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yolov5-p6.yaml 1.7KB

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  1. # YOLOv5 🚀 by Ultralytics, GPL-3.0 license
  2. # Parameters
  3. nc: 80 # number of classes
  4. depth_multiple: 1.0 # model depth multiple
  5. width_multiple: 1.0 # layer channel multiple
  6. anchors: 3 # auto-anchor 3 anchors per P output layer
  7. # YOLOv5 v6.0 backbone
  8. backbone:
  9. # [from, number, module, args]
  10. [[-1, 1, Conv, [64, 6, 2, 2]], # 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, 6, C3, [256]],
  15. [-1, 1, Conv, [512, 3, 2]], # 5-P4/16
  16. [-1, 9, C3, [512]],
  17. [-1, 1, Conv, [768, 3, 2]], # 7-P5/32
  18. [-1, 3, C3, [768]],
  19. [-1, 1, Conv, [1024, 3, 2]], # 9-P6/64
  20. [-1, 3, C3, [1024]],
  21. [-1, 1, SPPF, [1024, 5]], # 11
  22. ]
  23. # YOLOv5 v6.0 head
  24. head:
  25. [[-1, 1, Conv, [768, 1, 1]],
  26. [-1, 1, nn.Upsample, [None, 2, 'nearest']],
  27. [[-1, 8], 1, Concat, [1]], # cat backbone P5
  28. [-1, 3, C3, [768, False]], # 15
  29. [-1, 1, Conv, [512, 1, 1]],
  30. [-1, 1, nn.Upsample, [None, 2, 'nearest']],
  31. [[-1, 6], 1, Concat, [1]], # cat backbone P4
  32. [-1, 3, C3, [512, False]], # 19
  33. [-1, 1, Conv, [256, 1, 1]],
  34. [-1, 1, nn.Upsample, [None, 2, 'nearest']],
  35. [[-1, 4], 1, Concat, [1]], # cat backbone P3
  36. [-1, 3, C3, [256, False]], # 23 (P3/8-small)
  37. [-1, 1, Conv, [256, 3, 2]],
  38. [[-1, 20], 1, Concat, [1]], # cat head P4
  39. [-1, 3, C3, [512, False]], # 26 (P4/16-medium)
  40. [-1, 1, Conv, [512, 3, 2]],
  41. [[-1, 16], 1, Concat, [1]], # cat head P5
  42. [-1, 3, C3, [768, False]], # 29 (P5/32-large)
  43. [-1, 1, Conv, [768, 3, 2]],
  44. [[-1, 12], 1, Concat, [1]], # cat head P6
  45. [-1, 3, C3, [1024, False]], # 32 (P6/64-xlarge)
  46. [[23, 26, 29, 32], 1, Detect, [nc, anchors]], # Detect(P3, P4, P5, P6)
  47. ]