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时间:2025/7/9 8:01:58来源:https://blog.csdn.net/m0_67647321/article/details/142831234 浏览次数:0次
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秋招面试专栏推荐 :深度学习算法工程师面试问题总结【百面算法工程师】——点击即可跳转


💡💡💡本专栏所有程序均经过测试,可成功执行💡💡💡


本文介绍了一个空间组增强(SGE)模块,它可以为每个语义组中每个空间位置生成一个注意力因子,从而调整每个子特征的重要性,以便每个组可以自主地增强其学习的表达并抑制可能的噪声。文章在介绍主要的原理后,将手把手教学如何进行模块的代码添加和修改,并将修改后的完整代码放在文章的最后,方便大家一键运行,小白也可轻松上手实践。以帮助您更好地学习深度学习目标检测YOLO系列的挑战。

专栏地址:YOLO11入门 + 改进涨点——点击即可跳转 欢迎订阅

目录

1. 论文

2. 将SGE添加到YOLO11中

2.1 代码实现

2.2 更改init.py文件

2.3 新增yaml文件

2.4 注册模块

2.5 执行程序

3.修改后的网络结构图

4. 完整代码分享

5. GFLOPs

6. 进阶

7.总结


1. 论文

论文地址:Spatial Group-wise Enhance: Improving Semantic Feature Learning in Convolutional Networks——点击即可跳转

官方代码:官方代码仓库——点击即可跳转

2. 将SGE添加到YOLO11中

2.1 代码实现

关键步骤一:将下面代码粘贴到在/ultralytics/ultralytics/nn/modules/block.py中

from torch.nn import initclass SpatialGroupEnhance(nn.Module):def __init__(self, groups=8):super().__init__()self.groups = groupsself.avg_pool = nn.AdaptiveAvgPool2d(1)self.weight = nn.Parameter(torch.zeros(1, groups, 1, 1))self.bias = nn.Parameter(torch.zeros(1, groups, 1, 1))self.sig = nn.Sigmoid()self.init_weights()def init_weights(self):for m in self.modules():if isinstance(m, nn.Conv2d):init.kaiming_normal_(m.weight, mode='fan_out')if m.bias is not None:init.constant_(m.bias, 0)elif isinstance(m, nn.BatchNorm2d):init.constant_(m.weight, 1)init.constant_(m.bias, 0)elif isinstance(m, nn.Linear):init.normal_(m.weight, std=0.001)if m.bias is not None:init.constant_(m.bias, 0)def forward(self, x):b, c, h, w = x.shapex = x.view(b * self.groups, -1, h, w)  # bs*g,dim//g,h,wxn = x * self.avg_pool(x)  # bs*g,dim//g,h,wxn = xn.sum(dim=1, keepdim=True)  # bs*g,1,h,wt = xn.view(b * self.groups, -1)  # bs*g,h*wt = t - t.mean(dim=1, keepdim=True)  # bs*g,h*wstd = t.std(dim=1, keepdim=True) + 1e-5t = t / std  # bs*g,h*wt = t.view(b, self.groups, h, w)  # bs,g,h*wt = t * self.weight + self.bias  # bs,g,h*wt = t.view(b * self.groups, 1, h, w)  # bs*g,1,h*wx = x * self.sig(t)x = x.view(b, c, h, w)return x

2.2 更改init.py文件

关键步骤二:修改modules文件夹下的__init__.py文件,先导入函数

然后在下面的__all__中声明函数

2.3 新增yaml文件

关键步骤三:在 \ultralytics\ultralytics\cfg\models\11下新建文件 yolo11_SGE.yaml并将下面代码复制进去

  • 目标检测
# Ultralytics YOLO 🚀, AGPL-3.0 license
# YOLO11 object detection model with P3-P5 outputs. For Usage examples see https://docs.ultralytics.com/tasks/detect# Parameters
nc: 80 # number of classes
scales: # model compound scaling constants, i.e. 'model=yolo11n.yaml' will call yolo11.yaml with scale 'n'# [depth, width, max_channels]n: [0.50, 0.25, 1024] # summary: 319 layers, 2624080 parameters, 2624064 gradients, 6.6 GFLOPss: [0.50, 0.50, 1024] # summary: 319 layers, 9458752 parameters, 9458736 gradients, 21.7 GFLOPsm: [0.50, 1.00, 512] # summary: 409 layers, 20114688 parameters, 20114672 gradients, 68.5 GFLOPsl: [1.00, 1.00, 512] # summary: 631 layers, 25372160 parameters, 25372144 gradients, 87.6 GFLOPsx: [1.00, 1.50, 512] # summary: 631 layers, 56966176 parameters, 56966160 gradients, 196.0 GFLOPs# YOLO11n backbone
backbone:# [from, repeats, module, args]- [-1, 1, Conv, [64, 3, 2]] # 0-P1/2- [-1, 1, Conv, [128, 3, 2]] # 1-P2/4- [-1, 2, C3k2, [256, False, 0.25]]- [-1, 1, Conv, [256, 3, 2]] # 3-P3/8- [-1, 2, C3k2, [512, False, 0.25]]- [-1, 1, Conv, [512, 3, 2]] # 5-P4/16- [-1, 2, C3k2, [512, True]]- [-1, 1, Conv, [1024, 3, 2]] # 7-P5/32- [-1, 2, C3k2, [1024, True]]- [-1, 1, SPPF, [1024, 5]] # 9- [-1, 2, C2PSA, [1024]] # 10# YOLO11n head
head:- [-1, 1, nn.Upsample, [None, 2, "nearest"]]- [[-1, 6], 1, Concat, [1]] # cat backbone P4- [-1, 2, C3k2, [512, False]] # 13- [-1, 1, nn.Upsample, [None, 2, "nearest"]]- [[-1, 4], 1, Concat, [1]] # cat backbone P3- [-1, 2, C3k2, [256, False]] # 16 (P3/8-small)- [-1, 1, Conv, [256, 3, 2]]- [[-1, 13], 1, Concat, [1]] # cat head P4- [-1, 2, C3k2, [512, False]] # 19 (P4/16-medium)- [-1, 1, Conv, [512, 3, 2]]- [[-1, 10], 1, Concat, [1]] # cat head P5- [-1, 2, C3k2, [1024, True]] # 22 (P5/32-large)- [-1, 1, SpatialGroupEnhance, [1024]]- [[16, 19, 23], 1, Detect, [nc]] # Detect(P3, P4, P5)

  • 语义分割
# Ultralytics YOLO 🚀, AGPL-3.0 license
# YOLO11 object detection model with P3-P5 outputs. For Usage examples see https://docs.ultralytics.com/tasks/detect# Parameters
nc: 80 # number of classes
scales: # model compound scaling constants, i.e. 'model=yolo11n.yaml' will call yolo11.yaml with scale 'n'# [depth, width, max_channels]n: [0.50, 0.25, 1024] # summary: 319 layers, 2624080 parameters, 2624064 gradients, 6.6 GFLOPss: [0.50, 0.50, 1024] # summary: 319 layers, 9458752 parameters, 9458736 gradients, 21.7 GFLOPsm: [0.50, 1.00, 512] # summary: 409 layers, 20114688 parameters, 20114672 gradients, 68.5 GFLOPsl: [1.00, 1.00, 512] # summary: 631 layers, 25372160 parameters, 25372144 gradients, 87.6 GFLOPsx: [1.00, 1.50, 512] # summary: 631 layers, 56966176 parameters, 56966160 gradients, 196.0 GFLOPs# YOLO11n backbone
backbone:# [from, repeats, module, args]- [-1, 1, Conv, [64, 3, 2]] # 0-P1/2- [-1, 1, Conv, [128, 3, 2]] # 1-P2/4- [-1, 2, C3k2, [256, False, 0.25]]- [-1, 1, Conv, [256, 3, 2]] # 3-P3/8- [-1, 2, C3k2, [512, False, 0.25]]- [-1, 1, Conv, [512, 3, 2]] # 5-P4/16- [-1, 2, C3k2, [512, True]]- [-1, 1, Conv, [1024, 3, 2]] # 7-P5/32- [-1, 2, C3k2, [1024, True]]- [-1, 1, SPPF, [1024, 5]] # 9- [-1, 2, C2PSA, [1024]] # 10# YOLO11n head
head:- [-1, 1, nn.Upsample, [None, 2, "nearest"]]- [[-1, 6], 1, Concat, [1]] # cat backbone P4- [-1, 2, C3k2, [512, False]] # 13- [-1, 1, nn.Upsample, [None, 2, "nearest"]]- [[-1, 4], 1, Concat, [1]] # cat backbone P3- [-1, 2, C3k2, [256, False]] # 16 (P3/8-small)- [-1, 1, Conv, [256, 3, 2]]- [[-1, 13], 1, Concat, [1]] # cat head P4- [-1, 2, C3k2, [512, False]] # 19 (P4/16-medium)- [-1, 1, Conv, [512, 3, 2]]- [[-1, 10], 1, Concat, [1]] # cat head P5- [-1, 2, C3k2, [1024, True]] # 22 (P5/32-large)- [-1, 1, SpatialGroupEnhance, [1024]]- [[16, 19, 23], 1, Segment, [nc, 32, 256]] # Segment(P3, P4, P5)
  • 旋转目标检测 
# Ultralytics YOLO 🚀, AGPL-3.0 license
# YOLO11 object detection model with P3-P5 outputs. For Usage examples see https://docs.ultralytics.com/tasks/detect# Parameters
nc: 80 # number of classes
scales: # model compound scaling constants, i.e. 'model=yolo11n.yaml' will call yolo11.yaml with scale 'n'# [depth, width, max_channels]n: [0.50, 0.25, 1024] # summary: 319 layers, 2624080 parameters, 2624064 gradients, 6.6 GFLOPss: [0.50, 0.50, 1024] # summary: 319 layers, 9458752 parameters, 9458736 gradients, 21.7 GFLOPsm: [0.50, 1.00, 512] # summary: 409 layers, 20114688 parameters, 20114672 gradients, 68.5 GFLOPsl: [1.00, 1.00, 512] # summary: 631 layers, 25372160 parameters, 25372144 gradients, 87.6 GFLOPsx: [1.00, 1.50, 512] # summary: 631 layers, 56966176 parameters, 56966160 gradients, 196.0 GFLOPs# YOLO11n backbone
backbone:# [from, repeats, module, args]- [-1, 1, Conv, [64, 3, 2]] # 0-P1/2- [-1, 1, Conv, [128, 3, 2]] # 1-P2/4- [-1, 2, C3k2, [256, False, 0.25]]- [-1, 1, Conv, [256, 3, 2]] # 3-P3/8- [-1, 2, C3k2, [512, False, 0.25]]- [-1, 1, Conv, [512, 3, 2]] # 5-P4/16- [-1, 2, C3k2, [512, True]]- [-1, 1, Conv, [1024, 3, 2]] # 7-P5/32- [-1, 2, C3k2, [1024, True]]- [-1, 1, SPPF, [1024, 5]] # 9- [-1, 2, C2PSA, [1024]] # 10# YOLO11n head
head:- [-1, 1, nn.Upsample, [None, 2, "nearest"]]- [[-1, 6], 1, Concat, [1]] # cat backbone P4- [-1, 2, C3k2, [512, False]] # 13- [-1, 1, nn.Upsample, [None, 2, "nearest"]]- [[-1, 4], 1, Concat, [1]] # cat backbone P3- [-1, 2, C3k2, [256, False]] # 16 (P3/8-small)- [-1, 1, Conv, [256, 3, 2]]- [[-1, 13], 1, Concat, [1]] # cat head P4- [-1, 2, C3k2, [512, False]] # 19 (P4/16-medium)- [-1, 1, Conv, [512, 3, 2]]- [[-1, 10], 1, Concat, [1]] # cat head P5- [-1, 2, C3k2, [1024, True]] # 22 (P5/32-large)- [-1, 1, SpatialGroupEnhance, [1024]]- [[16, 19, 23], 1, OBB, [nc, 1]] # Detect(P3, P4, P5)

温馨提示:本文只是对yolo11基础上添加模块,如果要对yolo11n/l/m/x进行添加则只需要指定对应的depth_multiple 和 width_multiple。 


# YOLO11n
depth_multiple: 0.50  # model depth multiple
width_multiple: 0.25  # layer channel multiple
max_channel:1024# YOLO11s
depth_multiple: 0.50  # model depth multiple
width_multiple: 0.50  # layer channel multiple
max_channel:1024# YOLO11m
depth_multiple: 0.50  # model depth multiple
width_multiple: 1.00  # layer channel multiple
max_channel:512# YOLO11l 
depth_multiple: 1.00  # model depth multiple
width_multiple: 1.00  # layer channel multiple
max_channel:512 # YOLO11x
depth_multiple: 1.00  # model depth multiple
width_multiple: 1.50 # layer channel multiple
max_channel:512

2.4 注册模块

关键步骤四:在parse_model函数中进行注册,在parse_model函数中添加下面内容

先在task.py导入函数

然后在task.py文件下找到parse_model这个函数,如下图,添加 SpatialGroupEnhance

elif m is SpatialGroupEnhance:c1, c2 = ch[f], args[0]if c2 != nc:  # if c2 not equal to number of classes (i.e. for Classify() output)c2 = make_divisible(min(c2, max_channels) * width, 8)args = [c1, *args[1:]]

2.5 执行程序

在train.py中,将model的参数路径设置为yolo11_SGE.yaml的路径

建议大家写绝对路径,确保一定能找到

from ultralytics import YOLO
import warnings
warnings.filterwarnings('ignore')
from pathlib import Pathif __name__ == '__main__':# 加载模型model = YOLO("ultralytics/cfg/11/yolo11.yaml")  # 你要选择的模型yaml文件地址# Use the modelresults = model.train(data=r"你的数据集的yaml文件地址",epochs=100, batch=16, imgsz=640, workers=4, name=Path(model.cfg).stem)  # 训练模型

🚀运行程序,如果出现下面的内容则说明添加成功🚀 

                   from  n    params  module                                       arguments0                  -1  1       464  ultralytics.nn.modules.conv.Conv             [3, 16, 3, 2]1                  -1  1      4672  ultralytics.nn.modules.conv.Conv             [16, 32, 3, 2]2                  -1  1      6640  ultralytics.nn.modules.block.C3k2            [32, 64, 1, False, 0.25]      3                  -1  1     36992  ultralytics.nn.modules.conv.Conv             [64, 64, 3, 2]4                  -1  1     26080  ultralytics.nn.modules.block.C3k2            [64, 128, 1, False, 0.25]     5                  -1  1    147712  ultralytics.nn.modules.conv.Conv             [128, 128, 3, 2]6                  -1  1     87040  ultralytics.nn.modules.block.C3k2            [128, 128, 1, True]7                  -1  1    295424  ultralytics.nn.modules.conv.Conv             [128, 256, 3, 2]8                  -1  1    346112  ultralytics.nn.modules.block.C3k2            [256, 256, 1, True]9                  -1  1    164608  ultralytics.nn.modules.block.SPPF            [256, 256, 5]10                  -1  1    249728  ultralytics.nn.modules.block.C2PSA           [256, 256, 1]11                  -1  1         0  torch.nn.modules.upsampling.Upsample         [None, 2, 'nearest']12             [-1, 6]  1         0  ultralytics.nn.modules.conv.Concat           [1]13                  -1  1    111296  ultralytics.nn.modules.block.C3k2            [384, 128, 1, False]14                  -1  1         0  torch.nn.modules.upsampling.Upsample         [None, 2, 'nearest']15             [-1, 4]  1         0  ultralytics.nn.modules.conv.Concat           [1]16                  -1  1     32096  ultralytics.nn.modules.block.C3k2            [256, 64, 1, False]17                  -1  1     36992  ultralytics.nn.modules.conv.Conv             [64, 64, 3, 2]18            [-1, 13]  1         0  ultralytics.nn.modules.conv.Concat           [1]19                  -1  1     86720  ultralytics.nn.modules.block.C3k2            [192, 128, 1, False]20                  -1  1    147712  ultralytics.nn.modules.conv.Conv             [128, 128, 3, 2]21            [-1, 10]  1         0  ultralytics.nn.modules.conv.Concat           [1]22                  -1  1    378880  ultralytics.nn.modules.block.C3k2            [384, 256, 1, True]23                  -1  1       512  ultralytics.nn.modules.block.SpatialGroupEnhance[256]24        [16, 19, 22]  1    464912  ultralytics.nn.modules.head.Detect           [80, [64, 128, 256]]
YOLO11_SpatialGroupEnhance summary: 322 layers, 2,624,592 parameters, 2,624,576 gradients, 6.6 GFLOPs

3.修改后的网络结构图

4. 完整代码分享

这个后期补充吧~,先按照步骤来即可

5. GFLOPs

关于GFLOPs的计算方式可以查看百面算法工程师 | 卷积基础知识——Convolution

未改进的YOLO11n GFLOPs

改进后的GFLOPs

6. 进阶

可以与其他的注意力机制或者损失函数等结合,进一步提升检测效果

7.总结

通过以上的改进方法,我们成功提升了模型的表现。这只是一个开始,未来还有更多优化和技术深挖的空间。在这里,我想隆重向大家推荐我的专栏——《YOLO11改进有效涨点》。这个专栏专注于前沿的深度学习技术,特别是目标检测领域的最新进展,不仅包含对YOLO11的深入解析和改进策略,还会定期更新来自各大顶会(如CVPR、NeurIPS等)的论文复现和实战分享。

为什么订阅我的专栏? ——《YOLO11改进有效涨点》

  1. 前沿技术解读:专栏不仅限于YOLO系列的改进,还会涵盖各类主流与新兴网络的最新研究成果,帮助你紧跟技术潮流。

  2. 详尽的实践分享:所有内容实践性也极强。每次更新都会附带代码和具体的改进步骤,保证每位读者都能迅速上手。

  3. 问题互动与答疑:订阅我的专栏后,你将可以随时向我提问,获取及时的答疑

  4. 实时更新,紧跟行业动态:不定期发布来自全球顶会的最新研究方向和复现实验报告,让你时刻走在技术前沿。

专栏适合人群:

  • 对目标检测、YOLO系列网络有深厚兴趣的同学

  • 希望在用YOLO算法写论文的同学

  • 对YOLO算法感兴趣的同学等

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