高速公路违停检测
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  1. # parameters
  2. nc: 80 # number of classes
  3. depth_multiple: 1.0 # model depth multiple
  4. width_multiple: 1.0 # layer channel multiple
  5. # anchors
  6. anchors: 3
  7. # YOLOv5 backbone
  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. # YOLOv5 head
  22. head:
  23. [ [ -1, 1, Conv, [ 512, 1, 1 ] ],
  24. [ -1, 1, nn.Upsample, [ None, 2, 'nearest' ] ],
  25. [ [ -1, 6 ], 1, Concat, [ 1 ] ], # cat backbone P4
  26. [ -1, 3, C3, [ 512, False ] ], # 13
  27. [ -1, 1, Conv, [ 256, 1, 1 ] ],
  28. [ -1, 1, nn.Upsample, [ None, 2, 'nearest' ] ],
  29. [ [ -1, 4 ], 1, Concat, [ 1 ] ], # cat backbone P3
  30. [ -1, 3, C3, [ 256, False ] ], # 17 (P3/8-small)
  31. [ -1, 1, Conv, [ 128, 1, 1 ] ],
  32. [ -1, 1, nn.Upsample, [ None, 2, 'nearest' ] ],
  33. [ [ -1, 2 ], 1, Concat, [ 1 ] ], # cat backbone P2
  34. [ -1, 1, C3, [ 128, False ] ], # 21 (P2/4-xsmall)
  35. [ -1, 1, Conv, [ 128, 3, 2 ] ],
  36. [ [ -1, 18 ], 1, Concat, [ 1 ] ], # cat head P3
  37. [ -1, 3, C3, [ 256, False ] ], # 24 (P3/8-small)
  38. [ -1, 1, Conv, [ 256, 3, 2 ] ],
  39. [ [ -1, 14 ], 1, Concat, [ 1 ] ], # cat head P4
  40. [ -1, 3, C3, [ 512, False ] ], # 27 (P4/16-medium)
  41. [ -1, 1, Conv, [ 512, 3, 2 ] ],
  42. [ [ -1, 10 ], 1, Concat, [ 1 ] ], # cat head P5
  43. [ -1, 3, C3, [ 1024, False ] ], # 30 (P5/32-large)
  44. [ [ 24, 27, 30 ], 1, Detect, [ nc, anchors ] ], # Detect(P3, P4, P5)
  45. ]