YOLOv9改进 |
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YOLOv9改进
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一、本文介绍
大家好，本文给大家带来的是2024年2月21日全新发布的SOTA模型YOLOv9的补全教程（算是一种补全吧我个人认为），了解V7的读者都知道V7系列是不支持模型深度和宽度的修改的也就是没有办法像YOLOv8那样有多个版本（例如：V9n、V9s、V9m、V9l、V9x这样），所以我也是在YOLOv9最新版本的基础上给大家把这个共功能添加上了（修改模型的深度和宽度从而产生达到不同版本的效果），本文的内容均为我个人整理和撰写，创作不易，大家可以帮忙点赞评论支持一下！ 专栏地址：YOLOv9有效涨点专栏-持续复现各种顶会内容-有效涨点-全网改进最全的专栏
目录 一、本文介绍 二、模型获取和训练 2.1 训练教程 2.2 模型下载 三、 添加多版本教程 3.1 修改一 3.2 修改二 3.3 修改三 四、运行对比 4.1 gelan-n 4.1.1 gelan-n.yaml文件 4.1.2 运行截图 4.2 gelan-s 4.2.1 gelan-s.yaml文件 4.2.1 运行截图 4.3 gelan-m 4.3.1 gelan-m.yaml文件 4.3.2 运行截图 4.4 gelan-l 4.4.1 gelan-l.yaml文件 4.4.1 运行截图 4.5 gelan-x 4.5.1 gelan-x.yaml文件 4.5.1 运行截图 五、全文总结 二、模型获取和训练 2.1 训练教程在开始之前大家肯定要学会训练自己的YOLOv9模型（比较特殊）所以我这里给大家总结了教程，大家有需要的可以点击下面的链接进行跳转到我另一篇的文章里（文章里详细介绍了如何训练、验证、导出、推理等功能）。 使用教程：YOLOv9 | 利用yolov9训练自己的数据集 -＞ 推理、验证（源码解读 + 手撕结构图） 2.2 模型下载YOLOv9已经开源在Github上了，地址如下-> 官方论文地址： 官方论文地址点击即可跳转 官方代码地址： 官方代码地址点击即可跳转 三、 添加多版本教程下面给大家添加上教程，修改教程很简单大家看着修改即可。 3.1 修改一首先我们下载完YOLOv9的官方代码之后，我们找到如下的文件'models/yolo.py'文件，在其中找到'def parse_model(d, ch): # model_dict, input_channels(3)'。 下面的代码就是红框内的代码大家复制即可，省的自己可能打错字！ if m in (RepNCSPELAN4,): args[1] = make_divisible(args[1] * gw, 8) args[2] = make_divisible(args[2] * gw, 8) args[3] = max(round(args[3] * gd), 1) if n > 1 else n
同样还是同一个文件我们往下翻一下找到'elif m is CBLinear:'然后按照我给的图片内修改即可，修改后的代码我以及提供了大家复制替换下即可。 c2 = [int(x * gw) for x in args[0]]
修改完上面之后我们可以找到我们的模型配置文件进行修改了，我这里以'models/detect/gelan.yaml'为例。进行修改，
修改后的yaml文件提供给大家！ # YOLOv9 # parameters nc: 80 # number of classes depth_multiple: 0.33 # model depth multiple width_multiple: 0.25 # layer channel multiple #activation: nn.LeakyReLU(0.1) #activation: nn.ReLU() # anchors anchors: 3 # gelan backbone backbone: [ # conv down [-1, 1, Conv, [64, 3, 2]], # 0-P1/2 # conv down [-1, 1, Conv, [128, 3, 2]], # 1-P2/4 # elan-1 block [-1, 1, RepNCSPELAN4, [256, 128, 64, 1]], # 2 # avg-conv down [-1, 1, Conv, [256, 3, 2]], # 3-P3/8 # elan-2 block [-1, 1, RepNCSPELAN4, [512, 256, 128, 1]], # 4 # avg-conv down [-1, 1, Conv, [512, 3, 2]], # 5-P4/16 # elan-2 block [-1, 1, RepNCSPELAN4, [512, 512, 256, 1]], # 6 # avg-conv down [-1, 1, Conv, [512, 3, 2]], # 7-P5/32 # elan-2 block [-1, 1, RepNCSPELAN4, [512, 512, 256, 1]], # 8 ] # gelan head head: [ # elan-spp block [-1, 1, SPPELAN, [512, 256]], # 9 # up-concat merge [-1, 1, nn.Upsample, [None, 2, 'nearest']], [[-1, 6], 1, Concat, [1]], # cat backbone P4 # elan-2 block [-1, 1, RepNCSPELAN4, [512, 512, 256, 1]], # 12 # up-concat merge [-1, 1, nn.Upsample, [None, 2, 'nearest']], [[-1, 4], 1, Concat, [1]], # cat backbone P3 # elan-2 block [-1, 1, RepNCSPELAN4, [256, 256, 128, 1]], # 15 (P3/8-small) # avg-conv-down merge [-1, 1, Conv, [256, 3, 2]], [[-1, 12], 1, Concat, [1]], # cat head P4 # elan-2 block [-1, 1, RepNCSPELAN4, [512, 512, 256, 1]], # 18 (P4/16-medium) # avg-conv-down merge [-1, 1, Conv, [512, 3, 2]], [[-1, 9], 1, Concat, [1]], # cat head P5 # elan-2 block [-1, 1, RepNCSPELAN4, [512, 512, 256, 1]], # 21 (P5/32-large) # detect [[15, 18, 21], 1, DDetect, [nc]], # Detect(P3, P4, P5) ]到此就修改完了，此时我们的模型就支持修改深度和宽度系数了，修改的教程也很简单不知道为什么YOLOv7的作者不提供，所以这里我也是给其做了一个补全，其中修改一的修改方法主要是因为其yaml文件配置的参数都放到了args的位置所以不能直接插入到下面的判断里，所以大家注意不要修改错了！ 四、运行对比上面我们提供了修改的教程，下面提供几个版本的yaml文件和运行截图供大家参考。 4.1 gelan-n 4.1.1 gelan-n.yaml文件下面的两个参数是X版本对应的深度和宽度系数。 depth_multiple: 0.33 # model depth multiple width_multiple: 0.25 # layer channel multiple # YOLOv9 # parameters nc: 80 # number of classes depth_multiple: 0.33 # model depth multiple width_multiple: 0.25 # layer channel multiple #activation: nn.LeakyReLU(0.1) #activation: nn.ReLU() # anchors anchors: 3 # gelan backbone backbone: [ # conv down [-1, 1, Conv, [64, 3, 2]], # 0-P1/2 # conv down [-1, 1, Conv, [128, 3, 2]], # 1-P2/4 # elan-1 block [-1, 1, RepNCSPELAN4, [256, 128, 64, 1]], # 2 # avg-conv down [-1, 1, Conv, [256, 3, 2]], # 3-P3/8 # elan-2 block [-1, 1, RepNCSPELAN4, [512, 256, 128, 1]], # 4 # avg-conv down [-1, 1, Conv, [512, 3, 2]], # 5-P4/16 # elan-2 block [-1, 1, RepNCSPELAN4, [512, 512, 256, 1]], # 6 # avg-conv down [-1, 1, Conv, [512, 3, 2]], # 7-P5/32 # elan-2 block [-1, 1, RepNCSPELAN4, [512, 512, 256, 1]], # 8 ] # gelan head head: [ # elan-spp block [-1, 1, SPPELAN, [512, 256]], # 9 # up-concat merge [-1, 1, nn.Upsample, [None, 2, 'nearest']], [[-1, 6], 1, Concat, [1]], # cat backbone P4 # elan-2 block [-1, 1, RepNCSPELAN4, [512, 512, 256, 1]], # 12 # up-concat merge [-1, 1, nn.Upsample, [None, 2, 'nearest']], [[-1, 4], 1, Concat, [1]], # cat backbone P3 # elan-2 block [-1, 1, RepNCSPELAN4, [256, 256, 128, 1]], # 15 (P3/8-small) # avg-conv-down merge [-1, 1, Conv, [256, 3, 2]], [[-1, 12], 1, Concat, [1]], # cat head P4 # elan-2 block [-1, 1, RepNCSPELAN4, [512, 512, 256, 1]], # 18 (P4/16-medium) # avg-conv-down merge [-1, 1, Conv, [512, 3, 2]], [[-1, 9], 1, Concat, [1]], # cat head P5 # elan-2 block [-1, 1, RepNCSPELAN4, [512, 512, 256, 1]], # 21 (P5/32-large) # detect [[15, 18, 21], 1, DDetect, [nc]], # Detect(P3, P4, P5) ] 4.1.2 运行截图
下面的两个参数是X版本对应的深度和宽度系数。 depth_multiple: 0.33 # model depth multiple width_multiple: 0.50 # layer channel multiple # YOLOv9 # parameters nc: 80 # number of classes depth_multiple: 0.33 # model depth multiple width_multiple: 0.50 # layer channel multiple #activation: nn.LeakyReLU(0.1) #activation: nn.ReLU() # anchors anchors: 3 # gelan backbone backbone: [ # conv down [-1, 1, Conv, [64, 3, 2]], # 0-P1/2 # conv down [-1, 1, Conv, [128, 3, 2]], # 1-P2/4 # elan-1 block [-1, 1, RepNCSPELAN4, [256, 128, 64, 1]], # 2 # avg-conv down [-1, 1, Conv, [256, 3, 2]], # 3-P3/8 # elan-2 block [-1, 1, RepNCSPELAN4, [512, 256, 128, 1]], # 4 # avg-conv down [-1, 1, Conv, [512, 3, 2]], # 5-P4/16 # elan-2 block [-1, 1, RepNCSPELAN4, [512, 512, 256, 1]], # 6 # avg-conv down [-1, 1, Conv, [512, 3, 2]], # 7-P5/32 # elan-2 block [-1, 1, RepNCSPELAN4, [512, 512, 256, 1]], # 8 ] # gelan head head: [ # elan-spp block [-1, 1, SPPELAN, [512, 256]], # 9 # up-concat merge [-1, 1, nn.Upsample, [None, 2, 'nearest']], [[-1, 6], 1, Concat, [1]], # cat backbone P4 # elan-2 block [-1, 1, RepNCSPELAN4, [512, 512, 256, 1]], # 12 # up-concat merge [-1, 1, nn.Upsample, [None, 2, 'nearest']], [[-1, 4], 1, Concat, [1]], # cat backbone P3 # elan-2 block [-1, 1, RepNCSPELAN4, [256, 256, 128, 1]], # 15 (P3/8-small) # avg-conv-down merge [-1, 1, Conv, [256, 3, 2]], [[-1, 12], 1, Concat, [1]], # cat head P4 # elan-2 block [-1, 1, RepNCSPELAN4, [512, 512, 256, 1]], # 18 (P4/16-medium) # avg-conv-down merge [-1, 1, Conv, [512, 3, 2]], [[-1, 9], 1, Concat, [1]], # cat head P5 # elan-2 block [-1, 1, RepNCSPELAN4, [512, 512, 256, 1]], # 21 (P5/32-large) # detect [[15, 18, 21], 1, DDetect, [nc]], # Detect(P3, P4, P5) ] 4.2.1 运行截图
下面的两个参数是X版本对应的深度和宽度系数。 depth_multiple: 0.67 # model depth multiple width_multiple: 0.75 # layer channel multiple # YOLOv5 🚀 by Ultralytics, AGPL-3.0 license # Parameters nc: 80 # number of classes depth_multiple: 0.67 # model depth multiple width_multiple: 0.75 # 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 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 [-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) ] 4.3.2 运行截图
下面的两个参数是X版本对应的深度和宽度系数。 depth_multiple: 1.0 # model depth multiple width_multiple: 1.0 # layer channel multiple # 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 # gelan backbone backbone: [ # conv down [-1, 1, Conv, [64, 3, 2]], # 0-P1/2 # conv down [-1, 1, Conv, [128, 3, 2]], # 1-P2/4 # elan-1 block [-1, 1, RepNCSPELAN4, [256, 128, 64, 1]], # 2 # avg-conv down [-1, 1, Conv, [256, 3, 2]], # 3-P3/8 # elan-2 block [-1, 1, RepNCSPELAN4, [512, 256, 128, 1]], # 4 # avg-conv down [-1, 1, Conv, [512, 3, 2]], # 5-P4/16 # elan-2 block [-1, 1, RepNCSPELAN4, [512, 512, 256, 1]], # 6 # avg-conv down [-1, 1, Conv, [512, 3, 2]], # 7-P5/32 # elan-2 block [-1, 1, RepNCSPELAN4, [512, 512, 256, 1]], # 8 ] # gelan head head: [ # elan-spp block [-1, 1, SPPELAN, [512, 256]], # 9 # up-concat merge [-1, 1, nn.Upsample, [None, 2, 'nearest']], [[-1, 6], 1, Concat, [1]], # cat backbone P4 # elan-2 block [-1, 1, RepNCSPELAN4, [512, 512, 256, 1]], # 12 # up-concat merge [-1, 1, nn.Upsample, [None, 2, 'nearest']], [[-1, 4], 1, Concat, [1]], # cat backbone P3 # elan-2 block [-1, 1, RepNCSPELAN4, [256, 256, 128, 1]], # 15 (P3/8-small) # avg-conv-down merge [-1, 1, Conv, [256, 3, 2]], [[-1, 12], 1, Concat, [1]], # cat head P4 # elan-2 block [-1, 1, RepNCSPELAN4, [512, 512, 256, 1]], # 18 (P4/16-medium) # avg-conv-down merge [-1, 1, Conv, [512, 3, 2]], [[-1, 9], 1, Concat, [1]], # cat head P5 # elan-2 block [-1, 1, RepNCSPELAN4, [512, 512, 256, 1]], # 21 (P5/32-large) # detect [[15, 18, 21], 1, DDetect, [nc]], # Detect(P3, P4, P5) ] 4.4.1 运行截图
下面的两个参数是X版本对应的深度和宽度系数。 depth_multiple: 1.33 # model depth multiple width_multiple: 1.25 # layer channel multiple # YOLOv9 # parameters nc: 80 # number of classes depth_multiple: 1.33 # model depth multiple width_multiple: 1.25 # layer channel multiple #activation: nn.LeakyReLU(0.1) #activation: nn.ReLU() # anchors anchors: 3 # gelan backbone backbone: [ # conv down [-1, 1, Conv, [64, 3, 2]], # 0-P1/2 # conv down [-1, 1, Conv, [128, 3, 2]], # 1-P2/4 # elan-1 block [-1, 1, RepNCSPELAN4, [256, 128, 64, 1]], # 2 # avg-conv down [-1, 1, Conv, [256, 3, 2]], # 3-P3/8 # elan-2 block [-1, 1, RepNCSPELAN4, [512, 256, 128, 1]], # 4 # avg-conv down [-1, 1, Conv, [512, 3, 2]], # 5-P4/16 # elan-2 block [-1, 1, RepNCSPELAN4, [512, 512, 256, 1]], # 6 # avg-conv down [-1, 1, Conv, [512, 3, 2]], # 7-P5/32 # elan-2 block [-1, 1, RepNCSPELAN4, [512, 512, 256, 1]], # 8 ] # gelan head head: [ # elan-spp block [-1, 1, SPPELAN, [512, 256]], # 9 # up-concat merge [-1, 1, nn.Upsample, [None, 2, 'nearest']], [[-1, 6], 1, Concat, [1]], # cat backbone P4 # elan-2 block [-1, 1, RepNCSPELAN4, [512, 512, 256, 1]], # 12 # up-concat merge [-1, 1, nn.Upsample, [None, 2, 'nearest']], [[-1, 4], 1, Concat, [1]], # cat backbone P3 # elan-2 block [-1, 1, RepNCSPELAN4, [256, 256, 128, 1]], # 15 (P3/8-small) # avg-conv-down merge [-1, 1, Conv, [256, 3, 2]], [[-1, 12], 1, Concat, [1]], # cat head P4 # elan-2 block [-1, 1, RepNCSPELAN4, [512, 512, 256, 1]], # 18 (P4/16-medium) # avg-conv-down merge [-1, 1, Conv, [512, 3, 2]], [[-1, 9], 1, Concat, [1]], # cat head P5 # elan-2 block [-1, 1, RepNCSPELAN4, [512, 512, 256, 1]], # 21 (P5/32-large) # detect [[15, 18, 21], 1, DDetect, [nc]], # Detect(P3, P4, P5) ] 4.5.1 运行截图
其他版本的这里就不提供了，主要就是修改其ymal文件中对应的深度和宽度系数即可，我上面对应的每个版本都已经提供了。 五、全文总结到此本文的正式分享内容就结束了，在这里给大家推荐我的YOLOv9改进有效涨点专栏，本专栏目前为新开的，后期我会根据各种最新的前沿顶会进行论文复现，也会对一些老的改进机制进行补充，如果大家觉得本文帮助到你了，订阅本专栏（目前免费订阅，后期不迷路），关注后续更多的更新~ 专栏地址：YOLOv9有效涨点专栏-持续复现各种顶会内容-有效涨点-全网改进最全的专栏 |
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