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_base_ = './yolov6_s_syncbn_fast_8xb32-300e_coco.py' | |
# ======================= Possible modified parameters ======================= | |
# -----model related----- | |
# The scaling factor that controls the depth of the network structure | |
deepen_factor = 0.6 | |
# The scaling factor that controls the width of the network structure | |
widen_factor = 0.75 | |
# -----train val related----- | |
affine_scale = 0.9 # YOLOv5RandomAffine scaling ratio | |
# ============================== Unmodified in most cases =================== | |
model = dict( | |
backbone=dict( | |
type='YOLOv6CSPBep', | |
deepen_factor=deepen_factor, | |
widen_factor=widen_factor, | |
hidden_ratio=2. / 3, | |
block_cfg=dict(type='RepVGGBlock'), | |
act_cfg=dict(type='ReLU', inplace=True)), | |
neck=dict( | |
type='YOLOv6CSPRepPAFPN', | |
deepen_factor=deepen_factor, | |
widen_factor=widen_factor, | |
block_cfg=dict(type='RepVGGBlock'), | |
hidden_ratio=2. / 3, | |
block_act_cfg=dict(type='ReLU', inplace=True)), | |
bbox_head=dict( | |
type='YOLOv6Head', head_module=dict(widen_factor=widen_factor))) | |
mosaic_affine_pipeline = [ | |
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 - affine_scale, 1 + affine_scale), | |
# img_scale is (width, height) | |
border=(-_base_.img_scale[0] // 2, -_base_.img_scale[1] // 2), | |
border_val=(114, 114, 114)) | |
] | |
train_pipeline = [ | |
*_base_.pre_transform, *mosaic_affine_pipeline, | |
dict( | |
type='YOLOv5MixUp', | |
prob=0.1, | |
pre_transform=[*_base_.pre_transform, *mosaic_affine_pipeline]), | |
dict(type='YOLOv5HSVRandomAug'), | |
dict(type='mmdet.RandomFlip', prob=0.5), | |
dict( | |
type='mmdet.PackDetInputs', | |
meta_keys=('img_id', 'img_path', 'ori_shape', 'img_shape', 'flip', | |
'flip_direction')) | |
] | |
train_dataloader = dict(dataset=dict(pipeline=train_pipeline)) | |