yolov5_ins_l-v61_syncbn_fast_8xb16-300e_coco_instance.py

_base_ = './yolov5_ins_m-v61_syncbn_fast_8xb16-300e_coco_instance.py'  # noqa

# This config use refining bbox and `YOLOv5CopyPaste`.
# Refining bbox means refining bbox by mask while loading annotations and
# transforming after `YOLOv5RandomAffine`
# ========================modified parameters======================
deepen_factor = 1.0
widen_factor = 1.0

mixup_prob = 0.1
copypaste_prob = 0.1

# =======================Unmodified in most cases==================
img_scale = _base_.img_scale

model = dict(
    backbone=dict(
        deepen_factor=deepen_factor,
        widen_factor=widen_factor,
    ),
    neck=dict(
        deepen_factor=deepen_factor,
        widen_factor=widen_factor,
    ),
    bbox_head=dict(head_module=dict(widen_factor=widen_factor)))

pre_transform = _base_.pre_transform
albu_train_transforms = _base_.albu_train_transforms
mosaic_affine_pipeline = [
    dict(
        type='Mosaic',
        img_scale=img_scale,
        pad_val=114.0,
        pre_transform=pre_transform),
    dict(type='YOLOv5CopyPaste', prob=copypaste_prob),
    dict(
        type='YOLOv5RandomAffine',
        max_rotate_degree=0.0,
        max_shear_degree=0.0,
        scaling_ratio_range=(1 - _base_.affine_scale, 1 + _base_.affine_scale),
        border=(-img_scale[0] // 2, -img_scale[1] // 2),
        border_val=(114, 114, 114),
        min_area_ratio=_base_.min_area_ratio,
        max_aspect_ratio=_base_.max_aspect_ratio,
        use_mask_refine=_base_.use_mask2refine),
]

# enable mixup
train_pipeline = [
    *pre_transform,
    *mosaic_affine_pipeline,
    dict(
        type='YOLOv5MixUp',
        prob=mixup_prob,
        pre_transform=[*pre_transform, *mosaic_affine_pipeline]),
    # TODO: support mask transform in albu
    # Geometric transformations are not supported in albu now.
    dict(
        type='mmdet.Albu',
        transforms=albu_train_transforms,
        bbox_params=dict(
            type='BboxParams',
            format='pascal_voc',
            label_fields=['gt_bboxes_labels', 'gt_ignore_flags']),
        keymap={
            'img': 'image',
            'gt_bboxes': 'bboxes'
        }),
    dict(type='YOLOv5HSVRandomAug'),
    dict(type='mmdet.RandomFlip', prob=0.5),
    dict(
        type='Polygon2Mask',
        downsample_ratio=_base_.downsample_ratio,
        mask_overlap=_base_.mask_overlap),
    dict(
        type='PackDetInputs',
        meta_keys=('img_id', 'img_path', 'ori_shape', 'img_shape', 'flip',
                   'flip_direction'))
]

train_dataloader = dict(dataset=dict(pipeline=train_pipeline))