高速公路违停检测
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  1. # parameters
  2. nc: 3 # number of classes
  3. depth_multiple: 1.33 # model depth multiple
  4. width_multiple: 1.25 # layer channel multiple
  5. # anchors
  6. anchors:
  7. - [10,13, 16,30, 33,23] # P3/8
  8. - [30,61, 62,45, 59,119] # P4/16
  9. - [116,90, 156,198, 373,326] # P5/32
  10. # YOLOv5 backbone
  11. backbone:
  12. # [from, number, module, args]
  13. [[-1, 1, Focus, [64, 3]], # 0-P1/2
  14. [-1, 1, Conv, [128, 3, 2]], # 1-P2/4
  15. [-1, 3, C3, [128]],
  16. [-1, 1, Conv, [256, 3, 2]], # 3-P3/8
  17. [-1, 9, C3, [256]],
  18. [-1, 1, Conv, [512, 3, 2]], # 5-P4/16
  19. [-1, 9, C3, [512]],
  20. [-1, 1, Conv, [1024, 3, 2]], # 7-P5/32
  21. [-1, 1, SPP, [1024, [5, 9, 13]]],
  22. [-1, 3, C3, [1024, False]], # 9
  23. ]
  24. # YOLOv5 head
  25. head:
  26. [[-1, 1, Conv, [512, 1, 1]],
  27. [-1, 1, nn.Upsample, [None, 2, 'nearest']],
  28. [[-1, 6], 1, Concat, [1]], # cat backbone P4
  29. [-1, 3, C3, [512, False]], # 13
  30. [-1, 1, Conv, [256, 1, 1]],
  31. [-1, 1, nn.Upsample, [None, 2, 'nearest']],
  32. [[-1, 4], 1, Concat, [1]], # cat backbone P3
  33. [-1, 3, C3, [256, False]], # 17 (P3/8-small)
  34. [-1, 1, Conv, [256, 3, 2]],
  35. [[-1, 14], 1, Concat, [1]], # cat head P4
  36. [-1, 3, C3, [512, False]], # 20 (P4/16-medium)
  37. [-1, 1, Conv, [512, 3, 2]],
  38. [[-1, 10], 1, Concat, [1]], # cat head P5
  39. [-1, 3, C3, [1024, False]], # 23 (P5/32-large)
  40. [[17, 20, 23], 1, Detect, [nc, anchors]], # Detect(P3, P4, P5)
  41. ]