# parameters
#nc: 80  # number of classes
nc: 3  # number of classes
depth_multiple: 0.67  # model depth multiple
width_multiple: 0.75  # layer channel multiple   使卷积核个数变化

# anchors
anchors:
  - [5,6, 8,14, 15,11]  #P2/4  增加的锚点？？？？？ 增减检测层之后需要增加的   先验框的大小（4个尺度上的）
  - [10,13, 16,30, 33,23]  # P3/8  表示8倍下采样后的结果
  - [30,61, 62,45, 59,119]  # P4/16
  - [116,90, 156,198, 373,326]  # P5/32

# YOLOv5 backbone
backbone:
  # [from, number, module, args]
  #-1表示来自上一层输入；number表示本模块重复次数；

  [[-1, 1, Focus, [64, 3]],  # 0-P1/2   功能层参数
   [-1, 1, Conv, [128, 3, 2]],  # 1-P2/4   功能层参数  128表示128个卷积核，3表示3x3卷积核，2表示步长是2
   [-1, 3, C3, [128]],   #160*160   瓶颈层是主要学习到特征，可增减瓶颈层的深度实现模型深度变化

   [-1, 1, Conv, [256, 3, 2]],  # 3-P3/8
   [-1, 9, C3, [256]],  #80*80

   [-1, 1, Conv, [512, 3, 2]],  # 5-P4/16
   [-1, 9, C3, [512]], #40*40

   [-1, 1, Conv, [1024, 3, 2]],  # 7-P5/32
   [-1, 1, SPP, [1024, [5, 9, 13]]],  #spp也是功能层的参数
   [-1, 3, C3, [1024, False]],  # 9   20*20
  ]

# YOLOv5 head
head:
  [[-1, 1, Conv, [512, 1, 1]],  #20*20
   [-1, 1, nn.Upsample, [None, 2, 'nearest']], #40*40
   [[-1, 6], 1, Concat, [1]],  # cat backbone P4  40*40
   [-1, 3, C3, [512, False]],  # 13     40*40

   [-1, 1, Conv, [512, 1, 1]], #40*40
   [-1, 1, nn.Upsample, [None, 2, 'nearest']],
   [[-1, 4], 1, Concat, [1]],  # cat backbone P3   80*80
   [-1, 3, C3, [512, False]],  # 17 (P3/8-small)  80*80

   [-1, 1, Conv, [256, 1, 1]], #18  80*80
   [-1, 1, nn.Upsample, [None, 2, 'nearest']], #19  160*160
   [[-1, 2], 1, Concat, [1]], #20 cat backbone p2  160*160
   [-1, 3, C3, [256, False]], #21 160*160

   [-1, 1, Conv, [256, 3, 2]],  #22   80*80
   [[-1, 18], 1, Concat, [1]], #23 80*80
   [-1, 3, C3, [256, False]], #24 80*80

   [-1, 1, Conv, [256, 3, 2]], #25  40*40
   [[-1, 14], 1, Concat, [1]],  # 26  cat head P4  40*40
   [-1, 3, C3, [512, False]],  # 27 (P4/16-medium) 40*40

   [-1, 1, Conv, [512, 3, 2]],  #28  20*20
   [[-1, 10], 1, Concat, [1]],  #29 cat head P5  #20*20
   [-1, 3, C3, [1024, False]],  # 30 (P5/32-large)  20*20

   [[21, 24, 27, 30], 1, Detect, [nc, anchors]],  # Detect(p2, P3, P4, P5)
  ]