license_plate_recognition/models/yolov5l6.yaml

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

# anchors
anchors:
  - [6,7,  9,11,  13,16]  # P3/8
  - [18,23,  26,33,  37,47]  # P4/16
  - [54,67,  77,104,  112,154]  # P5/32
  - [174,238,  258,355,  445,568]  # P6/64

# 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, [ 768, 3, 2 ] ],  # 6-P5/32
    [ -1, 3, C3, [ 768 ] ],
    [ -1, 1, Conv, [ 1024, 3, 2 ] ],  # 8-P6/64
    [ -1, 1, SPP, [ 1024, [ 3, 5, 7 ] ] ],
    [ -1, 3, C3, [ 1024, False ] ],  # 10
  ]

# YOLOv5 head
head:
  [ [ -1, 1, Conv, [ 768, 1, 1 ] ],
    [ -1, 1, nn.Upsample, [ None, 2, 'nearest' ] ],
    [ [ -1, 7 ], 1, Concat, [ 1 ] ],  # cat backbone P5
    [ -1, 3, C3, [ 768, False ] ],  # 14

    [ -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 ] ],  # 18

    [ -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 ] ],  # 22 (P3/8-small)

    [ -1, 1, Conv, [ 256, 3, 2 ] ],
    [ [ -1, 19 ], 1, Concat, [ 1 ] ],  # cat head P4
    [ -1, 3, C3, [ 512, False ] ],  # 25 (P4/16-medium)

    [ -1, 1, Conv, [ 512, 3, 2 ] ],
    [ [ -1, 15 ], 1, Concat, [ 1 ] ],  # cat head P5
    [ -1, 3, C3, [ 768, False ] ],  # 28 (P5/32-large)

    [ -1, 1, Conv, [ 768, 3, 2 ] ],
    [ [ -1, 11 ], 1, Concat, [ 1 ] ],  # cat head P6
    [ -1, 3, C3, [ 1024, False ] ],  # 31 (P6/64-xlarge)

    [ [ 22, 25, 28, 31 ], 1, Detect, [ nc, anchors ] ],  # Detect(P3, P4, P5, P6)
  ]