# parameters
nc: 80  # number of classes
depth_multiple: 1.0  # model depth multiple
width_multiple: 1.0  # layer channel multiple

# anchors
anchors:
  - [12,16, 19,36, 40,28]  # P3/8
  - [36,75, 76,55, 72,146]  # P4/16
  - [142,110, 192,243, 459,401]  # P5/32

# CSP-Darknet backbone
backbone:
  # [from, number, module, args]
  [[-1, 1, Conv, [32, 3, 1]],  # 0
   [-1, 1, Conv, [64, 3, 2]],  # 1-P1/2
   [-1, 1, Bottleneck, [64]],
   [-1, 1, Conv, [128, 3, 2]],  # 3-P2/4
   [-1, 2, BottleneckCSPC, [128]],
   [-1, 1, Conv, [256, 3, 2]],  # 5-P3/8
   [-1, 8, BottleneckCSPC, [256]],
   [-1, 1, Conv, [512, 3, 2]],  # 7-P4/16
   [-1, 8, BottleneckCSPC, [512]],
   [-1, 1, Conv, [1024, 3, 2]], # 9-P5/32
   [-1, 4, BottleneckCSPC, [1024]],  # 10
  ]

# CSP-Dark-PAN head
head:
  [[-1, 1, SPPCSPC, [512]], # 11
   [-1, 1, Conv, [256, 1, 1]],
   [-1, 1, nn.Upsample, [None, 2, 'nearest']],
   [8, 1, Conv, [256, 1, 1]], # route backbone P4
   [[-1, -2], 1, Concat, [1]],
   [-1, 2, BottleneckCSPB, [256]], # 16 
   [-1, 1, Conv, [128, 1, 1]],
   [-1, 1, nn.Upsample, [None, 2, 'nearest']],
   [6, 1, Conv, [128, 1, 1]], # route backbone P3
   [[-1, -2], 1, Concat, [1]],
   [-1, 2, BottleneckCSPB, [128]], # 21
   [-1, 1, Conv, [256, 3, 1]],
   [-2, 1, Conv, [256, 3, 2]],
   [[-1, 16], 1, Concat, [1]],  # cat
   [-1, 2, BottleneckCSPB, [256]], # 25
   [-1, 1, Conv, [512, 3, 1]],
   [-2, 1, Conv, [512, 3, 2]],
   [[-1, 11], 1, Concat, [1]],  # cat
   [-1, 2, BottleneckCSPB, [512]], # 29
   [-1, 1, Conv, [1024, 3, 1]],

   [[22,26,30], 1, Detect, [nc, anchors]],   # Detect(P3, P4, P5)
  ]
