yolov3-tiny.yaml 1.2 KB

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  1. # Ultralytics YOLO 🚀, AGPL-3.0 license
  2. # YOLOv3-tiny object detection model with P4-P5 outputs. For details see https://docs.ultralytics.com/models/yolov3
  3. # Parameters
  4. nc: 80 # number of classes
  5. depth_multiple: 1.0 # model depth multiple
  6. width_multiple: 1.0 # layer channel multiple
  7. # YOLOv3-tiny backbone
  8. backbone:
  9. # [from, number, module, args]
  10. [[-1, 1, Conv, [16, 3, 1]], # 0
  11. [-1, 1, nn.MaxPool2d, [2, 2, 0]], # 1-P1/2
  12. [-1, 1, Conv, [32, 3, 1]],
  13. [-1, 1, nn.MaxPool2d, [2, 2, 0]], # 3-P2/4
  14. [-1, 1, Conv, [64, 3, 1]],
  15. [-1, 1, nn.MaxPool2d, [2, 2, 0]], # 5-P3/8
  16. [-1, 1, Conv, [128, 3, 1]],
  17. [-1, 1, nn.MaxPool2d, [2, 2, 0]], # 7-P4/16
  18. [-1, 1, Conv, [256, 3, 1]],
  19. [-1, 1, nn.MaxPool2d, [2, 2, 0]], # 9-P5/32
  20. [-1, 1, Conv, [512, 3, 1]],
  21. [-1, 1, nn.ZeroPad2d, [[0, 1, 0, 1]]], # 11
  22. [-1, 1, nn.MaxPool2d, [2, 1, 0]], # 12
  23. ]
  24. # YOLOv3-tiny head
  25. head:
  26. [[-1, 1, Conv, [1024, 3, 1]],
  27. [-1, 1, Conv, [256, 1, 1]],
  28. [-1, 1, Conv, [512, 3, 1]], # 15 (P5/32-large)
  29. [-2, 1, Conv, [128, 1, 1]],
  30. [-1, 1, nn.Upsample, [None, 2, 'nearest']],
  31. [[-1, 8], 1, Concat, [1]], # cat backbone P4
  32. [-1, 1, Conv, [256, 3, 1]], # 19 (P4/16-medium)
  33. [[19, 15], 1, Detect, [nc]], # Detect(P4, P5)
  34. ]