# YOLOv5 🚀 by Ultralytics, AGPL-3.0 license

# Parameters

nc: 80  # number of classes

depth_multiple: 0.33  # model depth multiple

width_multiple: 0.25  # layer channel multiple

anchors:

  - [19,27,  44,40,  38,94]  # P3/8

  - [96,68,  86,152,  180,137]  # P4/16

  - [140,301,  303,264,  238,542]  # P5/32

  - [436,615,  739,380,  925,792]  # P6/64

# YOLOv5 v6.0 backbone

backbone:

  # [from, number, module, args]

  [[-1, 1, Conv, [64, 6, 2, 2]],  # 0-P1/2

   [-1, 1, Conv, [128, 3, 2]],  # 1-P2/4

   [-1, 3, C3, [128]],

   [-1, 1, Conv, [256, 3, 2]],  # 3-P3/8

   [-1, 6, C3, [256]],

   [-1, 1, Conv, [512, 3, 2]],  # 5-P4/16

   [-1, 9, C3, [512]],

   [-1, 1, Conv, [768, 3, 2]],  # 7-P5/32

   [-1, 3, C3, [768]],

   [-1, 1, Conv, [1024, 3, 2]],  # 9-P6/64

   [-1, 3, C3, [1024]],

   [-1, 1, SPPF, [1024, 5]],  # 11

  ]

# YOLOv5 v6.0 head

head:

  [[-1, 1, Conv, [768, 1, 1]],

   [-1, 1, nn.Upsample, [None, 2, 'nearest']],

   [[-1, 8], 1, Concat, [1]],  # cat backbone P5

   [-1, 3, C3, [768, False]],  # 15

   [-1, 1, Conv, [512, 1, 1]],

   [-1, 1, nn.Upsample, [None, 2, 'nearest']],

   [[-1, 6], 1, Concat, [1]],  # cat backbone P4

   [-1, 3, C3, [512, False]],  # 19

   [-1, 1, Conv, [256, 1, 1]],

   [-1, 1, nn.Upsample, [None, 2, 'nearest']],

   [[-1, 4], 1, Concat, [1]],  # cat backbone P3

   [-1, 3, C3, [256, False]],  # 23 (P3/8-small)

   [-1, 1, Conv, [256, 3, 2]],

   [[-1, 20], 1, Concat, [1]],  # cat head P4

   [-1, 3, C3, [512, False]],  # 26 (P4/16-medium)

   [-1, 1, Conv, [512, 3, 2]],

   [[-1, 16], 1, Concat, [1]],  # cat head P5

   [-1, 3, C3, [768, False]],  # 29 (P5/32-large)

   [-1, 1, Conv, [768, 3, 2]],

   [[-1, 12], 1, Concat, [1]],  # cat head P6

   [-1, 3, C3, [1024, False]],  # 32 (P6/64-xlarge)

   [[23, 26, 29, 32], 1, Detect, [nc, anchors]],  # Detect(P3, P4, P5, P6)

  ]