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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)) | |