yolov5s.yaml 1.4 KB

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  1. # YOLOv5s模型配置
  2. # 参考自: https://github.com/ultralytics/yolov5/blob/master/models/yolov5s.yaml
  3. # 参数
  4. nc: 80 # 类别数量 (例如COCO数据集有80类)
  5. depth_multiple: 0.33 # 模型深度因子
  6. width_multiple: 0.50 # 模型宽度因子
  7. # 网络结构定义
  8. backbone:
  9. # [from, number, module, args]
  10. [[-1, 1, Focus, [64, 3]], # 0-P1/2
  11. [-1, 1, Conv, [128, 3, 2]], # 1-P2/4
  12. [-1, 3, C3, [128]],
  13. [-1, 1, Conv, [256, 3, 2]], # 3-P3/8
  14. [-1, 9, C3, [256]],
  15. [-1, 1, Conv, [512, 3, 2]], # 5-P4/16
  16. [-1, 9, C3, [512]],
  17. [-1, 1, Conv, [1024, 3, 2]], # 7-P5/32
  18. [-1, 1, SPP, [1024, [5, 9, 13]]],
  19. [-1, 3, C3, [1024, False]], # 9
  20. ]
  21. head:
  22. [[-1, 1, Conv, [512, 1, 1]],
  23. [-1, 1, nn.Upsample, [None, 2, 'nearest']],
  24. [[-1, 6], 1, Concat, [1]], # cat backbone P4
  25. [-1, 3, C3, [512, False]], # 13
  26. [-1, 1, Conv, [256, 1, 1]],
  27. [-1, 1, nn.Upsample, [None, 2, 'nearest']],
  28. [[-1, 4], 1, Concat, [1]], # cat backbone P3
  29. [-1, 3, C3, [256, False]], # 17 (P3/8-small)
  30. [-1, 1, Conv, [256, 3, 2]],
  31. [[-1, 14], 1, Concat, [1]], # cat head P4
  32. [-1, 3, C3, [512, False]], # 20 (P4/16-medium)
  33. [-1, 1, Conv, [512, 3, 2]],
  34. [[-1, 10], 1, Concat, [1]], # cat head P5
  35. [-1, 3, C3, [1024, False]], # 23 (P5/32-large)
  36. [[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)
  37. ]