Yolov5s/Yolov8s网络结构图

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已于 2023-09-11 11:59:44 修改 · 9.4k 阅读

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#YOLO

于 2023-02-01 08:48:21 首次发布

文章详细介绍了YOLOv5s和YOLOv8s两种目标检测模型的网络配置，包括参数设置、模型结构和检测层的实现。YOLOv5s和YOLOv8s在backbone和head部分有所不同，涉及卷积、上采样、拼接等操作，用于不同尺度特征的融合和预测。

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一、网络模型配置

Yolov5s

# Parameters
nc: 1  # number of classes
depth_multiple: 0.33  # model depth multiple
width_multiple: 0.50  # layer channel multiple
anchors:
  - [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 backbone
backbone:
  # [from, number, module, args]
  [[-1, 1, Focus, [64, 3]],          # 0-P1/2
   [-1, 1, Conv, [128, 3, 2]],       # 1-P2/4
   [-1, 3, C3, [128]],               # 2
   [-1, 1, Conv, [256, 3, 2]],       # 3-P3/8
   [-1, 9, C3, [256]],               # 4
   [-1, 1, Conv, [512, 3, 2]],       # 5-P4/16
   [-1, 9, C3, [512]],               # 6
   [-1, 1, Conv, [1024, 3, 2]],      # 7-P5/32
   [-1, 1, SPP, [1024, [5, 9, 13]]], # 8
   [-1, 3, C3, [1024, False]],       # 9
  ]

# YOLOv5 head
head:
  [[-1, 1, Conv, [512, 1, 1]],                 # 10
   [-1, 1, nn.Upsample, [None, 2, 'nearest']], # 11
   [[-1, 6], 1, Concat, [1]],                  # 12 cat backbone P4
   [-1, 3, C3, [512, False]],                  # 13

   [-1, 1, Conv, [256, 1, 1]],                 # 14
   [-1, 1, nn.Upsample, [None, 2, 'nearest']], # 15
   [[-1, 4], 1, Concat, [1]],                  # 16 cat backbone P3
   [-1, 3, C3, [256, False]],                  # 17 (P3/8-small)

   [-1, 1, Conv, [256, 3, 2]],                 # 18
   [[-1, 14], 1, Concat, [1]],                 # 19 cat head P4
   [-1, 3, C3, [512, False]]

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