yolov5u_l_mask-refine_syncbn_fast_8xb16-300e_coco.py

_base_ = './yolov5u_m_mask-refine_syncbn_fast_8xb16-300e_coco.py'

# This config will refine bbox by mask while loading annotations and
# transforming after `YOLOv5RandomAffine`

# ========================modified parameters======================
deepen_factor = 1.00
widen_factor = 1.00

mixup_prob = 0.15
copypaste_prob = 0.3

# =======================Unmodified in most cases==================
img_scale = _base_.img_scale
pre_transform = _base_.pre_transform
last_transform = _base_.last_transform
affine_scale = _base_.affine_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)))

mosaic_affine_transform = [
    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,
        max_aspect_ratio=100.,
        scaling_ratio_range=(1 - affine_scale, 1 + affine_scale),
        # img_scale is (width, height)
        border=(-img_scale[0] // 2, -img_scale[1] // 2),
        border_val=(114, 114, 114),
        min_area_ratio=_base_.min_area_ratio,
        use_mask_refine=_base_.use_mask2refine)
]

train_pipeline = [
    *pre_transform, *mosaic_affine_transform,
    dict(
        type='YOLOv5MixUp',
        prob=mixup_prob,
        pre_transform=[*pre_transform, *mosaic_affine_transform]),
    *last_transform
]

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