Replacing the YOLO 11 backbone with ResNet 50

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The indices are all wrong. If you’re new to computer vision, you shouldn’t be doing advanced things like these without understanding how they work.

nc: 2

scales:
  n: [0.33, 0.25, 1024]
  s: [0.33, 0.50, 1024]
  m: [0.67, 0.75, 768]
  l: [1.00, 1.00, 1024]
  x: [1.00, 1.25, 512]

backbone:

  # [from, repeats, module, args]

  - [-1, 1, TorchVision, [3, resnet50, DEFAULT, True, 2, True]] 

  - [0, 1, Index, [256, 5]]   # P3/8

  - [0, 1, Index, [512, 6]]   # P4/16

  - [0, 1, Index, [1024, 7]]  # P5/32

head:
  - [-1, 1, nn.Upsample, [None, 2, "nearest"]]
  - [[-1, 2], 1, Concat, [1]] # cat backbone P4
  - [-1, 2, C3k2, [512, False]] #

  - [-1, 1, nn.Upsample, [None, 2, "nearest"]]
  - [[-1, 1], 1, Concat, [1]] # cat backbone P3
  - [-1, 2, C3k2, [256, False]] # (P3/8-small)

  - [-1, 1, Conv, [256, 3, 2]]
  - [[-1, 6], 1, Concat, [1]] # cat head P4
  - [-1, 2, C3k2, [512, False]] # (P4/16-medium)

  - [-1, 1, Conv, [512, 3, 2]]
  - [[-1, 3], 1, Concat, [1]] # cat head P5
  - [-1, 2, C3k2, [1024, True]] # (P5/32-large)

  - [[-7, -4, -1], 1, Detect, [nc]] # Detect(P3, P4, P5)
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