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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:
  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. # darknet53 backbone
  11. backbone:
  12. # [from, number, module, args]
  13. [[-1, 1, Conv, [32, 3, 1]], # 0
  14. [-1, 1, Conv, [64, 3, 2]], # 1-P1/2
  15. [-1, 1, Bottleneck, [64]],
  16. [-1, 1, Conv, [128, 3, 2]], # 3-P2/4
  17. [-1, 2, Bottleneck, [128]],
  18. [-1, 1, Conv, [256, 3, 2]], # 5-P3/8
  19. [-1, 8, Bottleneck, [256]],
  20. [-1, 1, Conv, [512, 3, 2]], # 7-P4/16
  21. [-1, 8, Bottleneck, [512]],
  22. [-1, 1, Conv, [1024, 3, 2]], # 9-P5/32
  23. [-1, 4, Bottleneck, [1024]], # 10
  24. ]
  25. # YOLOv3-SPP head
  26. head:
  27. [[-1, 1, Bottleneck, [1024, False]], # 11
  28. [-1, 1, SPP, [512, [5, 9, 13]]],
  29. [-1, 1, Conv, [1024, 3, 1]],
  30. [-1, 1, Conv, [512, 1, 1]],
  31. [-1, 1, Conv, [1024, 3, 1]],
  32. [-1, 1, nn.Conv2d, [na * (nc + 5), 1, 1]], # 16 (P5/32-large)
  33. [-3, 1, Conv, [256, 1, 1]],
  34. [-1, 1, nn.Upsample, [None, 2, 'nearest']],
  35. [[-1, 8], 1, Concat, [1]], # cat backbone P4
  36. [-1, 1, Bottleneck, [512, False]],
  37. [-1, 1, Bottleneck, [512, False]],
  38. [-1, 1, Conv, [256, 1, 1]],
  39. [-1, 1, Conv, [512, 3, 1]],
  40. [-1, 1, nn.Conv2d, [na * (nc + 5), 1, 1]], # 24 (P4/16-medium)
  41. [-3, 1, Conv, [128, 1, 1]],
  42. [-1, 1, nn.Upsample, [None, 2, 'nearest']],
  43. [[-1, 6], 1, Concat, [1]], # cat backbone P3
  44. [-1, 1, Bottleneck, [256, False]],
  45. [-1, 2, Bottleneck, [256, False]],
  46. [-1, 1, nn.Conv2d, [na * (nc + 5), 1, 1]], # 30 (P3/8-small)
  47. [[], 1, Detect, [nc, anchors]], # Detect(P3, P4, P5)
  48. ]