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_base_ = './yolov5_ins_s-v61_syncbn_fast_8xb16-300e_coco_instance.py' # noqa
# ========================modified parameters======================
mask_overlap = False # Polygon2Mask
# ===============================Unmodified in most cases====================
model = dict(bbox_head=dict(mask_overlap=mask_overlap))
train_pipeline = [
*_base_.pre_transform,
dict(
type='Mosaic',
img_scale=_base_.img_scale,
pad_val=114.0,
pre_transform=_base_.pre_transform),
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=(-_base_.img_scale[0] // 2, -_base_.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=True),
dict(
type='mmdet.Albu',
transforms=_base_.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=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))
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