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yolov5s.yaml 1.4KB

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  1. # parameters
  2. nc: 80 # number of classes
  3. depth_multiple: 0.33 # model depth multiple
  4. width_multiple: 0.50 # 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]], # 1-P1/2
  14. [-1, 1, Conv, [128, 3, 2]], # 2-P2/4
  15. [-1, 3, Bottleneck, [128]],
  16. [-1, 1, Conv, [256, 3, 2]], # 4-P3/8
  17. [-1, 9, BottleneckCSP, [256]],
  18. [-1, 1, Conv, [512, 3, 2]], # 6-P4/16
  19. [-1, 9, BottleneckCSP, [512]],
  20. [-1, 1, Conv, [1024, 3, 2]], # 8-P5/32
  21. [-1, 1, SPP, [1024, [5, 9, 13]]],
  22. [-1, 6, BottleneckCSP, [1024]], # 10
  23. ]
  24. # yolov5 head
  25. head:
  26. [[-1, 3, BottleneckCSP, [1024, False]], # 11
  27. [-1, 1, nn.Conv2d, [na * (nc + 5), 1, 1, 0]], # 12 (P5/32-large)
  28. [-2, 1, nn.Upsample, [None, 2, 'nearest']],
  29. [[-1, 6], 1, Concat, [1]], # cat backbone P4
  30. [-1, 1, Conv, [512, 1, 1]],
  31. [-1, 3, BottleneckCSP, [512, False]],
  32. [-1, 1, nn.Conv2d, [na * (nc + 5), 1, 1, 0]], # 17 (P4/16-medium)
  33. [-2, 1, nn.Upsample, [None, 2, 'nearest']],
  34. [[-1, 4], 1, Concat, [1]], # cat backbone P3
  35. [-1, 1, Conv, [256, 1, 1]],
  36. [-1, 3, BottleneckCSP, [256, False]],
  37. [-1, 1, nn.Conv2d, [na * (nc + 5), 1, 1, 0]], # 22 (P3/8-small)
  38. [[], 1, Detect, [nc, anchors]], # Detect(P3, P4, P5)
  39. ]