yolor-csp.yaml 1.6 KB

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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. - [12,16, 19,36, 40,28] # P3/8
  8. - [36,75, 76,55, 72,146] # P4/16
  9. - [142,110, 192,243, 459,401] # P5/32
  10. # CSP-Darknet 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, BottleneckCSPC, [128]],
  18. [-1, 1, Conv, [256, 3, 2]], # 5-P3/8
  19. [-1, 8, BottleneckCSPC, [256]],
  20. [-1, 1, Conv, [512, 3, 2]], # 7-P4/16
  21. [-1, 8, BottleneckCSPC, [512]],
  22. [-1, 1, Conv, [1024, 3, 2]], # 9-P5/32
  23. [-1, 4, BottleneckCSPC, [1024]], # 10
  24. ]
  25. # CSP-Dark-PAN head
  26. head:
  27. [[-1, 1, SPPCSPC, [512]], # 11
  28. [-1, 1, Conv, [256, 1, 1]],
  29. [-1, 1, nn.Upsample, [None, 2, 'nearest']],
  30. [8, 1, Conv, [256, 1, 1]], # route backbone P4
  31. [[-1, -2], 1, Concat, [1]],
  32. [-1, 2, BottleneckCSPB, [256]], # 16
  33. [-1, 1, Conv, [128, 1, 1]],
  34. [-1, 1, nn.Upsample, [None, 2, 'nearest']],
  35. [6, 1, Conv, [128, 1, 1]], # route backbone P3
  36. [[-1, -2], 1, Concat, [1]],
  37. [-1, 2, BottleneckCSPB, [128]], # 21
  38. [-1, 1, Conv, [256, 3, 1]],
  39. [-2, 1, Conv, [256, 3, 2]],
  40. [[-1, 16], 1, Concat, [1]], # cat
  41. [-1, 2, BottleneckCSPB, [256]], # 25
  42. [-1, 1, Conv, [512, 3, 1]],
  43. [-2, 1, Conv, [512, 3, 2]],
  44. [[-1, 11], 1, Concat, [1]], # cat
  45. [-1, 2, BottleneckCSPB, [512]], # 29
  46. [-1, 1, Conv, [1024, 3, 1]],
  47. [[22,26,30], 1, IDetect, [nc, anchors]], # Detect(P3, P4, P5)
  48. ]