YOLOv5 四检测头配置

一、网络结构说明

Yolov5原网络结构如下：

增加一层检测层后，网络结构如下：（其中虚线表示删除的部分，细线表示增加的数据流动方向）

二、网络配置

第一步，在models文件夹下面创建yolov5s-add-one-layer.yaml文件。

第二步，将下面的内容粘贴到新创建的文件中。

# YOLOv5 by Ultralytics, GPL-3.0 license

# Parameters
nc: 2  # number of classes
depth_multiple: 0.33  # model depth multiple
width_multiple: 0.50  # layer channel multiple
anchors:
  - [4,5, 8,10, 22,18] # P2/4
  - [10,13, 16,30, 33,23]  # P3/8
  - [30,61, 62,45, 59,119]  # P4/16
  - [116,90, 156,198, 373,326]  # P5/32

# 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, [1024, 3, 2]],  # 7-P5/32
   [-1, 3, C3, [1024]],
   [-1, 1, SPPF, [1024, 5]],  # 9
  ]

# YOLOv5 v6.0 head
head:
  [[-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]],  # 13

   [-1, 1, Conv, [256, 1, 1]],
   [-1, 1, nn.Upsample, [None, 2, 'nearest']],
   [[-1, 4], 1, Concat, [1]],  # cat backbone P3

   # add feature extration layer 
   [-1, 3, C3, [256, False]],  # 17
   [-1, 1, Conv, [128, 1, 1]],
   [-1, 1, nn.Upsample, [None, 2, 'nearest']],
   [[-1, 2], 1, Concat, [1]],  # cat backbone P3

   # add detect layer
   [-1, 3, C3, [128, False]],  # 21 (P4/4-minium)

   [-1, 1, Conv, [128, 3, 2]],
   [[-1, 18], 1, Concat, [1]],  # cat head P3
   # end

   [-1, 3, C3, [256, False]],  # 24 (P3/8-small)

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

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

   [[21, 24, 27, 30], 1, Detect, [nc, anchors]],  # Detect(P2, P3, P4, P5)
  ]

第三步，正常执行新模型的训练流程，参考：快速使用YOLOv5进行训练VOC格式的数据集 - jupiter's blog (inat.top)。

参考资料

1. 【Yolov5】Yolov5添加检测层，四层结构对小目标、密集场景更友好_yolov5增加小目标检测层-CSDN博客