models/hub/yolov5-fpn.yaml

# Parameters
nc: 80  # number of classes
depth_multiple: 1.0  # model depth multiple
width_multiple: 1.0  # layer channel multiple
anchors:
  - [ 10,13, 16,30, 33,23 ]  # P3/8
  - [ 30,61, 62,45, 59,119 ]  # P4/16
  - [ 116,90, 156,198, 373,326 ]  # P5/32

# YOLOv5 backbone
backbone:
  # [from, number, module, args]
  [ [ -1, 1, Focus, [ 64, 3 ] ],  # 0-P1/2
    [ -1, 1, Conv, [ 128, 3, 2 ] ],  # 1-P2/4
    [ -1, 3, Bottleneck, [ 128 ] ],
    [ -1, 1, Conv, [ 256, 3, 2 ] ],  # 3-P3/8
    [ -1, 9, BottleneckCSP, [ 256 ] ],
    [ -1, 1, Conv, [ 512, 3, 2 ] ],  # 5-P4/16
    [ -1, 9, BottleneckCSP, [ 512 ] ],
    [ -1, 1, Conv, [ 1024, 3, 2 ] ],  # 7-P5/32
    [ -1, 1, SPP, [ 1024, [ 5, 9, 13 ] ] ],
    [ -1, 6, BottleneckCSP, [ 1024 ] ],  # 9
  ]

# YOLOv5 FPN head
head:
  [ [ -1, 3, BottleneckCSP, [ 1024, False ] ],  # 10 (P5/32-large)

    [ -1, 1, nn.Upsample, [ None, 2, 'nearest' ] ],
    [ [ -1, 6 ], 1, Concat, [ 1 ] ],  # cat backbone P4
    [ -1, 1, Conv, [ 512, 1, 1 ] ],
    [ -1, 3, BottleneckCSP, [ 512, False ] ],  # 14 (P4/16-medium)

    [ -1, 1, nn.Upsample, [ None, 2, 'nearest' ] ],
    [ [ -1, 4 ], 1, Concat, [ 1 ] ],  # cat backbone P3
    [ -1, 1, Conv, [ 256, 1, 1 ] ],
    [ -1, 3, BottleneckCSP, [ 256, False ] ],  # 18 (P3/8-small)

    [ [ 18, 14, 10 ], 1, Detect, [ nc, anchors ] ],  # Detect(P3, P4, P5)
  ]