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- # YOLOv6l model
- model = dict(
- type='YOLOv6l',
- pretrained='weights/yolov6l.pt',
- depth_multiple=1.0,
- width_multiple=1.0,
- backbone=dict(
- type='CSPBepBackbone',
- num_repeats=[1, 6, 12, 18, 6],
- out_channels=[64, 128, 256, 512, 1024],
- csp_e=float(1)/2,
- fuse_P2=True,
- ),
- neck=dict(
- type='CSPRepBiFPANNeck',
- num_repeats=[12, 12, 12, 12],
- out_channels=[256, 128, 128, 256, 256, 512],
- csp_e=float(1)/2,
- ),
- head=dict(
- type='EffiDeHead',
- in_channels=[128, 256, 512],
- num_layers=3,
- begin_indices=24,
- anchors=3,
- anchors_init=[[10,13, 19,19, 33,23],
- [30,61, 59,59, 59,119],
- [116,90, 185,185, 373,326]],
- out_indices=[17, 20, 23],
- strides=[8, 16, 32],
- atss_warmup_epoch=0,
- iou_type='giou',
- use_dfl=True,
- reg_max=16, #if use_dfl is False, please set reg_max to 0
- distill_weight={
- 'class': 2.0,
- 'dfl': 1.0,
- },
- )
- )
-
- 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,
- )
- training_mode = "conv_silu"
- # use normal conv to speed up training and further improve accuracy.
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