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专栏介绍：YOLOv9改进系列 | 包含深度学习最新创新，助力高效涨点！！！

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1. 文件说明

        在YOLOv5-v9，模型的结构是以yaml文件的存储。我们可以在原有的yaml基础上增、减、改模块，创作我们自己的模型。

YOLO系列 “.yaml“文件解读

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2. 改进示例

        以下分别以ACmix与C2f-SCConv为例，演示如何加入到YOLOv9-c.yaml文件中。

2.1 加入ACmix

2.1.1创建yaml文件

         首先复制一个yolov9-c.yaml文件，重命名为yolov9-ACmix-SCConv.yaml（其他命名也可）

2.1.2 加入模块

        在想加入ACmix模块的位置，按照使用说明的格式（或查看ACmix模块所需的初始化参数）插入模块。这里以在“#9”层后加入为例：

2.1.3 函数说明

        使用说明中的格式与ACmix的“init”函数。

2.1 加入C2f-SCConv

        查看C2f-SCConv的使用说明，需要填入的参数为c2，即输出通道数，其他括号中的参数可不填。

# YOLOv9

# parameters
nc: 80  # number of classes
depth_multiple: 1.0  # model depth multiple
width_multiple: 1.0  # layer channel multiple
#activation: nn.LeakyReLU(0.1)
#activation: nn.ReLU()

# anchors
anchors: 3

# YOLOv9 backbone
backbone:
  [
   [-1, 1, Silence, []],  
   
   # conv down
   [-1, 1, Conv, [64, 3, 2]],  # 1-P1/2

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

   # elan-1 block
   [-1, 1, RepNCSPELAN4, [256, 128, 64, 1]],  # 3

   # avg-conv down
   [-1, 1, ADown, [256]],  # 4-P3/8

   # elan-2 block
   [-1, 1, RepNCSPELAN4, [512, 256, 128, 1]],  # 5

   [-1, 1, C2fSCConv, [512]],  # 6

   # avg-conv down
   [-1, 1, ADown, [512]],  # 7-P4/16

   # elan-2 block
   [-1, 1, RepNCSPELAN4, [512, 512, 256, 1]],  # 8

   # avg-conv down
   [-1, 1, ADown, [512]],  # 9-P5/32

   # elan-2 block
   [-1, 1, RepNCSPELAN4, [512, 512, 256, 1]],  # 10
   [-1, 1, ACmix, [7]],  # 11
  ]

# YOLOv9 head
head:
  [
   # elan-spp block
   [-1, 1, SPPELAN, [512, 256]],  # 12

   # up-concat merge
   [-1, 1, nn.Upsample, [None, 2, 'nearest']],
   [[-1, 8], 1, Concat, [1]],  # cat backbone P4

   # elan-2 block
   [-1, 1, RepNCSPELAN4, [512, 512, 256, 1]],  # 15

   # up-concat merge
   [-1, 1, nn.Upsample, [None, 2, 'nearest']],
   [[-1, 6], 1, Concat, [1]],  # cat backbone P3

   # elan-2 block
   [-1, 1, RepNCSPELAN4, [256, 256, 128, 1]],  # 18 (P3/8-small)

   # avg-conv-down merge
   [-1, 1, ADown, [256]],
   [[-1, 15], 1, Concat, [1]],  # cat head P4

   # elan-2 block
   [-1, 1, RepNCSPELAN4, [512, 512, 256, 1]],  # 21 (P4/16-medium)

   # avg-conv-down merge
   [-1, 1, ADown, [512]],
   [[-1, 12], 1, Concat, [1]],  # cat head P5

   # elan-2 block
   [-1, 1, RepNCSPELAN4, [512, 512, 256, 1]],  # 24 (P5/32-large)
   

   # multi-level reversible auxiliary branch

   # routing
   [6, 1, CBLinear, [[256]]], # 25
   [8, 1, CBLinear, [[256, 512]]], # 26
   [11, 1, CBLinear, [[256, 512, 512]]], # 27

   # conv down
   [0, 1, Conv, [64, 3, 2]],  # 28-P1/2

   # conv down
   [-1, 1, Conv, [128, 3, 2]],  # 29-P2/4

   # elan-1 block
   [-1, 1, RepNCSPELAN4, [256, 128, 64, 1]],  # 30

   # avg-conv down fuse
   [-1, 1, ADown, [256]],  # 31-P3/8
   [[25, 26, 27, -1], 1, CBFuse, [[0, 0, 0]]], # 32

   # elan-2 block
   [-1, 1, RepNCSPELAN4, [512, 256, 128, 1]],  # 33

   # avg-conv down fuse
   [-1, 1, ADown, [512]],  # 34-P4/16
   [[26, 27, -1], 1, CBFuse, [[1, 1]]], # 35

   # elan-2 block
   [-1, 1, RepNCSPELAN4, [512, 512, 256, 1]],  # 36

   # avg-conv down fuse
   [-1, 1, ADown, [512]],  # 37-P5/32
   [[27, -1], 1, CBFuse, [[2]]], # 38

   # elan-2 block
   [-1, 1, RepNCSPELAN4, [512, 512, 256, 1]],  # 39
   
   
   
   # detection head

   # detect
   [[33, 36, 39, 18, 21, 24], 1, DualDDetect, [nc]],  # DualDDetect(A3, A4, A5, P3, P4, P5)
  ]

3 运行查看结果

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