models/yolov5l.yaml

# parameters
nc: 1  # number of classes
depth_multiple: 1.0  # model depth multiple
width_multiple: 1.0  # layer channel multiple

# anchors
anchors:
  - [4,5,  8,10,  13,16]  # P3/8
  - [23,29,  43,55,  73,105]  # P4/16
  - [146,217,  231,300,  335,433]  # P5/32

# YOLOv5 backbone
backbone:
  # [from, number, module, args]
  [[-1, 1, StemBlock, [64, 3, 2]],  # 0-P1/2
   [-1, 3, C3, [128]],
   [-1, 1, Conv, [256, 3, 2]],      # 2-P3/8
   [-1, 9, C3, [256]],
   [-1, 1, Conv, [512, 3, 2]],      # 4-P4/16
   [-1, 9, C3, [512]],
   [-1, 1, Conv, [1024, 3, 2]],     # 6-P5/32
   [-1, 1, SPP, [1024, [3,5,7]]],
   [-1, 3, C3, [1024, False]],      # 8
  ]

# YOLOv5 head
head:
  [[-1, 1, Conv, [512, 1, 1]],
   [-1, 1, nn.Upsample, [None, 2, 'nearest']],
   [[-1, 5], 1, Concat, [1]],  # cat backbone P4
   [-1, 3, C3, [512, False]],  # 12

   [-1, 1, Conv, [256, 1, 1]],
   [-1, 1, nn.Upsample, [None, 2, 'nearest']],
   [[-1, 3], 1, Concat, [1]],  # cat backbone P3
   [-1, 3, C3, [256, False]],  # 16 (P3/8-small)

   [-1, 1, Conv, [256, 3, 2]],
   [[-1, 13], 1, Concat, [1]],  # cat head P4
   [-1, 3, C3, [512, False]],  # 19 (P4/16-medium)

   [-1, 1, Conv, [512, 3, 2]],
   [[-1, 9], 1, Concat, [1]],  # cat head P5
   [-1, 3, C3, [1024, False]],  # 22 (P5/32-large)

   [[16, 19, 22], 1, Detect, [nc, anchors]],  # Detect(P3, P4, P5)
  ]