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metafile.yml
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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_16xb1-20e_coco
In Collection: HTC
Config: configs/htc/htc_x101-32x4d_fpn_16xb1-20e_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_16xb1-20e_coco
In Collection: HTC
Config: configs/htc/htc_x101-64x4d_fpn_16xb1-20e_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-dconv-c3-c5_fpn_ms-400-1400-16xb1-20e_coco
In Collection: HTC
Config: configs/htc/htc_x101-64x4d-dconv-c3-c5_fpn_ms-400-1400-16xb1-20e_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