_r101_fpn_random_seesaw_loss_normed_mask_mstrain_2x_lvis_v1.py new file mode 100644 index 0000000..0f29948 --- /dev/null +++ b/configs/seesaw_loss/cascade_mask_rcnn_r101_fpn_random_seesaw_loss_normed_mask_mstrain_2x_lvis_v1.py @@ -0,0 +1,5 @@ +_base_ = './cascade_mask_rcnn_r101_fpn_random_seesaw_loss_mstrain_2x_lvis_v1.py' # noqa: E501 +model = dict( + roi_head=dict( + mask_head=dict( + predictor_cfg=dict(type='NormedConv2d', tempearture=20)))) diff --git a/configs/seesaw_loss/cascade_mask_rcnn_r101_fpn_sample1e-3_seesaw_loss_mstrain_2x_lvis_v1.py b/configs/seesaw_loss/cascade_mask_rcnn_r101_fpn_sample1e-3_seesaw_loss_mstrain_2x_lvis_v1.py new file mode 100644 index 0000000..bb88750 --- /dev/null +++ b/configs/seesaw_loss/cascade_mask_rcnn_r101_fpn_sample1e-3_seesaw_loss_mstrain_2x_lvis_v1.py @@ -0,0 +1,98 @@ +_base_ = [ + '../_base_/models/cascade_mask_rcnn_r50_fpn.py', + '../_base_/datasets/lvis_v1_instance.py', + '../_base_/schedules/schedule_2x.py', '../_base_/default_runtime.py' +] + +model = dict( + backbone=dict( + depth=101, + init_cfg=dict(type='Pretrained', + checkpoint='torchvision://resnet101')), + roi_head=dict( + bbox_head=[ + dict( + type='Shared2FCBBoxHead', + in_channels=256, + fc_out_channels=1024, + roi_feat_size=7, + num_classes=1203, + bbox_coder=dict( + type='DeltaXYWHBBoxCoder', + target_means=[0., 0., 0., 0.], + target_stds=[0.1, 0.1, 0.2, 0.2]), + reg_class_agnostic=True, + cls_predictor_cfg=dict(type='NormedLinear', tempearture=20), + loss_cls=dict( + type='SeesawLoss', + p=0.8, + q=2.0, + num_classes=1203, + loss_weight=1.0), + loss_bbox=dict(type='SmoothL1Loss', beta=1.0, + loss_weight=1.0)), + dict( + type='Shared2FCBBoxHead', + in_channels=256, + fc_out_channels=1024, + roi_feat_size=7, + num_classes=1203, + bbox_coder=dict( + type='DeltaXYWHBBoxCoder', + target_means=[0., 0., 0., 0.], + target_stds=[0.05, 0.05, 0.1, 0.1]), + reg_class_agnostic=True, + cls_predictor_cfg=dict(type='NormedLinear', tempearture=20), + loss_cls=dict( + type='SeesawLoss', + p=0.8, + q=2.0, + num_classes=1203, + loss_weight=1.0), + loss_bbox=dict(type='SmoothL1Loss', beta=1.0, + loss_weight=1.0)), + dict( + type='Shared2FCBBoxHead', + in_channels=256, + fc_out_channels=1024, + roi_feat_size=7, + num_classes=1203, + bbox_coder=dict( + type='DeltaXYWHBBoxCoder', + target_means=[0., 0., 0., 0.], + target_stds=[0.033, 0.033, 0.067, 0.067]), + reg_class_agnostic=True, + cls_predictor_cfg=dict(type='NormedLinear', tempearture=20), + loss_cls=dict( + type='SeesawLoss', + p=0.8, + q=2.0, + num_classes=1203, + loss_weight=1.0), + loss_bbox=dict(type='SmoothL1Loss', beta=1.0, loss_weight=1.0)) + ], + mask_head=dict(num_classes=1203)), + test_cfg=dict( + rcnn=dict( + score_thr=0.0001, + # LVIS allows up to 300 + max_per_img=300))) +img_norm_cfg = dict( + mean=[123.675, 116.28, 103.53], std=[58.395, 57.12, 57.375], to_rgb=True) +train_pipeline = [ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations', with_bbox=True, with_mask=True), + dict( + type='Resize', + img_scale=[(1333, 640), (1333, 672), (1333, 704), (1333, 736), + (1333, 768), (1333, 800)], + multiscale_mode='value', + keep_ratio=True), + dict(type='RandomFlip', flip_ratio=0.5), + dict(type='Normalize', **img_norm_cfg), + dict(type='Pad', size_divisor=32), + dict(type='DefaultFormatBundle'), + dict(type='Collect', keys=['img', 'gt_bboxes', 'gt_labels', 'gt_masks']), +] +data = dict(train=dict(dataset=dict(pipeline=train_pipeline))) +evaluation = dict(interval=24, metric=['bbox', 'segm']) diff --git a/configs/seesaw_loss/cascade_mask_rcnn_r101_fpn_sample1e-3_seesaw_loss_normed_mask_mstrain_2x_lvis_v1.py b/configs/seesaw_loss/cascade_mask_rcnn_r101_fpn_sample1e-3_seesaw_loss_normed_mask_mstrain_2x_lvis_v1.py new file mode 100644 index 0000000..262e76b --- /dev/null +++ b/configs/seesaw_loss/cascade_mask_rcnn_r101_fpn_sample1e-3_seesaw_loss_normed_mask_mstrain_2x_lvis_v1.py @@ -0,0 +1,5 @@ +_base_ = './cascade_mask_rcnn_r101_fpn_sample1e-3_seesaw_loss_mstrain_2x_lvis_v1.py' # noqa: E501 +model = dict( + roi_head=dict( + mask_head=dict( + predictor_cfg=dict(type='NormedConv2d', tempearture=20)))) diff --git a/configs/seesaw_loss/mask_rcnn_r101_fpn_random_seesaw_loss_mstrain_2x_lvis_v1.py b/configs/seesaw_loss/mask_rcnn_r101_fpn_random_seesaw_loss_mstrain_2x_lvis_v1.py new file mode 100644 index 0000000..57deab1 --- /dev/null +++ b/configs/seesaw_loss/mask_rcnn_r101_fpn_random_seesaw_loss_mstrain_2x_lvis_v1.py @@ -0,0 +1,6 @@ +_base_ = './mask_rcnn_r50_fpn_random_seesaw_loss_mstrain_2x_lvis_v1.py' +model = dict( + backbone=dict( + depth=101, + init_cfg=dict(type='Pretrained', + checkpoint='torchvision://resnet101'))) diff --git a/configs/seesaw_loss/mask_rcnn_r101_fpn_random_seesaw_loss_normed_mask_mstrain_2x_lvis_v1.py b/configs/seesaw_loss/mask_rcnn_r101_fpn_random_seesaw_loss_normed_mask_mstrain_2x_lvis_v1.py new file mode 100644 index 0000000..a539929 --- /dev/null +++ b/configs/seesaw_loss/mask_rcnn_r101_fpn_random_seesaw_loss_normed_mask_mstrain_2x_lvis_v1.py @@ -0,0 +1,6 @@ +_base_ = './mask_rcnn_r50_fpn_random_seesaw_loss_normed_mask_mstrain_2x_lvis_v1.py' # noqa: E501 +model = dict( + backbone=dict( + depth=101, + init_cfg=dict(type='Pretrained', + checkpoint='torchvision://resnet101'))) diff --git a/configs/seesaw_loss/mask_rcnn_r101_fpn_sample1e-3_seesaw_loss_mstrain_2x_lvis_v1.py b/configs/seesaw_loss/mask_rcnn_r101_fpn_sample1e-3_seesaw_loss_mstrain_2x_lvis_v1.py new file mode 100644 index 0000000..1f5065e --- /dev/null +++ b/configs/seesaw_loss/mask_rcnn_r101_fpn_sample1e-3_seesaw_loss_mstrain_2x_lvis_v1.py @@ -0,0 +1,6 @@ +_base_ = './mask_rcnn_r50_fpn_sample1e-3_seesaw_loss_mstrain_2x_lvis_v1.py' +model = dict( + backbone=dict( + depth=101, + init_cfg=dict(type='Pretrained', + checkpoint='torchvision://resnet101'))) diff --git a/configs/seesaw_loss/mask_rcnn_r101_fpn_sample1e-3_seesaw_loss_normed_mask_mstrain_2x_lvis_v1.py b/configs/seesaw_loss/mask_rcnn_r101_fpn_sample1e-3_seesaw_loss_normed_mask_mstrain_2x_lvis_v1.py new file mode 100644 index 0000000..13d0b5f --- /dev/null +++ b/configs/seesaw_loss/mask_rcnn_r101_fpn_sample1e-3_seesaw_loss_normed_mask_mstrain_2x_lvis_v1.py @@ -0,0 +1,6 @@ +_base_ = './mask_rcnn_r50_fpn_sample1e-3_seesaw_loss_normed_mask_mstrain_2x_lvis_v1.py' # noqa: E501 +model = dict( + backbone=dict( + depth=101, + init_cfg=dict(type='Pretrained', + checkpoint='torchvision://resnet101'))) diff --git a/configs/seesaw_loss/mask_rcnn_r50_fpn_random_seesaw_loss_mstrain_2x_lvis_v1.py b/configs/seesaw_loss/mask_rcnn_r50_fpn_random_seesaw_loss_mstrain_2x_lvis_v1.py new file mode 100644 index 0000000..743f5f2 --- /dev/null +++ b/configs/seesaw_loss/mask_rcnn_r50_fpn_random_seesaw_loss_mstrain_2x_lvis_v1.py @@ -0,0 +1,75 @@ +_base_ = [ + '../_base_/models/mask_rcnn_r50_fpn.py', + '../_base_/datasets/coco_instance.py', + '../_base_/schedules/schedule_2x.py', '../_base_/default_runtime.py' +] +model = dict( + roi_head=dict( + bbox_head=dict( + num_classes=1203, + cls_predictor_cfg=dict(type='NormedLinear', tempearture=20), + loss_cls=dict( + type='SeesawLoss', + p=0.8, + q=2.0, + num_classes=1203, + loss_weight=1.0)), + mask_head=dict(num_classes=1203)), + test_cfg=dict( + rcnn=dict( + score_thr=0.0001, + # LVIS allows up to 300 + max_per_img=300))) +img_norm_cfg = dict( + mean=[123.675, 116.28, 103.53], std=[58.395, 57.12, 57.375], to_rgb=True) +train_pipeline = [ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations', with_bbox=True, with_mask=True), + dict( + type='Resize', + img_scale=[(1333, 640), (1333, 672), (1333, 704), (1333, 736), + (1333, 768), (1333, 800)], + multiscale_mode='value', + keep_ratio=True), + dict(type='RandomFlip', flip_ratio=0.5), + dict(type='Normalize', **img_norm_cfg), + dict(type='Pad', size_divisor=32), + dict(type='DefaultFormatBundle'), + dict(type='Collect', keys=['img', 'gt_bboxes', 'gt_labels', 'gt_masks']), +] +test_pipeline = [ + dict(type='LoadImageFromFile'), + dict( + type='MultiScaleFlipAug', + img_scale=(1333, 800), + flip=False, + transforms=[ + dict(type='Resize', keep_ratio=True), + dict(type='RandomFlip'), + dict(type='Normalize', **img_norm_cfg), + dict(type='Pad', size_divisor=32), + dict(type='ImageToTensor', keys=['img']), + dict(type='Collect', keys=['img']), + ]) +] +dataset_type = 'LVISV1Dataset' +data_root = 'data/lvis_v1/' +data = dict( + samples_per_gpu=2, + workers_per_gpu=2, + train=dict( + type=dataset_type, + ann_file=data_root + 'annotations/lvis_v1_train.json', + img_prefix=data_root, + pipeline=train_pipeline), + val=dict( + type=dataset_type, + ann_file=data_root + 'annotations/lvis_v1_val.json', + img_prefix=data_root, + pipeline=test_pipeline), + test=dict( + type=dataset_type, + ann_file=data_root + 'annotations/lvis_v1_val.json', + img_prefix=data_root, + pipeline=test_pipeline)) +evaluation = dict(interval=24, metric=['bbox', 'segm']) diff --git a/configs/seesaw_loss/mask_rcnn_r50_fpn_random_seesaw_loss_normed_mask_mstrain_2x_lvis_v1.py b/configs/seesaw_loss/mask_rcnn_r50_fpn_random_seesaw_loss_normed_mask_mstrain_2x_lvis_v1.py new file mode 100644 index 0000000..0af8921 --- /dev/null +++ b/configs/seesaw_loss/mask_rcnn_r50_fpn_random_seesaw_loss_normed_mask_mstrain_2x_lvis_v1.py @@ -0,0 +1,5 @@ +_base_ = './mask_rcnn_r50_fpn_random_seesaw_loss_mstrain_2x_lvis_v1.py' +model = dict( + roi_head=dict( + mask_head=dict( + predictor_cfg=dict(type='NormedConv2d', tempearture=20)))) diff --git a/configs/seesaw_loss/mask_rcnn_r50_fpn_sample1e-3_seesaw_loss_mstrain_2x_lvis_v1.py b/configs/seesaw_loss/mask_rcnn_r50_fpn_sample1e-3_seesaw_loss_mstrain_2x_lvis_v1.py new file mode 100644 index 0000000..4fc1504 --- /dev/null +++ b/configs/seesaw_loss/mask_rcnn_r50_fpn_sample1e-3_seesaw_loss_mstrain_2x_lvis_v1.py @@ -0,0 +1,41 @@ +_base_ = [ + '../_base_/models/mask_rcnn_r50_fpn.py', + '../_base_/datasets/lvis_v1_instance.py', + '../_base_/schedules/schedule_2x.py', '../_base_/default_runtime.py' +] +model = dict( + roi_head=dict( + bbox_head=dict( + num_classes=1203, + cls_predictor_cfg=dict(type='NormedLinear', tempearture=20), + loss_cls=dict( + type='SeesawLoss', + p=0.8, + q=2.0, + num_classes=1203, + loss_weight=1.0)), + mask_head=dict(num_classes=1203)), + test_cfg=dict( + rcnn=dict( + score_thr=0.0001, + # LVIS allows up to 300 + max_per_img=300))) +img_norm_cfg = dict( + mean=[123.675, 116.28, 103.53], std=[58.395, 57.12, 57.375], to_rgb=True) +train_pipeline = [ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations', with_bbox=True, with_mask=True), + dict( + type='Resize', + img_scale=[(1333, 640), (1333, 672), (1333, 704), (1333, 736), + (1333, 768), (1333, 800)], + multiscale_mode='value', + keep_ratio=True), + dict(type='RandomFlip', flip_ratio=0.5), + dict(type='Normalize', **img_norm_cfg), + dict(type='Pad', size_divisor=32), + dict(type='DefaultFormatBundle'), + dict(type='Collect', keys=['img', 'gt_bboxes', 'gt_labels', 'gt_masks']), +] +data = dict(train=dict(dataset=dict(pipeline=train_pipeline))) +evaluation = dict(interval=12, metric=['bbox', 'segm']) diff --git a/configs/seesaw_loss/mask_rcnn_r50_fpn_sample1e-3_seesaw_loss_normed_mask_mstrain_2x_lvis_v1.py b/configs/seesaw_loss/mask_rcnn_r50_fpn_sample1e-3_seesaw_loss_normed_mask_mstrain_2x_lvis_v1.py new file mode 100644 index 0000000..0ef6bd2 --- /dev/null +++ b/configs/seesaw_loss/mask_rcnn_r50_fpn_sample1e-3_seesaw_loss_normed_mask_mstrain_2x_lvis_v1.py @@ -0,0 +1,5 @@ +_base_ = './mask_rcnn_r50_fpn_sample1e-3_seesaw_loss_mstrain_2x_lvis_v1.py' +model = dict( + roi_head=dict( + mask_head=dict( + predictor_cfg=dict(type='NormedConv2d', tempearture=20)))) diff --git a/configs/selfsup_pretrain/README.md b/configs/selfsup_pretrain/README.md new file mode 100644 index 0000000..ad5c69f --- /dev/null +++ b/configs/selfsup_pretrain/README.md @@ -0,0 +1,95 @@ +# Backbones Trained by Self-Supervise Algorithms + +## Introduction + + + +We support to apply the backbone models pre-trained by different self-supervised methods in detection systems and provide their results on Mask R-CNN. + +The pre-trained models are converted from [MoCo](https://github.com/facebookresearch/moco) and downloaded from [SwAV](https://github.com/facebookresearch/swav). + +For SwAV, please cite + +```latex +@article{caron2020unsupervised, + title={Unsupervised Learning of Visual Features by Contrasting Cluster Assignments}, + author={Caron, Mathilde and Misra, Ishan and Mairal, Julien and Goyal, Priya and Bojanowski, Piotr and Joulin, Armand}, + booktitle={Proceedings of Advances in Neural Information Processing Systems (NeurIPS)}, + year={2020} +} +``` + +For MoCo, please cite + +```latex +@Article{he2019moco, + author = {Kaiming He and Haoqi Fan and Yuxin Wu and Saining Xie and Ross Girshick}, + title = {Momentum Contrast for Unsupervised Visual Representation Learning}, + journal = {arXiv preprint arXiv:1911.05722}, + year = {2019}, +} +@Article{chen2020mocov2, + author = {Xinlei Chen and Haoqi Fan and Ross Girshick and Kaiming He}, + title = {Improved Baselines with Momentum Contrastive Learning}, + journal = {arXiv preprint arXiv:2003.04297}, + year = {2020}, +} +``` + +## Usage + +To use a self-supervisely pretrained backbone, there are two steps to do: + +1. Download and convert the model to PyTorch-style supported by MMDetection +2. Modify the config and change the training setting accordingly + +### Convert model + +For more general usage, we also provide script `selfsup2mmdet.py` in the tools directory to convert the key of models pretrained by different self-supervised methods to PyTorch-style checkpoints used in MMDetection. + +```bash +python -u tools/model_converters/selfsup2mmdet.py ${PRETRAIN_PATH} ${STORE_PATH} --selfsup ${method} +``` + +This script convert model from `PRETRAIN_PATH` and store the converted model in `STORE_PATH`. + +For example, to use a ResNet-50 backbone released by MoCo, you can download it from [here](https://dl.fbaipublicfiles.com/moco/moco_checkpoints/moco_v2_800ep/moco_v2_800ep_pretrain.pth.tar) and use the following command + +```bash +python -u tools/model_converters/selfsup2mmdet.py ./moco_v2_800ep_pretrain.pth.tar mocov2_r50_800ep_pretrain.pth --selfsup moco +``` + +To use the ResNet-50 backbone released by SwAV, you can download it from [here](https://dl.fbaipublicfiles.com/deepcluster/swav_800ep_pretrain.pth.tar) + +### Modify config + +The backbone requires SyncBN and the `fronzen_stages` need to be changed. A config that use the moco backbone is as below + +```python +_base_ = [ + '../_base_/models/mask_rcnn_r50_fpn.py', + '../_base_/datasets/coco_instance.py', + '../_base_/schedules/schedule_1x.py', '../_base_/default_runtime.py' +] + +model = dict( + pretrained='./mocov2_r50_800ep_pretrain.pth', + backbone=dict( + frozen_stages=0, + norm_cfg=dict(type='SyncBN', requires_grad=True), + norm_eval=False)) + +``` + +## Results + +| Method | Backbone | Style | Lr schd | Mem (GB) | Inf time (fps) | box AP | mask AP | Config | Download | +| :-----: | :-----: | :-----: | :-----: | :------: | :------------: | :----: | :-----: | :------: | :--------: | +|Mask RCNN |[R50 by MoCo v2](./mask_rcnn_r50_fpn_mocov2-pretrain_1x_coco.py)| pytorch |1x|| |38.0|34.3|[config](https://github.com/open-mmlab/mmdetection/tree/master/configs/selfsup_pretrain/mask_rcnn_r50_fpn_mocov2-pretrain_1x_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/selfsup_pretrain/mask_rcnn_r50_fpn_mocov2-pretrain_1x_coco/mask_rcnn_r50_fpn_mocov2-pretrain_1x_coco_20210604_114614-a8b63483.pth) | [log](https://download.openmmlab.com/mmdetection/v2.0/selfsup_pretrain/mask_rcnn_r50_fpn_mocov2-pretrain_1x_coco/mask_rcnn_r50_fpn_mocov2-pretrain_1x_coco_20210604_114614.log.json)| +|Mask RCNN |[R50 by MoCo v2](./mask_rcnn_r50_fpn_mocov2-pretrain_ms-2x_coco.py)| pytorch | multi-scale 2x || |40.8|36.8|[config](https://github.com/open-mmlab/mmdetection/tree/master/configs/selfsup_pretrain/mask_rcnn_r50_fpn_mocov2-pretrain_ms-2x_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/selfsup_pretrain/mask_rcnn_r50_fpn_mocov2-pretrain_ms-2x_coco/mask_rcnn_r50_fpn_mocov2-pretrain_ms-2x_coco_20210605_163717-d95df20a.pth) | [log](https://download.openmmlab.com/mmdetection/v2.0/selfsup_pretrain/mask_rcnn_r50_fpn_mocov2-pretrain_ms-2x_coco/mask_rcnn_r50_fpn_mocov2-pretrain_ms-2x_coco_20210605_163717.log.json)| +|Mask RCNN |[R50 by SwAV](./mask_rcnn_r50_fpn_swav-pretrain_1x_coco.py)| pytorch | 1x || |39.1 | 35.7|[config](https://github.com/open-mmlab/mmdetection/tree/master/configs/selfsup_pretrain/mask_rcnn_r50_fpn_swav-pretrain_1x_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/selfsup_pretrain/mask_rcnn_r50_fpn_swav-pretrain_1x_coco/mask_rcnn_r50_fpn_swav-pretrain_1x_coco_20210604_114640-7b9baf28.pth) | [log](https://download.openmmlab.com/mmdetection/v2.0/selfsup_pretrain/mask_rcnn_r50_fpn_swav-pretrain_1x_coco/mask_rcnn_r50_fpn_swav-pretrain_1x_coco_20210604_114640.log.json)| +|Mask RCNN |[R50 by SwAV](./mask_rcnn_r50_fpn_swav-pretrain_ms-2x_coco.py)| pytorch | multi-scale 2x || |41.3|37.3|[config](https://github.com/open-mmlab/mmdetection/tree/master/configs/selfsup_pretrain/mask_rcnn_r50_fpn_swav-pretrain_ms-2x_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/selfsup_pretrain/mask_rcnn_r50_fpn_swav-pretrain_ms-2x_coco/mask_rcnn_r50_fpn_swav-pretrain_ms-2x_coco_20210605_163717-08e26fca.pth) | [log](https://download.openmmlab.com/mmdetection/v2.0/selfsup_pretrain/mask_rcnn_r50_fpn_swav-pretrain_ms-2x_coco/mask_rcnn_r50_fpn_swav-pretrain_ms-2x_coco_20210605_163717.log.json)| + +### Notice + +1. We only provide single-scale 1x and multi-scale 2x configs as examples to show how to use backbones trained by self-supervised algorithms. We will try to reproduce the results in their corresponding paper using the released backbone in the future. Please stay tuned. diff --git a/configs/selfsup_pretrain/mask_rcnn_r50_fpn_mocov2-pretrain_1x_coco.py b/configs/selfsup_pretrain/mask_rcnn_r50_fpn_mocov2-pretrain_1x_coco.py new file mode 100644 index 0000000..f1e0615 --- /dev/null +++ b/configs/selfsup_pretrain/mask_rcnn_r50_fpn_mocov2-pretrain_1x_coco.py @@ -0,0 +1,13 @@ +_base_ = [ + '../_base_/models/mask_rcnn_r50_fpn.py', + '../_base_/datasets/coco_instance.py', + '../_base_/schedules/schedule_1x.py', '../_base_/default_runtime.py' +] + +model = dict( + backbone=dict( + frozen_stages=0, + norm_cfg=dict(type='SyncBN', requires_grad=True), + norm_eval=False, + init_cfg=dict( + type='Pretrained', checkpoint='./mocov2_r50_800ep_pretrain.pth'))) diff --git a/configs/selfsup_pretrain/mask_rcnn_r50_fpn_mocov2-pretrain_ms-2x_coco.py b/configs/selfsup_pretrain/mask_rcnn_r50_fpn_mocov2-pretrain_ms-2x_coco.py new file mode 100644 index 0000000..09aa156 --- /dev/null +++ b/configs/selfsup_pretrain/mask_rcnn_r50_fpn_mocov2-pretrain_ms-2x_coco.py @@ -0,0 +1,32 @@ +_base_ = [ + '../_base_/models/mask_rcnn_r50_fpn.py', + '../_base_/datasets/coco_instance.py', + '../_base_/schedules/schedule_2x.py', '../_base_/default_runtime.py' +] + +model = dict( + backbone=dict( + frozen_stages=0, + norm_cfg=dict(type='SyncBN', requires_grad=True), + norm_eval=False, + init_cfg=dict( + type='Pretrained', checkpoint='./mocov2_r50_800ep_pretrain.pth'))) + +img_norm_cfg = dict( + mean=[123.675, 116.28, 103.53], std=[58.395, 57.12, 57.375], to_rgb=True) +train_pipeline = [ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations', with_bbox=True, with_mask=True), + dict( + type='Resize', + img_scale=[(1333, 640), (1333, 800)], + multiscale_mode='range', + keep_ratio=True), + dict(type='RandomFlip', flip_ratio=0.5), + dict(type='Normalize', **img_norm_cfg), + dict(type='Pad', size_divisor=32), + dict(type='DefaultFormatBundle'), + dict(type='Collect', keys=['img', 'gt_bboxes', 'gt_labels', 'gt_masks']) +] + +data = dict(train=dict(pipeline=train_pipeline)) diff --git a/configs/selfsup_pretrain/mask_rcnn_r50_fpn_swav-pretrain_1x_coco.py b/configs/selfsup_pretrain/mask_rcnn_r50_fpn_swav-pretrain_1x_coco.py new file mode 100644 index 0000000..f92a345 --- /dev/null +++ b/configs/selfsup_pretrain/mask_rcnn_r50_fpn_swav-pretrain_1x_coco.py @@ -0,0 +1,13 @@ +_base_ = [ + '../_base_/models/mask_rcnn_r50_fpn.py', + '../_base_/datasets/coco_instance.py', + '../_base_/schedules/schedule_1x.py', '../_base_/default_runtime.py' +] + +model = dict( + backbone=dict( + frozen_stages=0, + norm_cfg=dict(type='SyncBN', requires_grad=True), + norm_eval=False, + init_cfg=dict( + type='Pretrained', checkpoint='./swav_800ep_pretrain.pth.tar'))) diff --git a/configs/selfsup_pretrain/mask_rcnn_r50_fpn_swav-pretrain_ms-2x_coco.py b/configs/selfsup_pretrain/mask_rcnn_r50_fpn_swav-pretrain_ms-2x_coco.py new file mode 100644 index 0000000..fe47361 --- /dev/null +++ b/configs/selfsup_pretrain/mask_rcnn_r50_fpn_swav-pretrain_ms-2x_coco.py @@ -0,0 +1,32 @@ +_base_ = [ + '../_base_/models/mask_rcnn_r50_fpn.py', + '../_base_/datasets/coco_instance.py', + '../_base_/schedules/schedule_2x.py', '../_base_/default_runtime.py' +] + +model = dict( + backbone=dict( + frozen_stages=0, + norm_cfg=dict(type='SyncBN', requires_grad=True), + norm_eval=False, + init_cfg=dict( + type='Pretrained', checkpoint='./swav_800ep_pretrain.pth.tar'))) + +img_norm_cfg = dict( + mean=[123.675, 116.28, 103.53], std=[58.395, 57.12, 57.375], to_rgb=True) +train_pipeline = [ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations', with_bbox=True, with_mask=True), + dict( + type='Resize', + img_scale=[(1333, 640), (1333, 800)], + multiscale_mode='range', + keep_ratio=True), + dict(type='RandomFlip', flip_ratio=0.5), + dict(type='Normalize', **img_norm_cfg), + dict(type='Pad', size_divisor=32), + dict(type='DefaultFormatBundle'), + dict(type='Collect', keys=['img', 'gt_bboxes', 'gt_labels', 'gt_masks']) +] + +data = dict(train=dict(pipeline=train_pipeline)) diff --git a/configs/sparse_rcnn/README.md b/configs/sparse_rcnn/README.md index 60cc8a9..bd5f157 100644 --- a/configs/sparse_rcnn/README.md +++ b/configs/sparse_rcnn/README.md @@ -2,7 +2,7 @@ ## Introduction -[ALGORITHM] + ``` @article{peize2020sparse, diff --git a/configs/sparse_rcnn/metafile.yml b/configs/sparse_rcnn/metafile.yml new file mode 100644 index 0000000..bb1273e --- /dev/null +++ b/configs/sparse_rcnn/metafile.yml @@ -0,0 +1,80 @@ +Collections: + - Name: Sparse R-CNN + Metadata: + Training Data: COCO + Training Techniques: + - SGD with Momentum + - Weight Decay + Training Resources: 8x V100 GPUs + Architecture: + - FPN + - ResNet + - Sparse R-CNN + Paper: + URL: https://arxiv.org/abs/2011.12450 + Title: 'Sparse R-CNN: End-to-End Object Detection with Learnable Proposals' + README: configs/sparse_rcnn/README.md + Code: + URL: https://github.com/open-mmlab/mmdetection/blob/v2.9.0/mmdet/models/detectors/sparse_rcnn.py#L6 + Version: v2.9.0 + +Models: + - Name: sparse_rcnn_r50_fpn_1x_coco + In Collection: Sparse R-CNN + Config: configs/sparse_rcnn/sparse_rcnn_r50_fpn_1x_coco.py + Metadata: + Epochs: 12 + Results: + - Task: Object Detection + Dataset: COCO + Metrics: + box AP: 37.9 + Weights: https://download.openmmlab.com/mmdetection/v2.0/sparse_rcnn/sparse_rcnn_r50_fpn_1x_coco/sparse_rcnn_r50_fpn_1x_coco_20201222_214453-dc79b137.pth + + - Name: sparse_rcnn_r50_fpn_mstrain_480-800_3x_coco + In Collection: Sparse R-CNN + Config: configs/sparse_rcnn/sparse_rcnn_r50_fpn_mstrain_480-800_3x_coco.py + Metadata: + Epochs: 36 + Results: + - Task: Object Detection + Dataset: COCO + Metrics: + box AP: 42.8 + Weights: https://download.openmmlab.com/mmdetection/v2.0/sparse_rcnn/sparse_rcnn_r50_fpn_mstrain_480-800_3x_coco/sparse_rcnn_r50_fpn_mstrain_480-800_3x_coco_20201218_154234-7bc5c054.pth + + - Name: sparse_rcnn_r50_fpn_300_proposals_crop_mstrain_480-800_3x_coco + In Collection: Sparse R-CNN + Config: configs/sparse_rcnn/sparse_rcnn_r50_fpn_300_proposals_crop_mstrain_480-800_3x_coco.py + Metadata: + Epochs: 36 + Results: + - Task: Object Detection + Dataset: COCO + Metrics: + box AP: 45.0 + Weights: https://download.openmmlab.com/mmdetection/v2.0/sparse_rcnn/sparse_rcnn_r50_fpn_300_proposals_crop_mstrain_480-800_3x_coco/sparse_rcnn_r50_fpn_300_proposals_crop_mstrain_480-800_3x_coco_20201223_024605-9fe92701.pth + + - Name: sparse_rcnn_r101_fpn_mstrain_480-800_3x_coco + In Collection: Sparse R-CNN + Config: configs/sparse_rcnn/sparse_rcnn_r101_fpn_mstrain_480-800_3x_coco.py + Metadata: + Epochs: 36 + Results: + - Task: Object Detection + Dataset: COCO + Metrics: + box AP: 44.2 + Weights: https://download.openmmlab.com/mmdetection/v2.0/sparse_rcnn/sparse_rcnn_r101_fpn_mstrain_480-800_3x_coco/sparse_rcnn_r101_fpn_mstrain_480-800_3x_coco_20201223_121552-6c46c9d6.pth + + - Name: sparse_rcnn_r101_fpn_300_proposals_crop_mstrain_480-800_3x_coco + In Collection: Sparse R-CNN + Config: configs/sparse_rcnn/sparse_rcnn_r101_fpn_300_proposals_crop_mstrain_480-800_3x_coco.py + Metadata: + Epochs: 36 + Results: + - Task: Object Detection + Dataset: COCO + Metrics: + box AP: 46.2 + Weights: https://download.openmmlab.com/mmdetection/v2.0/sparse_rcnn/sparse_rcnn_r101_fpn_300_proposals_crop_mstrain_480-800_3x_coco/sparse_rcnn_r101_fpn_300_proposals_crop_mstrain_480-800_3x_coco_20201223_023452-c23c3564.pth diff --git a/configs/sparse_rcnn/sparse_rcnn_r101_fpn_300_proposals_crop_mstrain_480-800_3x_coco.py b/configs/sparse_rcnn/sparse_rcnn_r101_fpn_300_proposals_crop_mstrain_480-800_3x_coco.py index e7a94db..de323bd 100644 --- a/configs/sparse_rcnn/sparse_rcnn_r101_fpn_300_proposals_crop_mstrain_480-800_3x_coco.py +++ b/configs/sparse_rcnn/sparse_rcnn_r101_fpn_300_proposals_crop_mstrain_480-800_3x_coco.py @@ -1,3 +1,7 @@ _base_ = './sparse_rcnn_r50_fpn_300_proposals_crop_mstrain_480-800_3x_coco.py' -model = dict(pretrained='torchvision://resnet101', backbone=dict(depth=101)) +model = dict( + backbone=dict( + depth=101, + init_cfg=dict(type='Pretrained', + checkpoint='torchvision://resnet101'))) diff --git a/configs/sparse_rcnn/sparse_rcnn_r101_fpn_mstrain_480-800_3x_coco.py b/configs/sparse_rcnn/sparse_rcnn_r101_fpn_mstrain_480-800_3x_coco.py index 0439fc1..ab4c5f6 100644 --- a/configs/sparse_rcnn/sparse_rcnn_r101_fpn_mstrain_480-800_3x_coco.py +++ b/configs/sparse_rcnn/sparse_rcnn_r101_fpn_mstrain_480-800_3x_coco.py @@ -1,3 +1,7 @@ _base_ = './sparse_rcnn_r50_fpn_mstrain_480-800_3x_coco.py' -model = dict(pretrained='torchvision://resnet101', backbone=dict(depth=101)) +model = dict( + backbone=dict( + depth=101, + init_cfg=dict(type='Pretrained', + checkpoint='torchvision://resnet101'))) diff --git a/configs/sparse_rcnn/sparse_rcnn_r50_fpn_1x_coco.py b/configs/sparse_rcnn/sparse_rcnn_r50_fpn_1x_coco.py index 512eca6..b383ee4 100644 --- a/configs/sparse_rcnn/sparse_rcnn_r50_fpn_1x_coco.py +++ b/configs/sparse_rcnn/sparse_rcnn_r50_fpn_1x_coco.py @@ -6,7 +6,6 @@ num_proposals = 100 model = dict( type='SparseRCNN', - pretrained='torchvision://resnet50', backbone=dict( type='ResNet', depth=50, @@ -15,7 +14,8 @@ frozen_stages=1, norm_cfg=dict(type='BN', requires_grad=True), norm_eval=True, - style='pytorch'), + style='pytorch', + init_cfg=dict(type='Pretrained', checkpoint='torchvision://resnet50')), neck=dict( type='FPN', in_channels=[256, 512, 1024, 2048], diff --git a/configs/ssd/README.md b/configs/ssd/README.md index 51262d6..fea4f7b 100644 --- a/configs/ssd/README.md +++ b/configs/ssd/README.md @@ -2,7 +2,7 @@ ## Introduction -[ALGORITHM] + ```latex @article{Liu_2016, @@ -13,9 +13,40 @@ } ``` -## Results and models +## Results and models of SSD | Backbone | Size | Style | Lr schd | Mem (GB) | Inf time (fps) | box AP | Config | Download | | :------: | :---: | :---: | :-----: | :------: | :------------: | :----: | :------: | :--------: | -| VGG16 | 300 | caffe | 120e | 10.2 | 43.7 | 25.6 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/ssd/ssd300_coco.py) | [model](http://download.openmmlab.com/mmdetection/v2.0/ssd/ssd300_coco/ssd300_coco_20200307-a92d2092.pth) | [log](http://download.openmmlab.com/mmdetection/v2.0/ssd/ssd300_coco/ssd300_coco_20200307_174216.log.json) | -| VGG16 | 512 | caffe | 120e | 9.3 | 30.7 | 29.4 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/ssd/ssd512_coco.py) | [model](http://download.openmmlab.com/mmdetection/v2.0/ssd/ssd512_coco/ssd512_coco_20200308-038c5591.pth) | [log](http://download.openmmlab.com/mmdetection/v2.0/ssd/ssd512_coco/ssd512_coco_20200308_134447.log.json) | +| VGG16 | 300 | caffe | 120e | 9.9 | 43.7 | 25.5 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/ssd/ssd300_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/ssd/ssd300_coco/ssd300_coco_20210803_015428-d231a06e.pth) | [log](https://download.openmmlab.com/mmdetection/v2.0/ssd/ssd300_coco/ssd300_coco_20210803_015428.log.json) | +| VGG16 | 512 | caffe | 120e | 19.4 | 30.7 | 29.5 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/ssd/ssd512_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/ssd/ssd512_coco/ssd512_coco_20210803_022849-0a47a1ca.pth) | [log](https://download.openmmlab.com/mmdetection/v2.0/ssd/ssd512_coco/ssd512_coco_20210803_022849.log.json) | + +## Results and models of SSD-Lite + +| Backbone | Size | Training from scratch | Lr schd | Mem (GB) | Inf time (fps) | box AP | Config | Download | +| :------------: | :---: | :-------------------: | :-----: | :------: | :------------: | :----: | :------: | :--------: | +| MobileNetV2 | 320 | yes | 600e | 4.0 | 69.9 | 21.3 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/ssd/ssdlite_mobilenetv2_scratch_600e_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/ssd/ssdlite_mobilenetv2_scratch_600e_coco/ssdlite_mobilenetv2_scratch_600e_coco_20210629_110627-974d9307.pth) | [log](https://download.openmmlab.com/mmdetection/v2.0/ssd/ssdlite_mobilenetv2_scratch_600e_coco/ssdlite_mobilenetv2_scratch_600e_coco_20210629_110627.log.json) | + +## Notice + +### Compatibility + +In v2.14.0, [PR5291](https://github.com/open-mmlab/mmdetection/pull/5291) refactored SSD neck and head for more +flexible usage. If users want to use the SSD checkpoint trained in the older versions, we provide a scripts +`tools/model_converters/upgrade_ssd_version.py` to convert the model weights. + +```bash +python tools/model_converters/upgrade_ssd_version.py ${OLD_MODEL_PATH} ${NEW_MODEL_PATH} + +``` + +- OLD_MODEL_PATH: the path to load the old version SSD model. +- NEW_MODEL_PATH: the path to save the converted model weights. + +### SSD-Lite training settings + +There are some differences between our implementation of MobileNetV2 SSD-Lite and the one in [TensorFlow 1.x detection model zoo](https://github.com/tensorflow/models/blob/master/research/object_detection/g3doc/tf1_detection_zoo.md) . + +1. Use 320x320 as input size instead of 300x300. +2. The anchor sizes are different. +3. The C4 feature map is taken from the last layer of stage 4 instead of the middle of the block. +4. The model in TensorFlow1.x is trained on coco 2014 and validated on coco minival2014, but we trained and validated the model on coco 2017. The mAP on val2017 is usually a little lower than minival2014 (refer to the results in TensorFlow Object Detection API, e.g., MobileNetV2 SSD gets 22 mAP on minival2014 but 20.2 mAP on val2017). diff --git a/configs/ssd/metafile.yml b/configs/ssd/metafile.yml new file mode 100644 index 0000000..b9ee79c --- /dev/null +++ b/configs/ssd/metafile.yml @@ -0,0 +1,78 @@ +Collections: + - Name: SSD + Metadata: + Training Data: COCO + Training Techniques: + - SGD with Momentum + - Weight Decay + Training Resources: 8x V100 GPUs + Architecture: + - VGG + Paper: + URL: https://arxiv.org/abs/1512.02325 + Title: 'SSD: Single Shot MultiBox Detector' + README: configs/ssd/README.md + Code: + URL: https://github.com/open-mmlab/mmdetection/blob/v2.14.0/mmdet/models/dense_heads/ssd_head.py#L16 + Version: v2.14.0 + +Models: + - Name: ssd300_coco + In Collection: SSD + Config: configs/ssd/ssd300_coco.py + Metadata: + Training Memory (GB): 9.9 + inference time (ms/im): + - value: 22.88 + hardware: V100 + backend: PyTorch + batch size: 1 + mode: FP32 + resolution: (300, 300) + Epochs: 120 + Results: + - Task: Object Detection + Dataset: COCO + Metrics: + box AP: 25.5 + Weights: https://download.openmmlab.com/mmdetection/v2.0/ssd/ssd300_coco/ssd300_coco_20210803_015428-d231a06e.pth + + - Name: ssd512_coco + In Collection: SSD + Config: configs/ssd/ssd512_coco.py + Metadata: + Training Memory (GB): 19.4 + inference time (ms/im): + - value: 32.57 + hardware: V100 + backend: PyTorch + batch size: 1 + mode: FP32 + resolution: (512, 512) + Epochs: 120 + Results: + - Task: Object Detection + Dataset: COCO + Metrics: + box AP: 29.5 + Weights: https://download.openmmlab.com/mmdetection/v2.0/ssd/ssd512_coco/ssd512_coco_20210803_022849-0a47a1ca.pth + + - Name: ssdlite_mobilenetv2_scratch_600e_coco + In Collection: SSD + Config: configs/ssd/ssdlite_mobilenetv2_scratch_600e_coco.py + Metadata: + Training Memory (GB): 4.0 + inference time (ms/im): + - value: 14.3 + hardware: V100 + backend: PyTorch + batch size: 1 + mode: FP32 + resolution: (320, 320) + Epochs: 600 + Results: + - Task: Object Detection + Dataset: COCO + Metrics: + box AP: 21.3 + Weights: https://download.openmmlab.com/mmdetection/v2.0/ssd/ssdlite_mobilenetv2_scratch_600e_coco/ssdlite_mobilenetv2_scratch_600e_coco_20210629_110627-974d9307.pth diff --git a/configs/ssd/ssd300_coco.py b/configs/ssd/ssd300_coco.py index 75c5e4e..1098187 100644 --- a/configs/ssd/ssd300_coco.py +++ b/configs/ssd/ssd300_coco.py @@ -60,3 +60,7 @@ # optimizer optimizer = dict(type='SGD', lr=2e-3, momentum=0.9, weight_decay=5e-4) optimizer_config = dict(_delete_=True) +custom_hooks = [ + dict(type='NumClassCheckHook'), + dict(type='CheckInvalidLossHook', interval=50, priority='VERY_LOW') +] diff --git a/configs/ssd/ssd512_coco.py b/configs/ssd/ssd512_coco.py index 44d2920..3d5f346 100644 --- a/configs/ssd/ssd512_coco.py +++ b/configs/ssd/ssd512_coco.py @@ -1,7 +1,11 @@ _base_ = 'ssd300_coco.py' input_size = 512 model = dict( - backbone=dict(input_size=input_size), + neck=dict( + out_channels=(512, 1024, 512, 256, 256, 256, 256), + level_strides=(2, 2, 2, 2, 1), + level_paddings=(1, 1, 1, 1, 1), + last_kernel_size=4), bbox_head=dict( in_channels=(512, 1024, 512, 256, 256, 256, 256), anchor_generator=dict( @@ -69,3 +73,7 @@ # optimizer optimizer = dict(type='SGD', lr=2e-3, momentum=0.9, weight_decay=5e-4) optimizer_config = dict(_delete_=True) +custom_hooks = [ + dict(type='NumClassCheckHook'), + dict(type='CheckInvalidLossHook', interval=50, priority='VERY_LOW') +] diff --git a/configs/ssd/ssdlite_mobilenetv2_scratch_600e_coco.py b/configs/ssd/ssdlite_mobilenetv2_scratch_600e_coco.py new file mode 100644 index 0000000..653fff1 --- /dev/null +++ b/configs/ssd/ssdlite_mobilenetv2_scratch_600e_coco.py @@ -0,0 +1,145 @@ +_base_ = [ + '../_base_/datasets/coco_detection.py', '../_base_/default_runtime.py' +] + +model = dict( + type='SingleStageDetector', + backbone=dict( + type='MobileNetV2', + out_indices=(4, 7), + norm_cfg=dict(type='BN', eps=0.001, momentum=0.03), + init_cfg=dict(type='TruncNormal', layer='Conv2d', std=0.03)), + neck=dict( + type='SSDNeck', + in_channels=(96, 1280), + out_channels=(96, 1280, 512, 256, 256, 128), + level_strides=(2, 2, 2, 2), + level_paddings=(1, 1, 1, 1), + l2_norm_scale=None, + use_depthwise=True, + norm_cfg=dict(type='BN', eps=0.001, momentum=0.03), + act_cfg=dict(type='ReLU6'), + init_cfg=dict(type='TruncNormal', layer='Conv2d', std=0.03)), + bbox_head=dict( + type='SSDHead', + in_channels=(96, 1280, 512, 256, 256, 128), + num_classes=80, + use_depthwise=True, + norm_cfg=dict(type='BN', eps=0.001, momentum=0.03), + act_cfg=dict(type='ReLU6'), + init_cfg=dict(type='Normal', layer='Conv2d', std=0.001), + + # set anchor size manually instead of using the predefined + # SSD300 setting. + anchor_generator=dict( + type='SSDAnchorGenerator', + scale_major=False, + strides=[16, 32, 64, 107, 160, 320], + ratios=[[2, 3], [2, 3], [2, 3], [2, 3], [2, 3], [2, 3]], + min_sizes=[48, 100, 150, 202, 253, 304], + max_sizes=[100, 150, 202, 253, 304, 320]), + bbox_coder=dict( + type='DeltaXYWHBBoxCoder', + target_means=[.0, .0, .0, .0], + target_stds=[0.1, 0.1, 0.2, 0.2])), + # model training and testing settings + train_cfg=dict( + assigner=dict( + type='MaxIoUAssigner', + pos_iou_thr=0.5, + neg_iou_thr=0.5, + min_pos_iou=0., + ignore_iof_thr=-1, + gt_max_assign_all=False), + smoothl1_beta=1., + allowed_border=-1, + pos_weight=-1, + neg_pos_ratio=3, + debug=False), + test_cfg=dict( + nms_pre=1000, + nms=dict(type='nms', iou_threshold=0.45), + min_bbox_size=0, + score_thr=0.02, + max_per_img=200)) +cudnn_benchmark = True + +# dataset settings +dataset_type = 'CocoDataset' +data_root = 'data/coco/' +img_norm_cfg = dict( + mean=[123.675, 116.28, 103.53], std=[58.395, 57.12, 57.375], to_rgb=True) +train_pipeline = [ + dict(type='LoadImageFromFile', to_float32=True), + dict(type='LoadAnnotations', with_bbox=True), + dict( + type='PhotoMetricDistortion', + brightness_delta=32, + contrast_range=(0.5, 1.5), + saturation_range=(0.5, 1.5), + hue_delta=18), + dict( + type='Expand', + mean=img_norm_cfg['mean'], + to_rgb=img_norm_cfg['to_rgb'], + ratio_range=(1, 4)), + dict( + type='MinIoURandomCrop', + min_ious=(0.1, 0.3, 0.5, 0.7, 0.9), + min_crop_size=0.3), + dict(type='Resize', img_scale=(320, 320), keep_ratio=False), + dict(type='Normalize', **img_norm_cfg), + dict(type='RandomFlip', flip_ratio=0.5), + dict(type='Pad', size_divisor=320), + dict(type='DefaultFormatBundle'), + dict(type='Collect', keys=['img', 'gt_bboxes', 'gt_labels']), +] +test_pipeline = [ + dict(type='LoadImageFromFile'), + dict( + type='MultiScaleFlipAug', + img_scale=(320, 320), + flip=False, + transforms=[ + dict(type='Resize', keep_ratio=False), + dict(type='Normalize', **img_norm_cfg), + dict(type='Pad', size_divisor=320), + dict(type='ImageToTensor', keys=['img']), + dict(type='Collect', keys=['img']), + ]) +] +data = dict( + samples_per_gpu=24, + workers_per_gpu=4, + train=dict( + _delete_=True, + type='RepeatDataset', # use RepeatDataset to speed up training + times=5, + dataset=dict( + type=dataset_type, + ann_file=data_root + 'annotations/instances_train2017.json', + img_prefix=data_root + 'train2017/', + pipeline=train_pipeline)), + val=dict(pipeline=test_pipeline), + test=dict(pipeline=test_pipeline)) + +# optimizer +optimizer = dict(type='SGD', lr=0.015, momentum=0.9, weight_decay=4.0e-5) +optimizer_config = dict(grad_clip=None) + +# learning policy +lr_config = dict( + policy='CosineAnnealing', + warmup='linear', + warmup_iters=500, + warmup_ratio=0.001, + min_lr=0) +runner = dict(type='EpochBasedRunner', max_epochs=120) + +# Avoid evaluation and saving weights too frequently +evaluation = dict(interval=5, metric='bbox') +checkpoint_config = dict(interval=5) +custom_hooks = [ + dict(type='NumClassCheckHook'), + dict(type='CheckInvalidLossHook', interval=50, priority='VERY_LOW') +] diff --git a/configs/swin/mask_rcnn_swin-s-p4-w7_fpn_fp16_ms-crop-3x_coco.py b/configs/swin/mask_rcnn_swin-s-p4-w7_fpn_fp16_ms-crop-3x_coco.py new file mode 100644 index 0000000..237a169 --- /dev/null +++ b/configs/swin/mask_rcnn_swin-s-p4-w7_fpn_fp16_ms-crop-3x_coco.py @@ -0,0 +1,5 @@ +_base_ = './mask_rcnn_swin-t-p4-w7_fpn_fp16_ms-crop-3x_coco.py' +pretrained = 'https://download.openmmlab.com/mmclassification/v0/swin-transformer/swin_small_224_b16x64_300e_imagenet_20210615_110219-7f9d988b.pth' # noqa +model = dict( + backbone=dict(depths=[2, 2, 18, 2]), + init_cfg=dict(type='Pretrained', checkpoint=pretrained)) diff --git a/configs/swin/mask_rcnn_swin-t-p4-w7_fpn_1x_coco.py b/configs/swin/mask_rcnn_swin-t-p4-w7_fpn_1x_coco.py new file mode 100644 index 0000000..7f51ca4 --- /dev/null +++ b/configs/swin/mask_rcnn_swin-t-p4-w7_fpn_1x_coco.py @@ -0,0 +1,41 @@ +_base_ = [ + '../_base_/models/mask_rcnn_r50_fpn.py', + '../_base_/datasets/coco_instance.py', + '../_base_/schedules/schedule_1x.py', '../_base_/default_runtime.py' +] +pretrained = 'https://download.openmmlab.com/mmclassification/v0/swin-transformer/swin_tiny_224_b16x64_300e_imagenet_20210616_090925-66df6be6.pth' # noqa +model = dict( + type='MaskRCNN', + backbone=dict( + _delete_=True, + type='SwinTransformer', + embed_dims=96, + depths=[2, 2, 6, 2], + num_heads=[3, 6, 12, 24], + window_size=7, + mlp_ratio=4, + qkv_bias=True, + qk_scale=None, + drop_rate=0., + attn_drop_rate=0., + drop_path_rate=0.2, + patch_norm=True, + out_indices=(0, 1, 2, 3), + with_cp=False, + init_cfg=dict(type='Pretrained', checkpoint=pretrained)), + neck=dict(in_channels=[96, 192, 384, 768])) + +optimizer = dict( + _delete_=True, + type='AdamW', + lr=0.0001, + betas=(0.9, 0.999), + weight_decay=0.05, + paramwise_cfg=dict( + custom_keys={ + 'absolute_pos_embed': dict(decay_mult=0.), + 'relative_position_bias_table': dict(decay_mult=0.), + 'norm': dict(decay_mult=0.) + })) +lr_config = dict(warmup_iters=1000, step=[8, 11]) +runner = dict(max_epochs=12) diff --git a/configs/swin/mask_rcnn_swin-t-p4-w7_fpn_fp16_ms-crop-3x_coco.py b/configs/swin/mask_rcnn_swin-t-p4-w7_fpn_fp16_ms-crop-3x_coco.py new file mode 100644 index 0000000..2be3114 --- /dev/null +++ b/configs/swin/mask_rcnn_swin-t-p4-w7_fpn_fp16_ms-crop-3x_coco.py @@ -0,0 +1,3 @@ +_base_ = './mask_rcnn_swin-t-p4-w7_fpn_ms-crop-3x_coco.py' +# you need to set mode='dynamic' if you are using pytorch<=1.5.0 +fp16 = dict(loss_scale=dict(init_scale=512)) diff --git a/configs/swin/mask_rcnn_swin-t-p4-w7_fpn_ms-crop-3x_coco.py b/configs/swin/mask_rcnn_swin-t-p4-w7_fpn_ms-crop-3x_coco.py new file mode 100644 index 0000000..9124b18 --- /dev/null +++ b/configs/swin/mask_rcnn_swin-t-p4-w7_fpn_ms-crop-3x_coco.py @@ -0,0 +1,90 @@ +_base_ = [ + '../_base_/models/mask_rcnn_r50_fpn.py', + '../_base_/datasets/coco_instance.py', + '../_base_/schedules/schedule_1x.py', '../_base_/default_runtime.py' +] + +pretrained = 'https://download.openmmlab.com/mmclassification/v0/swin-transformer/swin_tiny_224_b16x64_300e_imagenet_20210616_090925-66df6be6.pth' # noqa + +model = dict( + type='MaskRCNN', + backbone=dict( + _delete_=True, + type='SwinTransformer', + embed_dims=96, + depths=[2, 2, 6, 2], + num_heads=[3, 6, 12, 24], + window_size=7, + mlp_ratio=4, + qkv_bias=True, + qk_scale=None, + drop_rate=0., + attn_drop_rate=0., + drop_path_rate=0.2, + patch_norm=True, + out_indices=(0, 1, 2, 3), + with_cp=False, + init_cfg=dict(type='Pretrained', checkpoint=pretrained)), + neck=dict(in_channels=[96, 192, 384, 768])) + +img_norm_cfg = dict( + mean=[123.675, 116.28, 103.53], std=[58.395, 57.12, 57.375], to_rgb=True) + +# augmentation strategy originates from DETR / Sparse RCNN +train_pipeline = [ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations', with_bbox=True, with_mask=True), + dict(type='RandomFlip', flip_ratio=0.5), + dict( + type='AutoAugment', + policies=[[ + dict( + type='Resize', + img_scale=[(480, 1333), (512, 1333), (544, 1333), (576, 1333), + (608, 1333), (640, 1333), (672, 1333), (704, 1333), + (736, 1333), (768, 1333), (800, 1333)], + multiscale_mode='value', + keep_ratio=True) + ], + [ + dict( + type='Resize', + img_scale=[(400, 1333), (500, 1333), (600, 1333)], + multiscale_mode='value', + keep_ratio=True), + dict( + type='RandomCrop', + crop_type='absolute_range', + crop_size=(384, 600), + allow_negative_crop=True), + dict( + type='Resize', + img_scale=[(480, 1333), (512, 1333), (544, 1333), + (576, 1333), (608, 1333), (640, 1333), + (672, 1333), (704, 1333), (736, 1333), + (768, 1333), (800, 1333)], + multiscale_mode='value', + override=True, + keep_ratio=True) + ]]), + dict(type='Normalize', **img_norm_cfg), + dict(type='Pad', size_divisor=32), + dict(type='DefaultFormatBundle'), + dict(type='Collect', keys=['img', 'gt_bboxes', 'gt_labels', 'gt_masks']), +] +data = dict(train=dict(pipeline=train_pipeline)) + +optimizer = dict( + _delete_=True, + type='AdamW', + lr=0.0001, + betas=(0.9, 0.999), + weight_decay=0.05, + paramwise_cfg=dict( + custom_keys={ + 'absolute_pos_embed': dict(decay_mult=0.), + 'relative_position_bias_table': dict(decay_mult=0.), + 'norm': dict(decay_mult=0.) + })) +lr_config = dict(warmup_iters=1000, step=[27, 33]) +runner = dict(max_epochs=36) diff --git a/configs/tridentnet/README.md b/configs/tridentnet/README.md index 8ab7c28..b6263f2 100644 --- a/configs/tridentnet/README.md +++ b/configs/tridentnet/README.md @@ -2,7 +2,7 @@ ## Introduction -[ALGORITHM] + ``` @InProceedings{li2019scale, diff --git a/configs/tridentnet/metafile.yml b/configs/tridentnet/metafile.yml new file mode 100644 index 0000000..2536f97 --- /dev/null +++ b/configs/tridentnet/metafile.yml @@ -0,0 +1,55 @@ +Collections: + - Name: TridentNet + Metadata: + Training Data: COCO + Training Techniques: + - SGD with Momentum + - Weight Decay + Training Resources: 8x V100 GPUs + Architecture: + - ResNet + - TridentNet Block + Paper: + URL: https://arxiv.org/abs/1901.01892 + Title: 'Scale-Aware Trident Networks for Object Detection' + README: configs/tridentnet/README.md + Code: + URL: https://github.com/open-mmlab/mmdetection/blob/v2.8.0/mmdet/models/detectors/trident_faster_rcnn.py#L6 + Version: v2.8.0 + +Models: + - Name: tridentnet_r50_caffe_1x_coco + In Collection: TridentNet + Config: configs/tridentnet/tridentnet_r50_caffe_1x_coco.py + Metadata: + Epochs: 12 + Results: + - Task: Object Detection + Dataset: COCO + Metrics: + box AP: 37.7 + Weights: https://download.openmmlab.com/mmdetection/v2.0/tridentnet/tridentnet_r50_caffe_1x_coco/tridentnet_r50_caffe_1x_coco_20201230_141838-2ec0b530.pth + + - Name: tridentnet_r50_caffe_mstrain_1x_coco + In Collection: TridentNet + Config: configs/tridentnet/tridentnet_r50_caffe_mstrain_1x_coco.py + Metadata: + Epochs: 12 + Results: + - Task: Object Detection + Dataset: COCO + Metrics: + box AP: 37.6 + Weights: https://download.openmmlab.com/mmdetection/v2.0/tridentnet/tridentnet_r50_caffe_mstrain_1x_coco/tridentnet_r50_caffe_mstrain_1x_coco_20201230_141839-6ce55ccb.pth + + - Name: tridentnet_r50_caffe_mstrain_3x_coco + In Collection: TridentNet + Config: configs/tridentnet/tridentnet_r50_caffe_mstrain_3x_coco.py + Metadata: + Epochs: 36 + Results: + - Task: Object Detection + Dataset: COCO + Metrics: + box AP: 40.3 + Weights: https://download.openmmlab.com/mmdetection/v2.0/tridentnet/tridentnet_r50_caffe_mstrain_3x_coco/tridentnet_r50_caffe_mstrain_3x_coco_20201130_100539-46d227ba.pth diff --git a/configs/tridentnet/tridentnet_r50_caffe_1x_coco.py b/configs/tridentnet/tridentnet_r50_caffe_1x_coco.py index a6a668c..d779f75 100644 --- a/configs/tridentnet/tridentnet_r50_caffe_1x_coco.py +++ b/configs/tridentnet/tridentnet_r50_caffe_1x_coco.py @@ -6,12 +6,14 @@ model = dict( type='TridentFasterRCNN', - pretrained='open-mmlab://detectron2/resnet50_caffe', backbone=dict( type='TridentResNet', trident_dilations=(1, 2, 3), num_branch=3, - test_branch_idx=1), + test_branch_idx=1, + init_cfg=dict( + type='Pretrained', + checkpoint='open-mmlab://detectron2/resnet50_caffe')), roi_head=dict(type='TridentRoIHead', num_branch=3, test_branch_idx=1), train_cfg=dict( rpn_proposal=dict(max_per_img=500), diff --git a/configs/vfnet/README.md b/configs/vfnet/README.md index d1a94d1..363f1b9 100644 --- a/configs/vfnet/README.md +++ b/configs/vfnet/README.md @@ -2,7 +2,7 @@ ## Introduction -[ALGORITHM] + **VarifocalNet (VFNet)** learns to predict the IoU-aware classification score which mixes the object presence confidence and localization accuracy together as the detection score for a bounding box. The learning is supervised by the proposed Varifocal Loss (VFL), based on a new star-shaped bounding box feature representation (the features at nine yellow sampling points). Given the new representation, the object localization accuracy is further improved by refining the initially regressed bounding box. The full paper is available at: [https://arxiv.org/abs/2008.13367](https://arxiv.org/abs/2008.13367). @@ -26,14 +26,14 @@ | Backbone | Style | DCN | MS train | Lr schd |Inf time (fps) | box AP (val) | box AP (test-dev) | Config | Download | |:------------:|:---------:|:-------:|:--------:|:-------:|:-------------:|:------------:|:-----------------:|:------:|:--------:| -| R-50 | pytorch | N | N | 1x | - | 41.6 | 41.6 | [config](https://github.com/open-mmlab/mmdetection/blob/master/configs/vfnet_r50_fpn_1x_coco.py) | [model](https://openmmlab.oss-cn-hangzhou.aliyuncs.com/mmdetection/v2.0/vfnet/vfnet_r50_fpn_1x_coco/vfnet_r50_fpn_1x_coco_20201027-38db6f58.pth) | [log](https://openmmlab.oss-cn-hangzhou.aliyuncs.com/mmdetection/v2.0/vfnet/vfnet_r50_fpn_1x_coco/vfnet_r50_fpn_1x_coco.json)| -| R-50 | pytorch | N | Y | 2x | - | 44.5 | 44.8 | [config](https://github.com/open-mmlab/mmdetection/blob/master/configs/vfnet_r50_fpn_mstrain_2x_coco.py) | [model](https://openmmlab.oss-cn-hangzhou.aliyuncs.com/mmdetection/v2.0/vfnet/vfnet_r50_fpn_mstrain_2x_coco/vfnet_r50_fpn_mstrain_2x_coco_20201027-7cc75bd2.pth) | [log](https://openmmlab.oss-cn-hangzhou.aliyuncs.com/mmdetection/v2.0/vfnet/vfnet_r50_fpn_mstrain_2x_coco/vfnet_r50_fpn_mstrain_2x_coco.json)| -| R-50 | pytorch | Y | Y | 2x | - | 47.8 | 48.0 | [config](https://github.com/open-mmlab/mmdetection/blob/master/configs/vfnet_r50_fpn_mdconv_c3-c5_mstrain_2x_coco.py) | [model](https://openmmlab.oss-cn-hangzhou.aliyuncs.com/mmdetection/v2.0/vfnet/vfnet_r50_fpn_mdconv_c3-c5_mstrain_2x_coco/vfnet_r50_fpn_mdconv_c3-c5_mstrain_2x_coco_20201027pth-6879c318.pth) | [log](https://openmmlab.oss-cn-hangzhou.aliyuncs.com/mmdetection/v2.0/vfnet/vfnet_r50_fpn_mdconv_c3-c5_mstrain_2x_coco/vfnet_r50_fpn_mdconv_c3-c5_mstrain_2x_coco.json)| -| R-101 | pytorch | N | N | 1x | - | 43.0 | 43.6 | [config](https://github.com/open-mmlab/mmdetection/blob/master/configs/vfnet_r101_fpn_1x_coco.py) | [model](https://openmmlab.oss-cn-hangzhou.aliyuncs.com/mmdetection/v2.0/vfnet/vfnet_r101_fpn_1x_coco/vfnet_r101_fpn_1x_coco_20201027pth-c831ece7.pth) | [log](https://openmmlab.oss-cn-hangzhou.aliyuncs.com/mmdetection/v2.0/vfnet/vfnet_r101_fpn_1x_coco/vfnet_r101_fpn_1x_coco.json)| -| R-101 | pytorch | N | Y | 2x | - | 46.2 | 46.7 | [config](https://github.com/open-mmlab/mmdetection/blob/master/configs/vfnet_r101_fpn_mstrain_2x_coco.py) | [model](https://openmmlab.oss-cn-hangzhou.aliyuncs.com/mmdetection/v2.0/vfnet/vfnet_r101_fpn_mstrain_2x_coco/vfnet_r101_fpn_mstrain_2x_coco_20201027pth-4a5d53f1.pth) | [log](https://openmmlab.oss-cn-hangzhou.aliyuncs.com/mmdetection/v2.0/vfnet/vfnet_r101_fpn_mstrain_2x_coco/vfnet_r101_fpn_mstrain_2x_coco.json)| -| R-101 | pytorch | Y | Y | 2x | - | 49.0 | 49.2 | [config](https://github.com/open-mmlab/mmdetection/blob/master/configs/vfnet_r101_fpn_mdconv_c3-c5_mstrain_2x_coco.py) | [model](https://openmmlab.oss-cn-hangzhou.aliyuncs.com/mmdetection/v2.0/vfnet/vfnet_r101_fpn_mdconv_c3-c5_mstrain_2x_coco/vfnet_r101_fpn_mdconv_c3-c5_mstrain_2x_coco_20201027pth-7729adb5.pth) | [log](https://openmmlab.oss-cn-hangzhou.aliyuncs.com/mmdetection/v2.0/vfnet/vfnet_r101_fpn_mdconv_c3-c5_mstrain_2x_coco/vfnet_r101_fpn_mdconv_c3-c5_mstrain_2x_coco.json)| -| X-101-32x4d | pytorch | Y | Y | 2x | - | 49.7 | 50.0 | [config](https://github.com/open-mmlab/mmdetection/blob/master/configs/vfnet_x101_32x4d_fpn_mdconv_c3-c5_mstrain_2x_coco.py) | [model](https://openmmlab.oss-cn-hangzhou.aliyuncs.com/mmdetection/v2.0/vfnet/vfnet_x101_32x4d_fpn_mdconv_c3-c5_mstrain_2x_coco/vfnet_x101_32x4d_fpn_mdconv_c3-c5_mstrain_2x_coco_20201027pth-d300a6fc.pth) | [log](https://openmmlab.oss-cn-hangzhou.aliyuncs.com/mmdetection/v2.0/vfnet/vfnet_x101_32x4d_fpn_mdconv_c3-c5_mstrain_2x_coco/vfnet_x101_32x4d_fpn_mdconv_c3-c5_mstrain_2x_coco.json)| -| X-101-64x4d | pytorch | Y | Y | 2x | - | 50.4 | 50.8 | [config](https://github.com/open-mmlab/mmdetection/blob/master/configs/vfnet_x101_64x4d_fpn_mdconv_c3-c5_mstrain_2x_coco.py) | [model](https://openmmlab.oss-cn-hangzhou.aliyuncs.com/mmdetection/v2.0/vfnet/vfnet_x101_64x4d_fpn_mdconv_c3-c5_mstrain_2x_coco/vfnet_x101_64x4d_fpn_mdconv_c3-c5_mstrain_2x_coco_20201027pth-b5f6da5e.pth) | [log](https://openmmlab.oss-cn-hangzhou.aliyuncs.com/mmdetection/v2.0/vfnet/vfnet_x101_64x4d_fpn_mdconv_c3-c5_mstrain_2x_coco/vfnet_x101_64x4d_fpn_mdconv_c3-c5_mstrain_2x_coco.json)| +| R-50 | pytorch | N | N | 1x | - | 41.6 | 41.6 | [config](https://github.com/open-mmlab/mmdetection/blob/master/configs/vfnet/vfnet_r50_fpn_1x_coco.py) | [model](https://openmmlab.oss-cn-hangzhou.aliyuncs.com/mmdetection/v2.0/vfnet/vfnet_r50_fpn_1x_coco/vfnet_r50_fpn_1x_coco_20201027-38db6f58.pth) | [log](https://openmmlab.oss-cn-hangzhou.aliyuncs.com/mmdetection/v2.0/vfnet/vfnet_r50_fpn_1x_coco/vfnet_r50_fpn_1x_coco.json)| +| R-50 | pytorch | N | Y | 2x | - | 44.5 | 44.8 | [config](https://github.com/open-mmlab/mmdetection/blob/master/configs/vfnet/vfnet_r50_fpn_mstrain_2x_coco.py) | [model](https://openmmlab.oss-cn-hangzhou.aliyuncs.com/mmdetection/v2.0/vfnet/vfnet_r50_fpn_mstrain_2x_coco/vfnet_r50_fpn_mstrain_2x_coco_20201027-7cc75bd2.pth) | [log](https://openmmlab.oss-cn-hangzhou.aliyuncs.com/mmdetection/v2.0/vfnet/vfnet_r50_fpn_mstrain_2x_coco/vfnet_r50_fpn_mstrain_2x_coco.json)| +| R-50 | pytorch | Y | Y | 2x | - | 47.8 | 48.0 | [config](https://github.com/open-mmlab/mmdetection/blob/master/configs/vfnet/vfnet_r50_fpn_mdconv_c3-c5_mstrain_2x_coco.py) | [model](https://openmmlab.oss-cn-hangzhou.aliyuncs.com/mmdetection/v2.0/vfnet/vfnet_r50_fpn_mdconv_c3-c5_mstrain_2x_coco/vfnet_r50_fpn_mdconv_c3-c5_mstrain_2x_coco_20201027pth-6879c318.pth) | [log](https://openmmlab.oss-cn-hangzhou.aliyuncs.com/mmdetection/v2.0/vfnet/vfnet_r50_fpn_mdconv_c3-c5_mstrain_2x_coco/vfnet_r50_fpn_mdconv_c3-c5_mstrain_2x_coco.json)| +| R-101 | pytorch | N | N | 1x | - | 43.0 | 43.6 | [config](https://github.com/open-mmlab/mmdetection/blob/master/configs/vfnet/vfnet_r101_fpn_1x_coco.py) | [model](https://openmmlab.oss-cn-hangzhou.aliyuncs.com/mmdetection/v2.0/vfnet/vfnet_r101_fpn_1x_coco/vfnet_r101_fpn_1x_coco_20201027pth-c831ece7.pth) | [log](https://openmmlab.oss-cn-hangzhou.aliyuncs.com/mmdetection/v2.0/vfnet/vfnet_r101_fpn_1x_coco/vfnet_r101_fpn_1x_coco.json)| +| R-101 | pytorch | N | Y | 2x | - | 46.2 | 46.7 | [config](https://github.com/open-mmlab/mmdetection/blob/master/configs/vfnet/vfnet_r101_fpn_mstrain_2x_coco.py) | [model](https://openmmlab.oss-cn-hangzhou.aliyuncs.com/mmdetection/v2.0/vfnet/vfnet_r101_fpn_mstrain_2x_coco/vfnet_r101_fpn_mstrain_2x_coco_20201027pth-4a5d53f1.pth) | [log](https://openmmlab.oss-cn-hangzhou.aliyuncs.com/mmdetection/v2.0/vfnet/vfnet_r101_fpn_mstrain_2x_coco/vfnet_r101_fpn_mstrain_2x_coco.json)| +| R-101 | pytorch | Y | Y | 2x | - | 49.0 | 49.2 | [config](https://github.com/open-mmlab/mmdetection/blob/master/configs/vfnet/vfnet_r101_fpn_mdconv_c3-c5_mstrain_2x_coco.py) | [model](https://openmmlab.oss-cn-hangzhou.aliyuncs.com/mmdetection/v2.0/vfnet/vfnet_r101_fpn_mdconv_c3-c5_mstrain_2x_coco/vfnet_r101_fpn_mdconv_c3-c5_mstrain_2x_coco_20201027pth-7729adb5.pth) | [log](https://openmmlab.oss-cn-hangzhou.aliyuncs.com/mmdetection/v2.0/vfnet/vfnet_r101_fpn_mdconv_c3-c5_mstrain_2x_coco/vfnet_r101_fpn_mdconv_c3-c5_mstrain_2x_coco.json)| +| X-101-32x4d | pytorch | Y | Y | 2x | - | 49.7 | 50.0 | [config](https://github.com/open-mmlab/mmdetection/blob/master/configs/vfnet/vfnet_x101_32x4d_fpn_mdconv_c3-c5_mstrain_2x_coco.py) | [model](https://openmmlab.oss-cn-hangzhou.aliyuncs.com/mmdetection/v2.0/vfnet/vfnet_x101_32x4d_fpn_mdconv_c3-c5_mstrain_2x_coco/vfnet_x101_32x4d_fpn_mdconv_c3-c5_mstrain_2x_coco_20201027pth-d300a6fc.pth) | [log](https://openmmlab.oss-cn-hangzhou.aliyuncs.com/mmdetection/v2.0/vfnet/vfnet_x101_32x4d_fpn_mdconv_c3-c5_mstrain_2x_coco/vfnet_x101_32x4d_fpn_mdconv_c3-c5_mstrain_2x_coco.json)| +| X-101-64x4d | pytorch | Y | Y | 2x | - | 50.4 | 50.8 | [config](https://github.com/open-mmlab/mmdetection/blob/master/configs/vfnet/vfnet_x101_64x4d_fpn_mdconv_c3-c5_mstrain_2x_coco.py) | [model](https://openmmlab.oss-cn-hangzhou.aliyuncs.com/mmdetection/v2.0/vfnet/vfnet_x101_64x4d_fpn_mdconv_c3-c5_mstrain_2x_coco/vfnet_x101_64x4d_fpn_mdconv_c3-c5_mstrain_2x_coco_20201027pth-b5f6da5e.pth) | [log](https://openmmlab.oss-cn-hangzhou.aliyuncs.com/mmdetection/v2.0/vfnet/vfnet_x101_64x4d_fpn_mdconv_c3-c5_mstrain_2x_coco/vfnet_x101_64x4d_fpn_mdconv_c3-c5_mstrain_2x_coco.json)| **Notes:** diff --git a/configs/vfnet/metafile.yml b/configs/vfnet/metafile.yml new file mode 100644 index 0000000..1d46b16 --- /dev/null +++ b/configs/vfnet/metafile.yml @@ -0,0 +1,116 @@ +Collections: + - Name: VFNet + Metadata: + Training Data: COCO + Training Techniques: + - SGD with Momentum + - Weight Decay + Training Resources: 8x V100 GPUs + Architecture: + - FPN + - ResNet + - Varifocal Loss + Paper: + URL: https://arxiv.org/abs/2008.13367 + Title: 'VarifocalNet: An IoU-aware Dense Object Detector' + README: configs/vfnet/README.md + Code: + URL: https://github.com/open-mmlab/mmdetection/blob/v2.6.0/mmdet/models/detectors/vfnet.py#L6 + Version: v2.6.0 + +Models: + - Name: vfnet_r50_fpn_1x_coco + In Collection: VFNet + Config: configs/vfnet/vfnet_r50_fpn_1x_coco.py + Metadata: + Epochs: 12 + Results: + - Task: Object Detection + Dataset: COCO + Metrics: + box AP: 41.6 + Weights: https://openmmlab.oss-cn-hangzhou.aliyuncs.com/mmdetection/v2.0/vfnet/vfnet_r50_fpn_1x_coco/vfnet_r50_fpn_1x_coco_20201027-38db6f58.pth + + - Name: vfnet_r50_fpn_mstrain_2x_coco + In Collection: VFNet + Config: configs/vfnet/vfnet_r50_fpn_mstrain_2x_coco.py + Metadata: + Epochs: 24 + Results: + - Task: Object Detection + Dataset: COCO + Metrics: + box AP: 44.8 + Weights: https://openmmlab.oss-cn-hangzhou.aliyuncs.com/mmdetection/v2.0/vfnet/vfnet_r50_fpn_mstrain_2x_coco/vfnet_r50_fpn_mstrain_2x_coco_20201027-7cc75bd2.pth + + - Name: vfnet_r50_fpn_mdconv_c3-c5_mstrain_2x_coco + In Collection: VFNet + Config: configs/vfnet/vfnet_r50_fpn_mdconv_c3-c5_mstrain_2x_coco.py + Metadata: + Epochs: 24 + Results: + - Task: Object Detection + Dataset: COCO + Metrics: + box AP: 48.0 + Weights: https://openmmlab.oss-cn-hangzhou.aliyuncs.com/mmdetection/v2.0/vfnet/vfnet_r50_fpn_mdconv_c3-c5_mstrain_2x_coco/vfnet_r50_fpn_mdconv_c3-c5_mstrain_2x_coco_20201027pth-6879c318.pth + + - Name: vfnet_r101_fpn_1x_coco + In Collection: VFNet + Config: configs/vfnet/vfnet_r101_fpn_1x_coco.py + Metadata: + Epochs: 12 + Results: + - Task: Object Detection + Dataset: COCO + Metrics: + box AP: 43.6 + Weights: https://openmmlab.oss-cn-hangzhou.aliyuncs.com/mmdetection/v2.0/vfnet/vfnet_r101_fpn_1x_coco/vfnet_r101_fpn_1x_coco_20201027pth-c831ece7.pth + + - Name: vfnet_r101_fpn_mstrain_2x_coco + In Collection: VFNet + Config: configs/vfnet/vfnet_r101_fpn_mstrain_2x_coco.py + Metadata: + Epochs: 24 + Results: + - Task: Object Detection + Dataset: COCO + Metrics: + box AP: 46.7 + Weights: https://openmmlab.oss-cn-hangzhou.aliyuncs.com/mmdetection/v2.0/vfnet/vfnet_r101_fpn_mstrain_2x_coco/vfnet_r101_fpn_mstrain_2x_coco_20201027pth-4a5d53f1.pth + + - Name: vfnet_r101_fpn_mdconv_c3-c5_mstrain_2x_coco + In Collection: VFNet + Config: configs/vfnet/vfnet_r101_fpn_mdconv_c3-c5_mstrain_2x_coco.py + Metadata: + Epochs: 24 + Results: + - Task: Object Detection + Dataset: COCO + Metrics: + box AP: 49.2 + Weights: https://openmmlab.oss-cn-hangzhou.aliyuncs.com/mmdetection/v2.0/vfnet/vfnet_r101_fpn_mdconv_c3-c5_mstrain_2x_coco/vfnet_r101_fpn_mdconv_c3-c5_mstrain_2x_coco_20201027pth-7729adb5.pth + + - Name: vfnet_x101_32x4d_fpn_mdconv_c3-c5_mstrain_2x_coco + In Collection: VFNet + Config: configs/vfnet/vfnet_x101_32x4d_fpn_mdconv_c3-c5_mstrain_2x_coco.py + Metadata: + Epochs: 24 + Results: + - Task: Object Detection + Dataset: COCO + Metrics: + box AP: 50.0 + Weights: https://openmmlab.oss-cn-hangzhou.aliyuncs.com/mmdetection/v2.0/vfnet/vfnet_x101_32x4d_fpn_mdconv_c3-c5_mstrain_2x_coco/vfnet_x101_32x4d_fpn_mdconv_c3-c5_mstrain_2x_coco_20201027pth-d300a6fc.pth + + - Name: vfnet_x101_64x4d_fpn_mdconv_c3-c5_mstrain_2x_coco + In Collection: VFNet + Config: configs/vfnet/vfnet_x101_64x4d_fpn_mdconv_c3-c5_mstrain_2x_coco.py + Metadata: + Epochs: 24 + Results: + - Task: Object Detection + Dataset: COCO + Metrics: + box AP: 50.8 + Weights: https://openmmlab.oss-cn-hangzhou.aliyuncs.com/mmdetection/v2.0/vfnet/vfnet_x101_64x4d_fpn_mdconv_c3-c5_mstrain_2x_coco/vfnet_x101_64x4d_fpn_mdconv_c3-c5_mstrain_2x_coco_20201027pth-b5f6da5e.pth diff --git a/configs/vfnet/vfnet_r101_fpn_1x_coco.py b/configs/vfnet/vfnet_r101_fpn_1x_coco.py index 0952131..b296a07 100644 --- a/configs/vfnet/vfnet_r101_fpn_1x_coco.py +++ b/configs/vfnet/vfnet_r101_fpn_1x_coco.py @@ -1,2 +1,6 @@ _base_ = './vfnet_r50_fpn_1x_coco.py' -model = dict(pretrained='torchvision://resnet101', backbone=dict(depth=101)) +model = dict( + backbone=dict( + depth=101, + init_cfg=dict(type='Pretrained', + checkpoint='torchvision://resnet101'))) diff --git a/configs/vfnet/vfnet_r101_fpn_2x_coco.py b/configs/vfnet/vfnet_r101_fpn_2x_coco.py index 334657d..27962f3 100644 --- a/configs/vfnet/vfnet_r101_fpn_2x_coco.py +++ b/configs/vfnet/vfnet_r101_fpn_2x_coco.py @@ -1,4 +1,8 @@ _base_ = './vfnet_r50_fpn_1x_coco.py' -model = dict(pretrained='torchvision://resnet101', backbone=dict(depth=101)) +model = dict( + backbone=dict( + depth=101, + init_cfg=dict(type='Pretrained', + checkpoint='torchvision://resnet101'))) lr_config = dict(step=[16, 22]) runner = dict(type='EpochBasedRunner', max_epochs=24) diff --git a/configs/vfnet/vfnet_r101_fpn_mdconv_c3-c5_mstrain_2x_coco.py b/configs/vfnet/vfnet_r101_fpn_mdconv_c3-c5_mstrain_2x_coco.py index f8ef6ec..e438c24 100644 --- a/configs/vfnet/vfnet_r101_fpn_mdconv_c3-c5_mstrain_2x_coco.py +++ b/configs/vfnet/vfnet_r101_fpn_mdconv_c3-c5_mstrain_2x_coco.py @@ -1,6 +1,5 @@ _base_ = './vfnet_r50_fpn_mdconv_c3-c5_mstrain_2x_coco.py' model = dict( - pretrained='torchvision://resnet101', backbone=dict( type='ResNet', depth=101, @@ -11,4 +10,6 @@ norm_eval=True, style='pytorch', dcn=dict(type='DCNv2', deform_groups=1, fallback_on_stride=False), - stage_with_dcn=(False, True, True, True))) + stage_with_dcn=(False, True, True, True), + init_cfg=dict(type='Pretrained', + checkpoint='torchvision://resnet101'))) diff --git a/configs/vfnet/vfnet_r101_fpn_mstrain_2x_coco.py b/configs/vfnet/vfnet_r101_fpn_mstrain_2x_coco.py index be7f075..eae69a0 100644 --- a/configs/vfnet/vfnet_r101_fpn_mstrain_2x_coco.py +++ b/configs/vfnet/vfnet_r101_fpn_mstrain_2x_coco.py @@ -1,2 +1,6 @@ _base_ = './vfnet_r50_fpn_mstrain_2x_coco.py' -model = dict(pretrained='torchvision://resnet101', backbone=dict(depth=101)) +model = dict( + backbone=dict( + depth=101, + init_cfg=dict(type='Pretrained', + checkpoint='torchvision://resnet101'))) diff --git a/configs/vfnet/vfnet_r2_101_fpn_mdconv_c3-c5_mstrain_2x_coco.py b/configs/vfnet/vfnet_r2_101_fpn_mdconv_c3-c5_mstrain_2x_coco.py index 8da3122..815a36e 100644 --- a/configs/vfnet/vfnet_r2_101_fpn_mdconv_c3-c5_mstrain_2x_coco.py +++ b/configs/vfnet/vfnet_r2_101_fpn_mdconv_c3-c5_mstrain_2x_coco.py @@ -1,6 +1,5 @@ _base_ = './vfnet_r50_fpn_mdconv_c3-c5_mstrain_2x_coco.py' model = dict( - pretrained='open-mmlab://res2net101_v1d_26w_4s', backbone=dict( type='Res2Net', depth=101, @@ -13,4 +12,7 @@ norm_eval=True, style='pytorch', dcn=dict(type='DCNv2', deform_groups=1, fallback_on_stride=False), - stage_with_dcn=(False, True, True, True))) + stage_with_dcn=(False, True, True, True), + init_cfg=dict( + type='Pretrained', + checkpoint='open-mmlab://res2net101_v1d_26w_4s'))) diff --git a/configs/vfnet/vfnet_r2_101_fpn_mstrain_2x_coco.py b/configs/vfnet/vfnet_r2_101_fpn_mstrain_2x_coco.py index 2bcf779..58022e0 100644 --- a/configs/vfnet/vfnet_r2_101_fpn_mstrain_2x_coco.py +++ b/configs/vfnet/vfnet_r2_101_fpn_mstrain_2x_coco.py @@ -1,6 +1,5 @@ _base_ = './vfnet_r50_fpn_mstrain_2x_coco.py' model = dict( - pretrained='open-mmlab://res2net101_v1d_26w_4s', backbone=dict( type='Res2Net', depth=101, @@ -11,4 +10,7 @@ frozen_stages=1, norm_cfg=dict(type='BN', requires_grad=True), norm_eval=True, - style='pytorch')) + style='pytorch', + init_cfg=dict( + type='Pretrained', + checkpoint='open-mmlab://res2net101_v1d_26w_4s'))) diff --git a/configs/vfnet/vfnet_r50_fpn_1x_coco.py b/configs/vfnet/vfnet_r50_fpn_1x_coco.py index 76566bd..7de6429 100644 --- a/configs/vfnet/vfnet_r50_fpn_1x_coco.py +++ b/configs/vfnet/vfnet_r50_fpn_1x_coco.py @@ -5,7 +5,6 @@ # model settings model = dict( type='VFNet', - pretrained='torchvision://resnet50', backbone=dict( type='ResNet', depth=50, @@ -14,14 +13,14 @@ frozen_stages=1, norm_cfg=dict(type='BN', requires_grad=True), norm_eval=True, - style='pytorch'), + style='pytorch', + init_cfg=dict(type='Pretrained', checkpoint='torchvision://resnet50')), neck=dict( type='FPN', in_channels=[256, 512, 1024, 2048], out_channels=256, start_level=1, - add_extra_convs=True, - extra_convs_on_inputs=False, # use P5 + add_extra_convs='on_output', # use P5 num_outs=5, relu_before_extra_convs=True), bbox_head=dict( diff --git a/configs/vfnet/vfnet_x101_32x4d_fpn_mdconv_c3-c5_mstrain_2x_coco.py b/configs/vfnet/vfnet_x101_32x4d_fpn_mdconv_c3-c5_mstrain_2x_coco.py index ebeef6f..7efa051 100644 --- a/configs/vfnet/vfnet_x101_32x4d_fpn_mdconv_c3-c5_mstrain_2x_coco.py +++ b/configs/vfnet/vfnet_x101_32x4d_fpn_mdconv_c3-c5_mstrain_2x_coco.py @@ -1,6 +1,5 @@ _base_ = './vfnet_r50_fpn_mdconv_c3-c5_mstrain_2x_coco.py' model = dict( - pretrained='open-mmlab://resnext101_32x4d', backbone=dict( type='ResNeXt', depth=101, @@ -13,4 +12,6 @@ norm_eval=True, style='pytorch', dcn=dict(type='DCNv2', deform_groups=1, fallback_on_stride=False), - stage_with_dcn=(False, True, True, True))) + stage_with_dcn=(False, True, True, True), + init_cfg=dict( + type='Pretrained', checkpoint='open-mmlab://resnext101_32x4d'))) diff --git a/configs/vfnet/vfnet_x101_32x4d_fpn_mstrain_2x_coco.py b/configs/vfnet/vfnet_x101_32x4d_fpn_mstrain_2x_coco.py index 5ed2650..49a4312 100644 --- a/configs/vfnet/vfnet_x101_32x4d_fpn_mstrain_2x_coco.py +++ b/configs/vfnet/vfnet_x101_32x4d_fpn_mstrain_2x_coco.py @@ -1,6 +1,5 @@ _base_ = './vfnet_r50_fpn_mstrain_2x_coco.py' model = dict( - pretrained='open-mmlab://resnext101_32x4d', backbone=dict( type='ResNeXt', depth=101, @@ -11,4 +10,6 @@ frozen_stages=1, norm_cfg=dict(type='BN', requires_grad=True), norm_eval=True, - style='pytorch')) + style='pytorch', + init_cfg=dict( + type='Pretrained', checkpoint='open-mmlab://resnext101_32x4d'))) diff --git a/configs/vfnet/vfnet_x101_64x4d_fpn_mdconv_c3-c5_mstrain_2x_coco.py b/configs/vfnet/vfnet_x101_64x4d_fpn_mdconv_c3-c5_mstrain_2x_coco.py index 2e19078..7e1ee42 100644 --- a/configs/vfnet/vfnet_x101_64x4d_fpn_mdconv_c3-c5_mstrain_2x_coco.py +++ b/configs/vfnet/vfnet_x101_64x4d_fpn_mdconv_c3-c5_mstrain_2x_coco.py @@ -1,6 +1,5 @@ _base_ = './vfnet_r50_fpn_mdconv_c3-c5_mstrain_2x_coco.py' model = dict( - pretrained='open-mmlab://resnext101_64x4d', backbone=dict( type='ResNeXt', depth=101, @@ -13,4 +12,6 @@ norm_eval=True, style='pytorch', dcn=dict(type='DCNv2', deform_groups=1, fallback_on_stride=False), - stage_with_dcn=(False, True, True, True))) + stage_with_dcn=(False, True, True, True), + init_cfg=dict( + type='Pretrained', checkpoint='open-mmlab://resnext101_64x4d'))) diff --git a/configs/vfnet/vfnet_x101_64x4d_fpn_mstrain_2x_coco.py b/configs/vfnet/vfnet_x101_64x4d_fpn_mstrain_2x_coco.py index 4329b34..e51064e 100644 --- a/configs/vfnet/vfnet_x101_64x4d_fpn_mstrain_2x_coco.py +++ b/configs/vfnet/vfnet_x101_64x4d_fpn_mstrain_2x_coco.py @@ -1,6 +1,5 @@ _base_ = './vfnet_r50_fpn_mstrain_2x_coco.py' model = dict( - pretrained='open-mmlab://resnext101_64x4d', backbone=dict( type='ResNeXt', depth=101, @@ -11,4 +10,6 @@ frozen_stages=1, norm_cfg=dict(type='BN', requires_grad=True), norm_eval=True, - style='pytorch')) + style='pytorch', + init_cfg=dict( + type='Pretrained', checkpoint='open-mmlab://resnext101_64x4d'))) diff --git a/configs/wider_face/README.md b/configs/wider_face/README.md index c62e10d..b8fe474 100644 --- a/configs/wider_face/README.md +++ b/configs/wider_face/README.md @@ -1,6 +1,6 @@ # WIDER Face Dataset -[DATASET] + To use the WIDER Face dataset you need to download it and extract to the `data/WIDERFace` folder. Annotation in the VOC format diff --git a/configs/yolact/README.md b/configs/yolact/README.md index 92b1377..da3559b 100644 --- a/configs/yolact/README.md +++ b/configs/yolact/README.md @@ -1,6 +1,6 @@ # **Y**ou **O**nly **L**ook **A**t **C**oefficien**T**s -[ALGORITHM] + ``` ██╗ ██╗ ██████╗ ██╗ █████╗ ██████╗████████╗ @@ -25,9 +25,9 @@ Here are our YOLACT models along with their FPS on a Titan Xp and mAP on COCO's | Image Size | GPU x BS | Backbone | *FPS | mAP | Weights | Configs | Download | |:----------:|:--------:|:-------------:|:-----:|:----:|:-------:|:------:|:--------:| -| 550 | 1x8 | Resnet50-FPN | 42.5 | 29.0 | | [config](https://github.com/open-mmlab/mmdetection/blob/master/configs/yolact_r50_1x8_coco.py) |[model](https://openmmlab.oss-cn-hangzhou.aliyuncs.com/mmdetection/v2.0/yolact/yolact_r50_1x8_coco_20200908-f38d58df.pth) | -| 550 | 8x8 | Resnet50-FPN | 42.5 | 28.4 | | [config](https://github.com/open-mmlab/mmdetection/blob/master/configs/yolact_r50_8x8_coco.py) | [model](https://openmmlab.oss-cn-hangzhou.aliyuncs.com/mmdetection/v2.0/yolact/yolact_r50_8x8_coco_20200908-ca34f5db.pth) | -| 550 | 1x8 | Resnet101-FPN | 33.5 | 30.4 | | [config](https://github.com/open-mmlab/mmdetection/blob/master/configs/yolact_r101_1x8_coco.py) | [model](https://openmmlab.oss-cn-hangzhou.aliyuncs.com/mmdetection/v2.0/yolact/yolact_r101_1x8_coco_20200908-4cbe9101.pth) | +| 550 | 1x8 | Resnet50-FPN | 42.5 | 29.0 | | [config](https://github.com/open-mmlab/mmdetection/blob/master/configs/yolact/yolact_r50_1x8_coco.py) |[model](https://openmmlab.oss-cn-hangzhou.aliyuncs.com/mmdetection/v2.0/yolact/yolact_r50_1x8_coco_20200908-f38d58df.pth) | +| 550 | 8x8 | Resnet50-FPN | 42.5 | 28.4 | | [config](https://github.com/open-mmlab/mmdetection/blob/master/configs/yolact/yolact_r50_8x8_coco.py) | [model](https://openmmlab.oss-cn-hangzhou.aliyuncs.com/mmdetection/v2.0/yolact/yolact_r50_8x8_coco_20200908-ca34f5db.pth) | +| 550 | 1x8 | Resnet101-FPN | 33.5 | 30.4 | | [config](https://github.com/open-mmlab/mmdetection/blob/master/configs/yolact/yolact_r101_1x8_coco.py) | [model](https://openmmlab.oss-cn-hangzhou.aliyuncs.com/mmdetection/v2.0/yolact/yolact_r101_1x8_coco_20200908-4cbe9101.pth) | *Note: The FPS is evaluated by the [original implementation](https://github.com/dbolya/yolact). When calculating FPS, only the model inference time is taken into account. Data loading and post-processing operations such as converting masks to RLE code, generating COCO JSON results, image rendering are not included. diff --git a/configs/yolact/metafile.yml b/configs/yolact/metafile.yml new file mode 100644 index 0000000..159b2e0 --- /dev/null +++ b/configs/yolact/metafile.yml @@ -0,0 +1,78 @@ +Collections: + - Name: YOLACT + Metadata: + Training Data: COCO + Training Techniques: + - SGD with Momentum + - Weight Decay + Training Resources: 8x V100 GPUs + Architecture: + - FPN + - ResNet + Paper: + URL: https://arxiv.org/abs/1904.02689 + Title: 'YOLACT: Real-time Instance Segmentation' + README: configs/yolact/README.md + Code: + URL: https://github.com/open-mmlab/mmdetection/blob/v2.5.0/mmdet/models/detectors/yolact.py#L9 + Version: v2.5.0 + +Models: + - Name: yolact_r50_1x8_coco + In Collection: YOLACT + Config: configs/yolact/yolact_r50_1x8_coco.py + Metadata: + Training Resources: 1x V100 GPU + Batch Size: 8 + inference time (ms/im): + - value: 23.53 + hardware: V100 + backend: PyTorch + batch size: 1 + mode: FP32 + resolution: (550, 550) + Results: + - Task: Instance Segmentation + Dataset: COCO + Metrics: + mask AP: 29.0 + Weights: https://openmmlab.oss-cn-hangzhou.aliyuncs.com/mmdetection/v2.0/yolact/yolact_r50_1x8_coco_20200908-f38d58df.pth + + - Name: yolact_r50_8x8_coco + In Collection: YOLACT + Config: configs/yolact/yolact_r50_8x8_coco.py + Metadata: + Batch Size: 64 + inference time (ms/im): + - value: 23.53 + hardware: V100 + backend: PyTorch + batch size: 1 + mode: FP32 + resolution: (550, 550) + Results: + - Task: Instance Segmentation + Dataset: COCO + Metrics: + mask AP: 28.4 + Weights: https://openmmlab.oss-cn-hangzhou.aliyuncs.com/mmdetection/v2.0/yolact/yolact_r50_8x8_coco_20200908-ca34f5db.pth + + - Name: yolact_r101_1x8_coco + In Collection: YOLACT + Config: configs/yolact/yolact_r101_1x8_coco.py + Metadata: + Training Resources: 1x V100 GPU + Batch Size: 8 + inference time (ms/im): + - value: 29.85 + hardware: V100 + backend: PyTorch + batch size: 1 + mode: FP32 + resolution: (550, 550) + Results: + - Task: Instance Segmentation + Dataset: COCO + Metrics: + mask AP: 30.4 + Weights: https://openmmlab.oss-cn-hangzhou.aliyuncs.com/mmdetection/v2.0/yolact/yolact_r101_1x8_coco_20200908-4cbe9101.pth diff --git a/configs/yolact/yolact_r101_1x8_coco.py b/configs/yolact/yolact_r101_1x8_coco.py index 2864b59..532631d 100644 --- a/configs/yolact/yolact_r101_1x8_coco.py +++ b/configs/yolact/yolact_r101_1x8_coco.py @@ -1,3 +1,7 @@ _base_ = './yolact_r50_1x8_coco.py' -model = dict(pretrained='torchvision://resnet101', backbone=dict(depth=101)) +model = dict( + backbone=dict( + depth=101, + init_cfg=dict(type='Pretrained', + checkpoint='torchvision://resnet101'))) diff --git a/configs/yolact/yolact_r50_1x8_coco.py b/configs/yolact/yolact_r50_1x8_coco.py index d0e5ace..9d2103c 100644 --- a/configs/yolact/yolact_r50_1x8_coco.py +++ b/configs/yolact/yolact_r50_1x8_coco.py @@ -4,7 +4,6 @@ img_size = 550 model = dict( type='YOLACT', - pretrained='torchvision://resnet50', backbone=dict( type='ResNet', depth=50, @@ -14,7 +13,8 @@ norm_cfg=dict(type='BN', requires_grad=True), norm_eval=False, # update the statistics of bn zero_init_residual=False, - style='pytorch'), + style='pytorch', + init_cfg=dict(type='Pretrained', checkpoint='torchvision://resnet50')), neck=dict( type='FPN', in_channels=[256, 512, 1024, 2048], diff --git a/configs/yolo/README.md b/configs/yolo/README.md index 1f539c6..6c7ba55 100644 --- a/configs/yolo/README.md +++ b/configs/yolo/README.md @@ -2,7 +2,7 @@ ## Introduction -[ALGORITHM] + ```latex @misc{redmon2018yolov3, @@ -19,9 +19,26 @@ | Backbone | Scale | Lr schd | Mem (GB) | Inf time (fps) | box AP | Config | Download | | :-------------: | :-----: | :-----: | :------: | :------------: | :----: | :------: | :--------: | -| DarkNet-53 | 320 | 273e | 2.7 | 63.9 | 27.9 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/yolo/yolov3_d53_320_273e_coco.py) | [model](http://download.openmmlab.com/mmdetection/v2.0/yolo/yolov3_d53_320_273e_coco/yolov3_d53_320_273e_coco-421362b6.pth) | [log](http://download.openmmlab.com/mmdetection/v2.0/yolo/yolov3_d53_320_273e_coco/yolov3_d53_320_273e_coco-20200819_172101.log.json) | -| DarkNet-53 | 416 | 273e | 3.8 | 61.2 | 30.9 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/yolo/yolov3_d53_mstrain-416_273e_coco.py) | [model](http://download.openmmlab.com/mmdetection/v2.0/yolo/yolov3_d53_mstrain-416_273e_coco/yolov3_d53_mstrain-416_273e_coco-2b60fcd9.pth) | [log](http://download.openmmlab.com/mmdetection/v2.0/yolo/yolov3_d53_mstrain-416_273e_coco/yolov3_d53_mstrain-416_273e_coco-20200819_173424.log.json) | -| DarkNet-53 | 608 | 273e | 7.1 | 48.1 | 33.4 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/yolo/yolov3_d53_mstrain-608_273e_coco.py) | [model](http://download.openmmlab.com/mmdetection/v2.0/yolo/yolov3_d53_mstrain-608_273e_coco/yolov3_d53_mstrain-608_273e_coco-139f5633.pth) | [log](http://download.openmmlab.com/mmdetection/v2.0/yolo/yolov3_d53_mstrain-608_273e_coco/yolov3_d53_mstrain-608_273e_coco-20200819_170820.log.json) | +| DarkNet-53 | 320 | 273e | 2.7 | 63.9 | 27.9 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/yolo/yolov3_d53_320_273e_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/yolo/yolov3_d53_320_273e_coco/yolov3_d53_320_273e_coco-421362b6.pth) | [log](https://download.openmmlab.com/mmdetection/v2.0/yolo/yolov3_d53_320_273e_coco/yolov3_d53_320_273e_coco-20200819_172101.log.json) | +| DarkNet-53 | 416 | 273e | 3.8 | 61.2 | 30.9 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/yolo/yolov3_d53_mstrain-416_273e_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/yolo/yolov3_d53_mstrain-416_273e_coco/yolov3_d53_mstrain-416_273e_coco-2b60fcd9.pth) | [log](https://download.openmmlab.com/mmdetection/v2.0/yolo/yolov3_d53_mstrain-416_273e_coco/yolov3_d53_mstrain-416_273e_coco-20200819_173424.log.json) | +| DarkNet-53 | 608 | 273e | 7.4 | 48.1 | 33.7 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/yolo/yolov3_d53_mstrain-608_273e_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/yolo/yolov3_d53_mstrain-608_273e_coco/yolov3_d53_mstrain-608_273e_coco_20210518_115020-a2c3acb8.pth) | [log](https://download.openmmlab.com/mmdetection/v2.0/yolo/yolov3_d53_mstrain-608_273e_coco/yolov3_d53_mstrain-608_273e_coco_20210518_115020.log.json) | + +## Mixed Precision Training + +We also train YOLOv3 with mixed precision training. + +| Backbone | Scale | Lr schd | Mem (GB) | Inf time (fps) | box AP | Config | Download | +| :-------------: | :-----: | :-----: | :------: | :------------: | :----: | :------: | :--------: | +| DarkNet-53 | 608 | 273e | 4.7 | 48.1 | 33.8 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/yolo/yolov3_d53_fp16_mstrain-608_273e_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/yolo/yolov3_d53_fp16_mstrain-608_273e_coco/yolov3_d53_fp16_mstrain-608_273e_coco_20210517_213542-4bc34944.pth) | [log](https://download.openmmlab.com/mmdetection/v2.0/yolo/yolov3_d53_fp16_mstrain-608_273e_coco/yolov3_d53_fp16_mstrain-608_273e_coco_20210517_213542.log.json) | + +## Lightweight models + +| Backbone | Scale | Lr schd | Mem (GB) | Inf time (fps) | box AP | Config | Download | +| :-------------: | :-----: | :-----: | :------: | :------------: | :----: | :------: | :--------: | +| MobileNetV2 | 416 | 300e | 5.3 | | 23.9 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/yolo/yolov3_mobilenetv2_mstrain-416_300e_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/yolo/yolov3_mobilenetv2_mstrain-416_300e_coco/yolov3_mobilenetv2_mstrain-416_300e_coco_20210718_010823-f68a07b3.pth) | [log](https://download.openmmlab.com/mmdetection/v2.0/yolo/yolov3_mobilenetv2_mstrain-416_300e_coco/yolov3_mobilenetv2_mstrain-416_300e_coco_20210718_010823.log.json) | +| MobileNetV2 | 320 | 300e | 3.2 | | 22.2 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/yolo/yolov3_mobilenetv2_320_300e_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/yolo/yolov3_mobilenetv2_320_300e_coco/yolov3_mobilenetv2_320_300e_coco_20210719_215349-d18dff72.pth) | [log](https://download.openmmlab.com/mmdetection/v2.0/yolo/yolov3_mobilenetv2_320_300e_coco/yolov3_mobilenetv2_320_300e_coco_20210719_215349.log.json) | + +Notice: We reduce the number of channels to 96 in both head and neck. It can reduce the flops and parameters, which makes these models more suitable for edge devices. ## Credit diff --git a/configs/yolo/metafile.yml b/configs/yolo/metafile.yml new file mode 100644 index 0000000..22c35da --- /dev/null +++ b/configs/yolo/metafile.yml @@ -0,0 +1,124 @@ +Collections: + - Name: YOLOv3 + Metadata: + Training Data: COCO + Training Techniques: + - SGD with Momentum + - Weight Decay + Training Resources: 8x V100 GPUs + Architecture: + - DarkNet + Paper: + URL: https://arxiv.org/abs/1804.02767 + Title: 'YOLOv3: An Incremental Improvement' + README: configs/yolo/README.md + Code: + URL: https://github.com/open-mmlab/mmdetection/blob/v2.4.0/mmdet/models/detectors/yolo.py#L8 + Version: v2.4.0 + +Models: + - Name: yolov3_d53_320_273e_coco + In Collection: YOLOv3 + Config: configs/yolo/yolov3_d53_320_273e_coco.py + Metadata: + Training Memory (GB): 2.7 + inference time (ms/im): + - value: 15.65 + hardware: V100 + backend: PyTorch + batch size: 1 + mode: FP32 + resolution: (320, 320) + Epochs: 273 + Results: + - Task: Object Detection + Dataset: COCO + Metrics: + box AP: 27.9 + Weights: https://download.openmmlab.com/mmdetection/v2.0/yolo/yolov3_d53_320_273e_coco/yolov3_d53_320_273e_coco-421362b6.pth + + - Name: yolov3_d53_mstrain-416_273e_coco + In Collection: YOLOv3 + Config: configs/yolo/yolov3_d53_mstrain-416_273e_coco.py + Metadata: + Training Memory (GB): 3.8 + inference time (ms/im): + - value: 16.34 + hardware: V100 + backend: PyTorch + batch size: 1 + mode: FP32 + resolution: (416, 416) + Epochs: 273 + Results: + - Task: Object Detection + Dataset: COCO + Metrics: + box AP: 30.9 + Weights: https://download.openmmlab.com/mmdetection/v2.0/yolo/yolov3_d53_mstrain-416_273e_coco/yolov3_d53_mstrain-416_273e_coco-2b60fcd9.pth + + - Name: yolov3_d53_mstrain-608_273e_coco + In Collection: YOLOv3 + Config: configs/yolo/yolov3_d53_mstrain-608_273e_coco.py + Metadata: + Training Memory (GB): 7.4 + inference time (ms/im): + - value: 20.79 + hardware: V100 + backend: PyTorch + batch size: 1 + mode: FP32 + resolution: (608, 608) + Epochs: 273 + Results: + - Task: Object Detection + Dataset: COCO + Metrics: + box AP: 33.7 + Weights: https://download.openmmlab.com/mmdetection/v2.0/yolo/yolov3_d53_mstrain-608_273e_coco/yolov3_d53_mstrain-608_273e_coco_20210518_115020-a2c3acb8.pth + + - Name: yolov3_d53_fp16_mstrain-608_273e_coco + In Collection: YOLOv3 + Config: configs/yolo/yolov3_d53_fp16_mstrain-608_273e_coco.py + Metadata: + Training Memory (GB): 4.7 + inference time (ms/im): + - value: 20.79 + hardware: V100 + backend: PyTorch + batch size: 1 + mode: FP16 + resolution: (608, 608) + Epochs: 273 + Results: + - Task: Object Detection + Dataset: COCO + Metrics: + box AP: 33.8 + Weights: https://download.openmmlab.com/mmdetection/v2.0/yolo/yolov3_d53_fp16_mstrain-608_273e_coco/yolov3_d53_fp16_mstrain-608_273e_coco_20210517_213542-4bc34944.pth + + - Name: yolov3_mobilenetv2_320_300e_coco + In Collection: YOLOv3 + Config: configs/yolo/yolov3_mobilenetv2_320_300e_coco.py + Metadata: + Training Memory (GB): 3.2 + Epochs: 300 + Results: + - Task: Object Detection + Dataset: COCO + Metrics: + box AP: 22.2 + Weights: https://download.openmmlab.com/mmdetection/v2.0/yolo/yolov3_mobilenetv2_320_300e_coco/yolov3_mobilenetv2_320_300e_coco_20210719_215349-d18dff72.pth + + - Name: yolov3_mobilenetv2_mstrain-416_300e_coco + In Collection: YOLOv3 + Config: configs/yolo/yolov3_mobilenetv2_mstrain-416_300e_coco.py + Metadata: + Training Memory (GB): 5.3 + Epochs: 300 + Results: + - Task: Object Detection + Dataset: COCO + Metrics: + box AP: 23.9 + Weights: https://download.openmmlab.com/mmdetection/v2.0/yolo/yolov3_mobilenetv2_mstrain-416_300e_coco/yolov3_mobilenetv2_mstrain-416_300e_coco_20210718_010823-f68a07b3.pth diff --git a/configs/yolo/yolov3_d53_fp16_mstrain-608_273e_coco.py b/configs/yolo/yolov3_d53_fp16_mstrain-608_273e_coco.py new file mode 100644 index 0000000..4ef2422 --- /dev/null +++ b/configs/yolo/yolov3_d53_fp16_mstrain-608_273e_coco.py @@ -0,0 +1,3 @@ +_base_ = './yolov3_d53_mstrain-608_273e_coco.py' +# fp16 settings +fp16 = dict(loss_scale='dynamic') diff --git a/configs/yolo/yolov3_d53_mstrain-608_273e_coco.py b/configs/yolo/yolov3_d53_mstrain-608_273e_coco.py index 9c65305..83026b0 100644 --- a/configs/yolo/yolov3_d53_mstrain-608_273e_coco.py +++ b/configs/yolo/yolov3_d53_mstrain-608_273e_coco.py @@ -2,8 +2,11 @@ # model settings model = dict( type='YOLOV3', - pretrained='open-mmlab://darknet53', - backbone=dict(type='Darknet', depth=53, out_indices=(3, 4, 5)), + backbone=dict( + type='Darknet', + depth=53, + out_indices=(3, 4, 5), + init_cfg=dict(type='Pretrained', checkpoint='open-mmlab://darknet53')), neck=dict( type='YOLOV3Neck', num_scales=3, diff --git a/configs/yolo/yolov3_mobilenetv2_320_300e_coco.py b/configs/yolo/yolov3_mobilenetv2_320_300e_coco.py new file mode 100644 index 0000000..faec1b4 --- /dev/null +++ b/configs/yolo/yolov3_mobilenetv2_320_300e_coco.py @@ -0,0 +1,53 @@ +_base_ = ['./yolov3_mobilenetv2_mstrain-416_300e_coco.py'] + +# yapf:disable +model = dict( + bbox_head=dict( + anchor_generator=dict( + base_sizes=[[(220, 125), (128, 222), (264, 266)], + [(35, 87), (102, 96), (60, 170)], + [(10, 15), (24, 36), (72, 42)]]))) +# yapf:enable + +# dataset settings +img_norm_cfg = dict( + mean=[123.675, 116.28, 103.53], std=[58.395, 57.12, 57.375], to_rgb=True) +train_pipeline = [ + dict(type='LoadImageFromFile', to_float32=True), + dict(type='LoadAnnotations', with_bbox=True), + dict(type='PhotoMetricDistortion'), + dict( + type='Expand', + mean=img_norm_cfg['mean'], + to_rgb=img_norm_cfg['to_rgb'], + ratio_range=(1, 2)), + dict( + type='MinIoURandomCrop', + min_ious=(0.4, 0.5, 0.6, 0.7, 0.8, 0.9), + min_crop_size=0.3), + dict(type='Resize', img_scale=(320, 320), keep_ratio=True), + dict(type='RandomFlip', flip_ratio=0.5), + dict(type='Normalize', **img_norm_cfg), + dict(type='Pad', size_divisor=32), + dict(type='DefaultFormatBundle'), + dict(type='Collect', keys=['img', 'gt_bboxes', 'gt_labels']) +] +test_pipeline = [ + dict(type='LoadImageFromFile'), + dict( + type='MultiScaleFlipAug', + img_scale=(320, 320), + flip=False, + transforms=[ + dict(type='Resize', keep_ratio=True), + dict(type='RandomFlip'), + dict(type='Normalize', **img_norm_cfg), + dict(type='Pad', size_divisor=32), + dict(type='DefaultFormatBundle'), + dict(type='Collect', keys=['img']) + ]) +] +data = dict( + train=dict(dataset=dict(pipeline=train_pipeline)), + val=dict(pipeline=test_pipeline), + test=dict(pipeline=test_pipeline)) diff --git a/configs/yolo/yolov3_mobilenetv2_mstrain-416_300e_coco.py b/configs/yolo/yolov3_mobilenetv2_mstrain-416_300e_coco.py new file mode 100644 index 0000000..645c0ee --- /dev/null +++ b/configs/yolo/yolov3_mobilenetv2_mstrain-416_300e_coco.py @@ -0,0 +1,137 @@ +_base_ = '../_base_/default_runtime.py' +# model settings +model = dict( + type='YOLOV3', + backbone=dict( + type='MobileNetV2', + out_indices=(2, 4, 6), + act_cfg=dict(type='LeakyReLU', negative_slope=0.1), + init_cfg=dict( + type='Pretrained', checkpoint='open-mmlab://mmdet/mobilenet_v2')), + neck=dict( + type='YOLOV3Neck', + num_scales=3, + in_channels=[320, 96, 32], + out_channels=[96, 96, 96]), + bbox_head=dict( + type='YOLOV3Head', + num_classes=80, + in_channels=[96, 96, 96], + out_channels=[96, 96, 96], + anchor_generator=dict( + type='YOLOAnchorGenerator', + base_sizes=[[(116, 90), (156, 198), (373, 326)], + [(30, 61), (62, 45), (59, 119)], + [(10, 13), (16, 30), (33, 23)]], + strides=[32, 16, 8]), + bbox_coder=dict(type='YOLOBBoxCoder'), + featmap_strides=[32, 16, 8], + loss_cls=dict( + type='CrossEntropyLoss', + use_sigmoid=True, + loss_weight=1.0, + reduction='sum'), + loss_conf=dict( + type='CrossEntropyLoss', + use_sigmoid=True, + loss_weight=1.0, + reduction='sum'), + loss_xy=dict( + type='CrossEntropyLoss', + use_sigmoid=True, + loss_weight=2.0, + reduction='sum'), + loss_wh=dict(type='MSELoss', loss_weight=2.0, reduction='sum')), + # training and testing settings + train_cfg=dict( + assigner=dict( + type='GridAssigner', + pos_iou_thr=0.5, + neg_iou_thr=0.5, + min_pos_iou=0)), + test_cfg=dict( + nms_pre=1000, + min_bbox_size=0, + score_thr=0.05, + conf_thr=0.005, + nms=dict(type='nms', iou_threshold=0.45), + max_per_img=100)) +# dataset settings +dataset_type = 'CocoDataset' +data_root = 'data/coco/' +img_norm_cfg = dict( + mean=[123.675, 116.28, 103.53], std=[58.395, 57.12, 57.375], to_rgb=True) +train_pipeline = [ + dict(type='LoadImageFromFile', to_float32=True), + dict(type='LoadAnnotations', with_bbox=True), + dict(type='PhotoMetricDistortion'), + dict( + type='Expand', + mean=img_norm_cfg['mean'], + to_rgb=img_norm_cfg['to_rgb'], + ratio_range=(1, 2)), + dict( + type='MinIoURandomCrop', + min_ious=(0.4, 0.5, 0.6, 0.7, 0.8, 0.9), + min_crop_size=0.3), + dict( + type='Resize', + img_scale=[(320, 320), (416, 416)], + multiscale_mode='range', + keep_ratio=True), + dict(type='RandomFlip', flip_ratio=0.5), + dict(type='Normalize', **img_norm_cfg), + dict(type='Pad', size_divisor=32), + dict(type='DefaultFormatBundle'), + dict(type='Collect', keys=['img', 'gt_bboxes', 'gt_labels']) +] +test_pipeline = [ + dict(type='LoadImageFromFile'), + dict( + type='MultiScaleFlipAug', + img_scale=(416, 416), + flip=False, + transforms=[ + dict(type='Resize', keep_ratio=True), + dict(type='RandomFlip'), + dict(type='Normalize', **img_norm_cfg), + dict(type='Pad', size_divisor=32), + dict(type='DefaultFormatBundle'), + dict(type='Collect', keys=['img']) + ]) +] +data = dict( + samples_per_gpu=24, + workers_per_gpu=4, + train=dict( + type='RepeatDataset', # use RepeatDataset to speed up training + times=10, + dataset=dict( + type=dataset_type, + ann_file=data_root + 'annotations/instances_train2017.json', + img_prefix=data_root + 'train2017/', + pipeline=train_pipeline)), + val=dict( + type=dataset_type, + ann_file=data_root + 'annotations/instances_val2017.json', + img_prefix=data_root + 'val2017/', + pipeline=test_pipeline), + test=dict( + type=dataset_type, + ann_file=data_root + 'annotations/instances_val2017.json', + img_prefix=data_root + 'val2017/', + pipeline=test_pipeline)) +# optimizer +optimizer = dict(type='SGD', lr=0.003, momentum=0.9, weight_decay=0.0005) +optimizer_config = dict(grad_clip=dict(max_norm=35, norm_type=2)) +# learning policy +lr_config = dict( + policy='step', + warmup='linear', + warmup_iters=4000, + warmup_ratio=0.0001, + step=[24, 28]) +# runtime settings +runner = dict(type='EpochBasedRunner', max_epochs=30) +evaluation = dict(interval=1, metric=['bbox']) +find_unused_parameters = True diff --git a/configs/yolof/README.md b/configs/yolof/README.md new file mode 100644 index 0000000..e16e8a2 --- /dev/null +++ b/configs/yolof/README.md @@ -0,0 +1,25 @@ +# You Only Look One-level Feature + +## Introduction + + + +``` +@inproceedings{chen2021you, + title={You Only Look One-level Feature}, + author={Chen, Qiang and Wang, Yingming and Yang, Tong and Zhang, Xiangyu and Cheng, Jian and Sun, Jian}, + booktitle={IEEE Conference on Computer Vision and Pattern Recognition}, + year={2021} +} +``` + +## Results and Models + +| Backbone | Style | Epoch | Lr schd | Mem (GB) | box AP | Config | Download | +|:---------:|:-------:|:-------:|:-------:|:--------:|:------:|:------:|:--------:| +| R-50-C5 | caffe | Y | 1x | 8.3 | 37.5 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/yolof/yolof_r50_c5_8x8_1x_coco.py) |[model](https://download.openmmlab.com/mmdetection/v2.0/yolof/yolof_r50_c5_8x8_1x_coco/yolof_r50_c5_8x8_1x_coco_20210425_024427-8e864411.pth) | [log](https://download.openmmlab.com/mmdetection/v2.0/yolof/yolof_r50_c5_8x8_1x_coco/yolof_r50_c5_8x8_1x_coco_20210425_024427.log.json) | + +**Note**: + +1. We find that the performance is unstable and may fluctuate by about 0.3 mAP. mAP 37.4 ~ 37.7 is acceptable in YOLOF_R_50_C5_1x. Such fluctuation can also be found in the [original implementation](https://github.com/chensnathan/YOLOF). +2. In addition to instability issues, sometimes there are large loss fluctuations and NAN, so there may still be problems with this project, which will be improved subsequently. diff --git a/configs/yolof/metafile.yml b/configs/yolof/metafile.yml new file mode 100644 index 0000000..9436fee --- /dev/null +++ b/configs/yolof/metafile.yml @@ -0,0 +1,32 @@ +Collections: + - Name: YOLOF + Metadata: + Training Data: COCO + Training Techniques: + - SGD with Momentum + - Weight Decay + Training Resources: 8x V100 GPUs + Architecture: + - Dilated Encoder + - ResNet + Paper: + URL: https://arxiv.org/abs/2103.09460 + Title: 'You Only Look One-level Feature' + README: configs/yolof/README.md + Code: + URL: https://github.com/open-mmlab/mmdetection/blob/v2.12.0/mmdet/models/detectors/yolof.py#L6 + Version: v2.12.0 + +Models: + - Name: yolof_r50_c5_8x8_1x_coco + In Collection: YOLOF + Config: configs/yolof/yolof_r50_c5_8x8_1x_coco.py + Metadata: + Training Memory (GB): 8.3 + Epochs: 12 + Results: + - Task: Object Detection + Dataset: COCO + Metrics: + box AP: 37.5 + Weights: https://download.openmmlab.com/mmdetection/v2.0/yolof/yolof_r50_c5_8x8_1x_coco/yolof_r50_c5_8x8_1x_coco_20210425_024427-8e864411.pth diff --git a/configs/yolof/yolof_r50_c5_8x8_1x_coco.py b/configs/yolof/yolof_r50_c5_8x8_1x_coco.py new file mode 100644 index 0000000..0e3b501 --- /dev/null +++ b/configs/yolof/yolof_r50_c5_8x8_1x_coco.py @@ -0,0 +1,105 @@ +_base_ = [ + '../_base_/datasets/coco_detection.py', + '../_base_/schedules/schedule_1x.py', '../_base_/default_runtime.py' +] +model = dict( + type='YOLOF', + backbone=dict( + type='ResNet', + depth=50, + num_stages=4, + out_indices=(3, ), + frozen_stages=1, + norm_cfg=dict(type='BN', requires_grad=False), + norm_eval=True, + style='caffe', + init_cfg=dict( + type='Pretrained', + checkpoint='open-mmlab://detectron/resnet50_caffe')), + neck=dict( + type='DilatedEncoder', + in_channels=2048, + out_channels=512, + block_mid_channels=128, + num_residual_blocks=4), + bbox_head=dict( + type='YOLOFHead', + num_classes=80, + in_channels=512, + reg_decoded_bbox=True, + anchor_generator=dict( + type='AnchorGenerator', + ratios=[1.0], + scales=[1, 2, 4, 8, 16], + strides=[32]), + bbox_coder=dict( + type='DeltaXYWHBBoxCoder', + target_means=[.0, .0, .0, .0], + target_stds=[1., 1., 1., 1.], + add_ctr_clamp=True, + ctr_clamp=32), + loss_cls=dict( + type='FocalLoss', + use_sigmoid=True, + gamma=2.0, + alpha=0.25, + loss_weight=1.0), + loss_bbox=dict(type='GIoULoss', loss_weight=1.0)), + # training and testing settings + train_cfg=dict( + assigner=dict( + type='UniformAssigner', pos_ignore_thr=0.15, neg_ignore_thr=0.7), + allowed_border=-1, + pos_weight=-1, + debug=False), + test_cfg=dict( + nms_pre=1000, + min_bbox_size=0, + score_thr=0.05, + nms=dict(type='nms', iou_threshold=0.6), + max_per_img=100)) +# optimizer +optimizer = dict( + type='SGD', + lr=0.12, + momentum=0.9, + weight_decay=0.0001, + paramwise_cfg=dict( + norm_decay_mult=0., custom_keys={'backbone': dict(lr_mult=1. / 3)})) +lr_config = dict(warmup_iters=1500, warmup_ratio=0.00066667) + +# use caffe img_norm +img_norm_cfg = dict( + mean=[103.530, 116.280, 123.675], std=[1.0, 1.0, 1.0], to_rgb=False) +train_pipeline = [ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations', with_bbox=True), + dict(type='Resize', img_scale=(1333, 800), keep_ratio=True), + dict(type='RandomFlip', flip_ratio=0.5), + dict(type='RandomShift', shift_ratio=0.5, max_shift_px=32), + dict(type='Normalize', **img_norm_cfg), + dict(type='Pad', size_divisor=32), + dict(type='DefaultFormatBundle'), + dict(type='Collect', keys=['img', 'gt_bboxes', 'gt_labels']) +] +test_pipeline = [ + dict(type='LoadImageFromFile'), + dict( + type='MultiScaleFlipAug', + img_scale=(1333, 800), + flip=False, + transforms=[ + dict(type='Resize', keep_ratio=True), + dict(type='RandomFlip'), + dict(type='Normalize', **img_norm_cfg), + dict(type='Pad', size_divisor=32), + dict(type='ImageToTensor', keys=['img']), + dict(type='Collect', keys=['img']), + ]) +] +data = dict( + samples_per_gpu=8, + workers_per_gpu=8, + train=dict(pipeline=train_pipeline), + val=dict(pipeline=test_pipeline), + test=dict(pipeline=test_pipeline)) diff --git a/configs/yolof/yolof_r50_c5_8x8_iter-1x_coco.py b/configs/yolof/yolof_r50_c5_8x8_iter-1x_coco.py new file mode 100644 index 0000000..c95c02d --- /dev/null +++ b/configs/yolof/yolof_r50_c5_8x8_iter-1x_coco.py @@ -0,0 +1,14 @@ +_base_ = './yolof_r50_c5_8x8_1x_coco.py' + +# We implemented the iter-based config according to the source code. +# COCO dataset has 117266 images after filtering. We use 8 gpu and +# 8 batch size training, so 22500 is equivalent to +# 22500/(117266/(8x8))=12.3 epoch, 15000 is equivalent to 8.2 epoch, +# 20000 is equivalent to 10.9 epoch. Due to lr(0.12) is large, +# the iter-based and epoch-based setting have about 0.2 difference on +# the mAP evaluation value. +lr_config = dict(step=[15000, 20000]) +runner = dict(_delete_=True, type='IterBasedRunner', max_iters=22500) +checkpoint_config = dict(interval=2500) +evaluation = dict(interval=4500) +log_config = dict(interval=20) diff --git a/configs/yolox/README.md b/configs/yolox/README.md new file mode 100644 index 0000000..dd00796 --- /dev/null +++ b/configs/yolox/README.md @@ -0,0 +1,25 @@ +# YOLOX: Exceeding YOLO Series in 2021 + +## Introduction + + + +```latex +@article{yolox2021, + title={{YOLOX}: Exceeding YOLO Series in 2021}, + author={Ge, Zheng and Liu, Songtao and Wang, Feng and Li, Zeming and Sun, Jian}, + journal={arXiv preprint arXiv:2107.08430}, + year={2021} +} +``` + +## Results and Models + +| Backbone | size | Mem (GB) | box AP | Config | Download | +|:---------:|:-------:|:-------:|:-------:|:--------:|:------:| +| YOLOX-Tiny | 416 | 3.6 | 31.6 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/yolox/yolox_tiny_8x8_300e_coco.py) |[model](https://download.openmmlab.com/mmdetection/v2.0/yolox/yolox_tiny_8x8_300e_coco/yolox_tiny_8x8_300e_coco_20210806_234250-4ff3b67e.pth) | [log](https://download.openmmlab.com/mmdetection/v2.0/yolox/yolox_tiny_8x8_300e_coco/yolox_tiny_8x8_300e_coco_20210806_234250.log.json) | + +**Note**: + +1. The test score threshold is 0.001. +2. We find that the performance is unstable and may fluctuate by about 0.7 mAP. We will continue to investigate and improve it. diff --git a/configs/yolox/metafile.yml b/configs/yolox/metafile.yml new file mode 100644 index 0000000..22ad6cc --- /dev/null +++ b/configs/yolox/metafile.yml @@ -0,0 +1,33 @@ +Collections: + - Name: YOLOX + Metadata: + Training Data: COCO + Training Techniques: + - SGD with Nesterov + - Weight Decay + - Cosine Annealing Lr Updater + Training Resources: 8x TITANXp GPUs + Architecture: + - CSPDarkNet + - PAFPN + Paper: + URL: https://arxiv.org/abs/2107.08430 + Title: 'YOLOX: Exceeding YOLO Series in 2021' + README: configs/yolox/README.md + Code: + URL: https://github.com/open-mmlab/mmdetection/blob/v2.15.1/mmdet/models/detectors/yolox.py#L6 + Version: v2.15.1 + +Models: + - Name: yolox_tiny_8x8_300e_coco + In Collection: YOLOX + Config: configs/yolox/yolox_tiny_8x8_300e_coco.py + Metadata: + Training Memory (GB): 3.6 + Epochs: 300 + Results: + - Task: Object Detection + Dataset: COCO + Metrics: + box AP: 31.6 + Weights: https://download.openmmlab.com/mmdetection/v2.0/yolox/yolox_tiny_8x8_300e_coco/yolox_tiny_8x8_300e_coco_20210806_234250-4ff3b67e.pth diff --git a/configs/yolox/yolox_l_8x8_300e_coco.py b/configs/yolox/yolox_l_8x8_300e_coco.py new file mode 100644 index 0000000..dcbfa18 --- /dev/null +++ b/configs/yolox/yolox_l_8x8_300e_coco.py @@ -0,0 +1,8 @@ +_base_ = './yolox_s_8x8_300e_coco.py' + +# model settings +model = dict( + backbone=dict(deepen_factor=1.0, widen_factor=1.0), + neck=dict( + in_channels=[256, 512, 1024], out_channels=256, num_csp_blocks=3), + bbox_head=dict(in_channels=256, feat_channels=256)) diff --git a/configs/yolox/yolox_m_8x8_300e_coco.py b/configs/yolox/yolox_m_8x8_300e_coco.py new file mode 100644 index 0000000..3048c95 --- /dev/null +++ b/configs/yolox/yolox_m_8x8_300e_coco.py @@ -0,0 +1,8 @@ +_base_ = './yolox_s_8x8_300e_coco.py' + +# model settings +model = dict( + backbone=dict(deepen_factor=0.67, widen_factor=0.75), + neck=dict(in_channels=[192, 384, 768], out_channels=192, num_csp_blocks=2), + bbox_head=dict(in_channels=192, feat_channels=192), +) diff --git a/configs/yolox/yolox_nano_8x8_300e_coco.py b/configs/yolox/yolox_nano_8x8_300e_coco.py new file mode 100644 index 0000000..d33ed04 --- /dev/null +++ b/configs/yolox/yolox_nano_8x8_300e_coco.py @@ -0,0 +1,11 @@ +_base_ = './yolox_tiny_8x8_300e_coco.py' + +# model settings +model = dict( + backbone=dict(deepen_factor=0.33, widen_factor=0.25, use_depthwise=True), + neck=dict( + in_channels=[64, 128, 256], + out_channels=64, + num_csp_blocks=1, + use_depthwise=True), + bbox_head=dict(in_channels=64, feat_channels=64, use_depthwise=True)) diff --git a/configs/yolox/yolox_s_8x8_300e_coco.py b/configs/yolox/yolox_s_8x8_300e_coco.py new file mode 100644 index 0000000..7137495 --- /dev/null +++ b/configs/yolox/yolox_s_8x8_300e_coco.py @@ -0,0 +1,143 @@ +_base_ = ['../_base_/schedules/schedule_1x.py', '../_base_/default_runtime.py'] + +# model settings +model = dict( + type='YOLOX', + backbone=dict(type='CSPDarknet', deepen_factor=0.33, widen_factor=0.5), + neck=dict( + type='YOLOXPAFPN', + in_channels=[128, 256, 512], + out_channels=128, + num_csp_blocks=1), + bbox_head=dict( + type='YOLOXHead', num_classes=80, in_channels=128, feat_channels=128), + train_cfg=dict(assigner=dict(type='SimOTAAssigner', center_radius=2.5)), + # In order to align the source code, the threshold of the val phase is + # 0.01, and the threshold of the test phase is 0.001. + test_cfg=dict(score_thr=0.01, nms=dict(type='nms', iou_threshold=0.65))) + +# dataset settings +data_root = 'data/coco/' +dataset_type = 'CocoDataset' + +img_norm_cfg = dict( + mean=[123.675, 116.28, 103.53], std=[58.395, 57.12, 57.375], to_rgb=True) + +img_scale = (640, 640) + +train_pipeline = [ + dict(type='Mosaic', img_scale=img_scale, pad_val=114.0), + dict( + type='RandomAffine', + scaling_ratio_range=(0.1, 2), + border=(-img_scale[0] // 2, -img_scale[1] // 2)), + dict( + type='MixUp', + img_scale=img_scale, + ratio_range=(0.8, 1.6), + pad_val=114.0), + dict( + type='PhotoMetricDistortion', + brightness_delta=32, + contrast_range=(0.5, 1.5), + saturation_range=(0.5, 1.5), + hue_delta=18), + dict(type='RandomFlip', flip_ratio=0.5), + dict(type='Resize', keep_ratio=True), + dict(type='Pad', pad_to_square=True, pad_val=114.0), + dict(type='Normalize', **img_norm_cfg), + dict(type='DefaultFormatBundle'), + dict(type='Collect', keys=['img', 'gt_bboxes', 'gt_labels']) +] + +train_dataset = dict( + type='MultiImageMixDataset', + dataset=dict( + type=dataset_type, + ann_file=data_root + 'annotations/instances_train2017.json', + img_prefix=data_root + 'train2017/', + pipeline=[ + dict(type='LoadImageFromFile', to_float32=True), + dict(type='LoadAnnotations', with_bbox=True) + ], + filter_empty_gt=False, + ), + pipeline=train_pipeline, + dynamic_scale=img_scale) + +test_pipeline = [ + dict(type='LoadImageFromFile'), + dict( + type='MultiScaleFlipAug', + img_scale=img_scale, + flip=False, + transforms=[ + dict(type='Resize', keep_ratio=True), + dict(type='RandomFlip'), + dict(type='Pad', size=img_scale, pad_val=114.0), + dict(type='Normalize', **img_norm_cfg), + dict(type='DefaultFormatBundle'), + dict(type='Collect', keys=['img']) + ]) +] + +data = dict( + samples_per_gpu=8, + workers_per_gpu=2, + train=train_dataset, + val=dict( + type=dataset_type, + ann_file=data_root + 'annotations/instances_val2017.json', + img_prefix=data_root + 'val2017/', + pipeline=test_pipeline), + test=dict( + type=dataset_type, + ann_file=data_root + 'annotations/instances_val2017.json', + img_prefix=data_root + 'val2017/', + pipeline=test_pipeline)) + +# optimizer +# default 8 gpu +optimizer = dict( + type='SGD', + lr=0.01, + momentum=0.9, + weight_decay=5e-4, + nesterov=True, + paramwise_cfg=dict(norm_decay_mult=0., bias_decay_mult=0.)) +optimizer_config = dict(grad_clip=None) + +# learning policy +lr_config = dict( + _delete_=True, + policy='YOLOX', + warmup='exp', + by_epoch=False, + warmup_by_epoch=True, + warmup_ratio=1, + warmup_iters=5, # 5 epoch + num_last_epochs=15, + min_lr_ratio=0.05) +runner = dict(type='EpochBasedRunner', max_epochs=300) + +resume_from = None +interval = 10 + +custom_hooks = [ + dict(type='YOLOXModeSwitchHook', num_last_epochs=15, priority=48), + dict( + type='SyncRandomSizeHook', + ratio_range=(14, 26), + img_scale=img_scale, + interval=interval, + priority=48), + dict( + type='SyncNormHook', + num_last_epochs=15, + interval=interval, + priority=48), + dict(type='ExpMomentumEMAHook', resume_from=resume_from, priority=49) +] +checkpoint_config = dict(interval=interval) +evaluation = dict(interval=interval, metric='bbox') +log_config = dict(interval=50) diff --git a/configs/yolox/yolox_tiny_8x8_300e_coco.py b/configs/yolox/yolox_tiny_8x8_300e_coco.py new file mode 100644 index 0000000..4d517cb --- /dev/null +++ b/configs/yolox/yolox_tiny_8x8_300e_coco.py @@ -0,0 +1,79 @@ +_base_ = './yolox_s_8x8_300e_coco.py' + +# model settings +model = dict( + backbone=dict(deepen_factor=0.33, widen_factor=0.375), + neck=dict(in_channels=[96, 192, 384], out_channels=96), + bbox_head=dict(in_channels=96, feat_channels=96)) + +# dataset settings +img_norm_cfg = dict( + mean=[123.675, 116.28, 103.53], std=[58.395, 57.12, 57.375], to_rgb=True) + +img_scale = (640, 640) + +train_pipeline = [ + dict(type='Mosaic', img_scale=img_scale, pad_val=114.0), + dict( + type='RandomAffine', + scaling_ratio_range=(0.5, 1.5), + border=(-img_scale[0] // 2, -img_scale[1] // 2)), + dict( + type='PhotoMetricDistortion', + brightness_delta=32, + contrast_range=(0.5, 1.5), + saturation_range=(0.5, 1.5), + hue_delta=18), + dict(type='RandomFlip', flip_ratio=0.5), + dict(type='Resize', keep_ratio=True), + dict(type='Pad', pad_to_square=True, pad_val=114.0), + dict(type='Normalize', **img_norm_cfg), + dict(type='DefaultFormatBundle'), + dict(type='Collect', keys=['img', 'gt_bboxes', 'gt_labels']) +] + +test_pipeline = [ + dict(type='LoadImageFromFile'), + dict( + type='MultiScaleFlipAug', + img_scale=(416, 416), + flip=False, + transforms=[ + dict(type='Resize', keep_ratio=True), + dict(type='RandomFlip'), + dict(type='Pad', size=(416, 416), pad_val=114.0), + dict(type='Normalize', **img_norm_cfg), + dict(type='DefaultFormatBundle'), + dict(type='Collect', keys=['img']) + ]) +] + +train_dataset = dict(pipeline=train_pipeline) + +data = dict( + train=train_dataset, + val=dict(pipeline=test_pipeline), + test=dict(pipeline=test_pipeline)) + +resume_from = None +interval = 10 + +# Execute in the order of insertion when the priority is the same. +# The smaller the value, the higher the priority +custom_hooks = [ + dict(type='YOLOXModeSwitchHook', num_last_epochs=15, priority=48), + dict( + type='SyncRandomSizeHook', + ratio_range=(10, 20), + img_scale=img_scale, + interval=interval, + priority=48), + dict( + type='SyncNormHook', + num_last_epochs=15, + interval=interval, + priority=48), + dict(type='ExpMomentumEMAHook', resume_from=resume_from, priority=49) +] +checkpoint_config = dict(interval=interval) +evaluation = dict(interval=interval, metric='bbox') diff --git a/configs/yolox/yolox_x_8x8_300e_coco.py b/configs/yolox/yolox_x_8x8_300e_coco.py new file mode 100644 index 0000000..65c0b75 --- /dev/null +++ b/configs/yolox/yolox_x_8x8_300e_coco.py @@ -0,0 +1,8 @@ +_base_ = './yolox_s_8x8_300e_coco.py' + +# model settings +model = dict( + backbone=dict(deepen_factor=1.33, widen_factor=1.25), + neck=dict( + in_channels=[320, 640, 1280], out_channels=320, num_csp_blocks=4), + bbox_head=dict(in_channels=320, feat_channels=320))