yolov5从V1.0到V6.2网络变化梳理(yolov5使用教程)

yolov5从V1.0到V6.2网络变化梳理

推荐整理分享yolov5从V1.0到V6.2网络变化梳理(yolov5使用教程)，希望有所帮助，仅作参考，欢迎阅读内容。

文章相关热门搜索词:yolov5版本,yolov5 5.0,yolov5m,yolov5版本,yolov5版本,yolov5版本,yolov5版本,yolov5 5.0,内容如对您有帮助，希望把文章链接给更多的朋友！

最近在学yolov5的网络结构，发现不同的人描述yolov5的网络结构并不同，有的说是C3模块有的说是BottleneckCSP，这给我一个小白带来了很大困扰。查询了很多文章终于在一篇文章中有博主提到，yolov5新版本用C3代替了BottleneckCSP。所以为了搞清楚yolov5的具体网络结构，在这里把所有的版本结构记录下来，以便之后的学习理解与查看。

v1.0版本如下： backbone主要模块：Focus、Conv、BottleneckCSP、SPP head主要模块：BottleneckCSP、Conv、nn.Upsample、Concat、nn.Conv2d

# YOLOv5 backbonebackbone: # [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 headhead: [[-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) ]

v2.0版本如下： backbone主要模块：Focus、Conv、BottleneckCSP、SPP head主要模块：BottleneckCSP、Conv、nn.Upsample、Concat v2.0与V1.0相比最大的区别就是少了nn.Conv2d模块，并且Detect指定为17, 20, 23层输出

# YOLOv5 backbonebackbone: # [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 headhead: [[-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) ]

V3.0与V3.1和V2.0相同，这里不放代码；

从V4.0版本开始，作者开始用C3代替了BottleneckCSP，而其他的结构不变。C3结构作用基本相同均为BottleneckCSP架构，只是在修正单元的选择上有所不同，其包含了3个标准卷积层以及多个Bottleneck模块（数量由配置文件.yaml的n和depth_multiple参数乘积决定）

# YOLOv5 backbonebackbone: # [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 headhead: [[-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) ]

一张图快速了解C3与BottleneckCSP区别(从别人那扒的，嘘）: V5.0版本也没有改变网络结构，这里不放代码；

v6.0版本将第0层的Focus替换成Conv，将SPP替换成SPPF； ps：SPPF比SPP快了一倍时间yolov5 github上有代码验证，这里只放结果。

# YOLOv5 v6.0 backbonebackbone: # [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 headhead: [[-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) ]

V6.1和V6.2版本与V6.0相同，这里不放代码；

总结： V1.0版本backbone主要模块：Focus、Conv、BottleneckCSP、SPP；head主要模块：BottleneckCSP、Conv、nn.Upsample、Concat、nn.Conv2d。 V2.0版本在V1.0版本基础上删去nn.Conv2d，并且Detect指定为17, 20, 23层输出。 V4.0版本用C3代替了BottleneckCSP，而其他的结构不变。 V6.0版本将第0层的Focus替换成Conv，将SPP替换成SPPF；

最后放一张V6.1版本网络结构图：（来自github官方）