YOLOV5网络结构

YOLOv5代码注释版更新啦，注释的是最近的2021.07.14的版本，且注释更全
github:https://github.com/Laughing-q/yolov5_annotations

YOLOV5网络结构

粗略地图
yolov5-1.0~4.0
yolov5-5.0
详细图纸
yolov5-3.0
yolov5-4.0
yolov5-5.0

ultralytics\yolov5，v5还在开发当中，目前的网络结构如下图，要是网络结构有更新，笔者也会更新结构图。

2020.7.24：
才画出这个图，作者就更新了v2.0版本，改了网络结构。。。。
所以以下的结构图只适用于yolov5_v1.0版本。
之后笔者再更新v2.0版本的结构图。
2020.7.28：
笔者今天仔细看了一下新的结构yaml配置文件，发现其实网络结构没有变(之前笔者只是大概的看了一下github上的commit记录，看到很多删除和添加，就以为改动很大。。。），他只是把下图中neck部分的第一个BottleneckCSP (1024,1024) x3纳入到了backbone里，然后把output中的卷积Conv2d放到了models/yolo.py/Detect()类里计算，如果更新结构图的话也只是把neck最下面的BottleneckCSP块移到SPP块的下面，所以就暂时不更新结构图了，以下结构图依然适用。
v2.0版本yolov5x mAP有提升，但yolov5s mAP却下降了，目前主要的改变是：训练策略的改变，包括余弦退火的公式更新了，以及类别损失cls_loss的系数gain，对数据进行仿射变换(dataset.py数据增强部分)的超参数进行调整，三个output的损失比重balance的调整。

2020.8.15
yolov5更新了v3.0版本
主要做出的变化是，采用了hardswish激活函数替换CONV(下图右下角模块)模块的LeakyReLu，但是注意：BottleneckCSP模块中的LeakyReLu未被替换，采用了CIOU作为损失函数(但这个更新好像是还在v2.0版本过渡的时候已经更新)，还更改了一个默认超参数:translate=0.5 → 0.1(数据增强的仿射系数)。

2020.8.16
结构图已更新，将上述2020.7.28提到的的BottleneckCSP模块纳入到backbone中，并更新CONV模块的激活函数hardswish.

2020.12.25
有读者发现网络结构图有些细节画错了，BottleneckCSP处最后应该是CONV模块，SPP模块concat通道应该是(c_in*2)，已更新。

2021.01.06
更新yolov5-4.0网络结构，C3结构替换BottleneckCSP。CONV的激活函数换成SiLU，目前发现的其他改变是三个output的损失比重balance的调整balance = [4.0, 1.0, 0.4] → [4.0, 1.0, 0.3]

2021.04.25
更新yolov5-5.0网络结构，增加一层通道数768的特征图 level，spp模块中的池化由[5,9,13]→[3,5,7]。
（直接照着新的yolov5l6.yaml画的，如果有错请指正，谢谢）

下图中括号内的四个数字分别代表：（输入通道、输出通道、卷积核大小、步长）；
两个数字代表：（输入通道，输出通道）；
一个数字代表：（输出通道）；
且上采样是采用nearst插值，两倍上采样；
x N表示堆叠此模块N次。

粗略地图

yolov5-1.0~4.0

yolov5-5.0

详细图纸

yolov5-3.0

v2.0和v1.0就是把下图中CONV模块中hardswish换成leakyrelu即可

yolov5-3.0网络结构图

yolov5-4.0

yolov5-4.0网络结构图

yolov5-5.0

yolov5-5.0网络结构图

至于Focus的部分附上代码帮助理解：

def forward(self, x):  # x(b,c,w,h) -> y(b,4c,w/2,h/2)
    return self.conv(torch.cat([x[..., ::2, ::2], x[..., 1::2, ::2], x[..., ::2, 1::2], x[..., 1::2, 1::2]], 1))

注意：backbone最后一个BottleneckCSP(C3)和neck部分开始的BottleneckCSP(C3)模块便不再使用shortcut残差连接，对应下面配置文件里的False(意为shortcut=False)。
附上yolov5l的配置文件：

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

# anchors
anchors:
  - [116,90, 156,198, 373,326]  # P5/32
  - [30,61, 62,45, 59,119]  # P4/16
  - [10,13, 16,30, 33,23]  # P3/8

# 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, BottleneckCSP, [128]],
   [-1, 1, Conv, [256, 3, 2]],  # 3-P3/8
   [-1, 9, BottleneckCSP, [256]],
   [-1, 1, Conv, [512, 3, 2]],  # 5-P4/16
   [-1, 9, BottleneckCSP, [512]],
   [-1, 1, Conv, [1024, 3, 2]], # 7-P5/32
   [-1, 1, SPP, [1024, [5, 9, 13]]],
  ]

# YOLOv5 head
head:
  [[-1, 3, BottleneckCSP, [1024, False]],  # 9

   [-1, 1, Conv, [512, 1, 1]],
   [-1, 1, nn.Upsample, [None, 2, 'nearest']],
   [[-1, 6], 1, Concat, [1]],  # cat backbone P4
   [-1, 3, BottleneckCSP, [512, False]],  # 13

   [-1, 1, Conv, [256, 1, 1]],
   [-1, 1, nn.Upsample, [None, 2, 'nearest']],
   [[-1, 4], 1, Concat, [1]],  # cat backbone P3
   [-1, 3, BottleneckCSP, [256, False]],
   [-1, 1, nn.Conv2d, [na * (nc + 5), 1, 1]],  # 18 (P3/8-small)

   [-2, 1, Conv, [256, 3, 2]],
   [[-1, 14], 1, Concat, [1]],  # cat head P4
   [-1, 3, BottleneckCSP, [512, False]],
   [-1, 1, nn.Conv2d, [na * (nc + 5), 1, 1]],  # 22 (P4/16-medium)

   [-2, 1, Conv, [512, 3, 2]],
   [[-1, 10], 1, Concat, [1]],  # cat head P5
   [-1, 3, BottleneckCSP, [1024, False]],
   [-1, 1, nn.Conv2d, [na * (nc + 5), 1, 1]],  # 26 (P5/32-large)

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

附上v2.0和v3.0版本的配置文件：

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

# anchors
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, BottleneckCSP, [128]],
   [-1, 1, Conv, [256, 3, 2]],  # 3-P3/8
   [-1, 9, BottleneckCSP, [256]],
   [-1, 1, Conv, [512, 3, 2]],  # 5-P4/16
   [-1, 9, BottleneckCSP, [512]],
   [-1, 1, Conv, [1024, 3, 2]],  # 7-P5/32
   [-1, 1, SPP, [1024, [5, 9, 13]]],
   [-1, 3, BottleneckCSP, [1024, False]],  # 9
  ]

# YOLOv5 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, BottleneckCSP, [512, False]],  # 13

   [-1, 1, Conv, [256, 1, 1]],
   [-1, 1, nn.Upsample, [None, 2, 'nearest']],
   [[-1, 4], 1, Concat, [1]],  # cat backbone P3
   [-1, 3, BottleneckCSP, [256, False]],  # 17

   [-1, 1, Conv, [256, 3, 2]],
   [[-1, 14], 1, Concat, [1]],  # cat head P4
   [-1, 3, BottleneckCSP, [512, False]],  # 20

   [-1, 1, Conv, [512, 3, 2]],
   [[-1, 10], 1, Concat, [1]],  # cat head P5
   [-1, 3, BottleneckCSP, [1024, False]],  # 23

   [[17, 20, 23], 1, Detect, [nc, anchors]],  # Detect(P3, P4, P5)
  ]

v4.0

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

# anchors
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]],
   [-1, 1, Conv, [256, 3, 2]],  # 3-P3/8
   [-1, 9, 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, 1, SPP, [1024, [5, 9, 13]]],
   [-1, 3, C3, [1024, False]],  # 9
  ]

# YOLOv5 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
   [-1, 3, C3, [256, False]],  # 17 (P3/8-small)

   [-1, 1, Conv, [256, 3, 2]],
   [[-1, 14], 1, Concat, [1]],  # cat head P4
   [-1, 3, C3, [512, False]],  # 20 (P4/16-medium)

   [-1, 1, Conv, [512, 3, 2]],
   [[-1, 10], 1, Concat, [1]],  # cat head P5
   [-1, 3, C3, [1024, False]],  # 23 (P5/32-large)

   [[17, 20, 23], 1, Detect, [nc, anchors]],  # Detect(P3, P4, P5)
  ]

v5.0

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

# anchors
anchors:
  - [ 19,27,  44,40,  38,94 ]  # P3/8
  - [ 96,68,  86,152,  180,137 ]  # P4/16
  - [ 140,301,  303,264,  238,542 ]  # P5/32
  - [ 436,615,  739,380,  925,792 ]  # P6/64

# 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 ] ],
    [ -1, 1, Conv, [ 256, 3, 2 ] ],  # 3-P3/8
    [ -1, 9, C3, [ 256 ] ],
    [ -1, 1, Conv, [ 512, 3, 2 ] ],  # 5-P4/16
    [ -1, 9, C3, [ 512 ] ],
    [ -1, 1, Conv, [ 768, 3, 2 ] ],  # 7-P5/32
    [ -1, 3, C3, [ 768 ] ],
    [ -1, 1, Conv, [ 1024, 3, 2 ] ],  # 9-P6/64
    [ -1, 1, SPP, [ 1024, [ 3, 5, 7 ] ] ],
    [ -1, 3, C3, [ 1024, False ] ],  # 11
  ]

# YOLOv5 head
head:
  [ [ -1, 1, Conv, [ 768, 1, 1 ] ],
    [ -1, 1, nn.Upsample, [ None, 2, 'nearest' ] ],
    [ [ -1, 8 ], 1, Concat, [ 1 ] ],  # cat backbone P5
    [ -1, 3, C3, [ 768, False ] ],  # 15

    [ -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 ] ],  # 19

    [ -1, 1, Conv, [ 256, 1, 1 ] ],
    [ -1, 1, nn.Upsample, [ None, 2, 'nearest' ] ],
    [ [ -1, 4 ], 1, Concat, [ 1 ] ],  # cat backbone P3
    [ -1, 3, C3, [ 256, False ] ],  # 23 (P3/8-small)

    [ -1, 1, Conv, [ 256, 3, 2 ] ],
    [ [ -1, 20 ], 1, Concat, [ 1 ] ],  # cat head P4
    [ -1, 3, C3, [ 512, False ] ],  # 26 (P4/16-medium)

    [ -1, 1, Conv, [ 512, 3, 2 ] ],
    [ [ -1, 16 ], 1, Concat, [ 1 ] ],  # cat head P5
    [ -1, 3, C3, [ 768, False ] ],  # 29 (P5/32-large)

    [ -1, 1, Conv, [ 768, 3, 2 ] ],
    [ [ -1, 12 ], 1, Concat, [ 1 ] ],  # cat head P6
    [ -1, 3, C3, [ 1024, False ] ],  # 32 (P6/64-xlarge)

    [ [ 23, 26, 29, 32 ], 1, Detect, [ nc, anchors ] ],  # Detect(P3, P4, P5, P6)
  ]

原文链接：https://blog.csdn.net/Q1u1NG/article/details/107511465

YOLOV5网络结构

YOLOV5网络结构

粗略地图

yolov5-1.0~4.0

yolov5-5.0

详细图纸

yolov5-3.0

yolov5-4.0

yolov5-5.0

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