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- # YOLOv5s模型配置
- # 参考自: https://github.com/ultralytics/yolov5/blob/master/models/yolov5s.yaml
- # 参数
- nc: 80 # 类别数量 (例如COCO数据集有80类)
- depth_multiple: 0.33 # 模型深度因子
- width_multiple: 0.50 # 模型宽度因子
- # 网络结构定义
- backbone:
- # [from, number, module, args]
- [[-1, 1, Focus, [64, 3]], # 0-P1/2
- [-1, 1, Conv, [128, 3, 2]], # 1-P2/4
- [-1, 3, C3, [128]],
- [-1, 1, Conv, [256, 3, 2]], # 3-P3/8
- [-1, 9, C3, [256]],
- [-1, 1, Conv, [512, 3, 2]], # 5-P4/16
- [-1, 9, C3, [512]],
- [-1, 1, Conv, [1024, 3, 2]], # 7-P5/32
- [-1, 1, SPP, [1024, [5, 9, 13]]],
- [-1, 3, C3, [1024, False]], # 9
- ]
- head:
- [[-1, 1, Conv, [512, 1, 1]],
- [-1, 1, nn.Upsample, [None, 2, 'nearest']],
- [[-1, 6], 1, Concat, [1]], # cat backbone P4
- [-1, 3, C3, [512, False]], # 13
- [-1, 1, Conv, [256, 1, 1]],
- [-1, 1, nn.Upsample, [None, 2, 'nearest']],
- [[-1, 4], 1, Concat, [1]], # cat backbone P3
- [-1, 3, C3, [256, False]], # 17 (P3/8-small)
- [-1, 1, Conv, [256, 3, 2]],
- [[-1, 14], 1, Concat, [1]], # cat head P4
- [-1, 3, C3, [512, False]], # 20 (P4/16-medium)
- [-1, 1, Conv, [512, 3, 2]],
- [[-1, 10], 1, Concat, [1]], # cat head P5
- [-1, 3, C3, [1024, False]], # 23 (P5/32-large)
- [[17, 20, 23], 1, Detect, [nc, [[10, 13, 16, 30, 33, 23], [30, 61, 62, 45, 59, 119], [116, 90, 156, 198, 373, 326]]]], # Detect(P3, P4, P5)
- ]
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