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| # YOLOv6n model | |
| model = dict( | |
| type='YOLOv6n', | |
| pretrained='./weights/yolov6n.pt', | |
| depth_multiple=0.33, | |
| width_multiple=0.25, | |
| backbone=dict( | |
| type='EfficientRep', | |
| num_repeats=[1, 6, 12, 18, 6], | |
| out_channels=[64, 128, 256, 512, 1024], | |
| ), | |
| neck=dict( | |
| type='RepPAN', | |
| num_repeats=[12, 12, 12, 12], | |
| out_channels=[256, 128, 128, 256, 256, 512], | |
| ), | |
| head=dict( | |
| type='EffiDeHead', | |
| in_channels=[128, 256, 512], | |
| num_layers=3, | |
| begin_indices=24, | |
| anchors=1, | |
| out_indices=[17, 20, 23], | |
| strides=[8, 16, 32], | |
| iou_type='ciou' | |
| ) | |
| ) | |
| solver = dict( | |
| optim='SGD', | |
| lr_scheduler='Cosine', | |
| lr0=0.0032, | |
| lrf=0.12, | |
| momentum=0.843, | |
| weight_decay=0.00036, | |
| warmup_epochs=2.0, | |
| warmup_momentum=0.5, | |
| warmup_bias_lr=0.05 | |
| ) | |
| data_aug = dict( | |
| hsv_h=0.0138, | |
| hsv_s=0.664, | |
| hsv_v=0.464, | |
| degrees=0.373, | |
| translate=0.245, | |
| scale=0.898, | |
| shear=0.602, | |
| flipud=0.00856, | |
| fliplr=0.5, | |
| mosaic=1.0, | |
| mixup=0.243 | |
| ) | |