当前位置： 首页 > news >正文

YOLO11改进 | Neck | 有效提升小目标检测效果，附完整代码结构图【论文必备】

news 2024/11/2 0:56:35

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💡💡💡

本文给大家带来的教程是将YOLO11的卷积替换为一种轻量化的卷积结构来提取特征。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. 代码实现

2.1 将代码添加到YOLO11中

2.2 更改init.py文件

2.3 添加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：Slim-neck by GSConv: A lightweight-design for real-time detector architectures——点击即可跳转

官方代码：官方代码仓库——点击即可跳转 

2. 代码实现

2.1 将代码添加到YOLO11中

关键步骤一：在/ultralytics/ultralytics/nn/modules/下新建Slim-Neck.py，并粘贴下面代码

import torch
import torch.nn as nn
import mathdef autopad(k, p=None, d=1):"""Pads kernel to 'same' output shape, adjusting for optional dilation; returns padding size.`k`: kernel, `p`: padding, `d`: dilation."""if d > 1:k = d * (k - 1) + 1 if isinstance(k, int) else [d * (x - 1) + 1 for x in k]  # actual kernel-sizeif p is None:p = k // 2 if isinstance(k, int) else [x // 2 for x in k]  # auto-padreturn pclass Conv(nn.Module):# Standard convolution with args(ch_in, ch_out, kernel, stride, padding, groups, dilation, activation)default_act = nn.SiLU()  # default activationdef __init__(self, c1, c2, k=1, s=1, p=None, g=1, d=1, act=True):"""Initializes a standard convolution layer with optional batch normalization and activation."""super().__init__()self.conv = nn.Conv2d(c1, c2, k, s, autopad(k, p, d), groups=g, dilation=d, bias=False)self.bn = nn.BatchNorm2d(c2)self.act = self.default_act if act is True else act if isinstance(act, nn.Module) else nn.Identity()def forward(self, x):"""Applies a convolution followed by batch normalization and an activation function to the input tensor `x`."""return self.act(self.bn(self.conv(x)))def forward_fuse(self, x):"""Applies a fused convolution and activation function to the input tensor `x`."""return self.act(self.conv(x))class DWConv(Conv):"""Depth-wise convolution."""def __init__(self, c1, c2, k=1, s=1, d=1, act=True):  # ch_in, ch_out, kernel, stride, dilation, activation"""Initialize Depth-wise convolution with given parameters."""super().__init__(c1, c2, k, s, g=math.gcd(c1, c2), d=d, act=act)class GSConv(nn.Module):# GSConv https://github.com/AlanLi1997/slim-neck-by-gsconvdef __init__(self, c1, c2, k=1, s=1, p=None, g=1, d=1, act=True):super().__init__()c_ = c2 // 2self.cv1 = Conv(c1, c_, k, s, p, g, d, Conv.default_act)self.cv2 = Conv(c_, c_, 5, 1, p, c_, d, Conv.default_act)def forward(self, x):x1 = self.cv1(x)x2 = torch.cat((x1, self.cv2(x1)), 1)# shuffle# y = x2.reshape(x2.shape[0], 2, x2.shape[1] // 2, x2.shape[2], x2.shape[3])# y = y.permute(0, 2, 1, 3, 4)# return y.reshape(y.shape[0], -1, y.shape[3], y.shape[4])b, n, h, w = x2.size()b_n = b * n // 2y = x2.reshape(b_n, 2, h * w)y = y.permute(1, 0, 2)y = y.reshape(2, -1, n // 2, h, w)return torch.cat((y[0], y[1]), 1)class GSConvns(GSConv):# GSConv with a normative-shuffle https://github.com/AlanLi1997/slim-neck-by-gsconvdef __init__(self, c1, c2, k=1, s=1, p=None, g=1, act=True):super().__init__(c1, c2, k, s, p, g, act=True)c_ = c2 // 2self.shuf = nn.Conv2d(c_ * 2, c2, 1, 1, 0, bias=False)def forward(self, x):x1 = self.cv1(x)x2 = torch.cat((x1, self.cv2(x1)), 1)# normative-shuffle, TRT supportedreturn nn.ReLU()(self.shuf(x2))class GSBottleneck(nn.Module):# GS Bottleneck https://github.com/AlanLi1997/slim-neck-by-gsconvdef __init__(self, c1, c2, k=3, s=1, e=0.5):super().__init__()c_ = int(c2*e)# for lightingself.conv_lighting = nn.Sequential(GSConv(c1, c_, 1, 1),GSConv(c_, c2, 3, 1, act=False))self.shortcut = Conv(c1, c2, 1, 1, act=False)def forward(self, x):return self.conv_lighting(x) + self.shortcut(x)class GSBottleneckns(GSBottleneck):# GS Bottleneck https://github.com/AlanLi1997/slim-neck-by-gsconvdef __init__(self, c1, c2, k=3, s=1, e=0.5):super().__init__(c1, c2, k, s, e)c_ = int(c2*e)# for lightingself.conv_lighting = nn.Sequential(GSConvns(c1, c_, 1, 1),GSConvns(c_, c2, 3, 1, act=False))class GSBottleneckC(GSBottleneck):# cheap GS Bottleneck https://github.com/AlanLi1997/slim-neck-by-gsconvdef __init__(self, c1, c2, k=3, s=1):super().__init__(c1, c2, k, s)self.shortcut = DWConv(c1, c2, k, s, act=False)class VoVGSCSP(nn.Module):# VoVGSCSP module with GSBottleneckdef __init__(self, c1, c2, n=1, shortcut=True, g=1, e=0.5):super().__init__()c_ = int(c2 * e)  # hidden channelsself.cv1 = Conv(c1, c_, 1, 1)self.cv2 = Conv(c1, c_, 1, 1)self.gsb = nn.Sequential(*(GSBottleneck(c_, c_, e=1.0) for _ in range(n)))self.res = Conv(c_, c_, 3, 1, act=False)self.cv3 = Conv(2 * c_, c2, 1)def forward(self, x):x1 = self.gsb(self.cv1(x))y = self.cv2(x)return self.cv3(torch.cat((y, x1), dim=1))class VoVGSCSPns(VoVGSCSP):def __init__(self, c1, c2, n=1, shortcut=True, g=1, e=0.5):super().__init__(c1, c2, n, shortcut, g, e)c_ = int(c2 * e)  # hidden channelsself.gsb = nn.Sequential(*(GSBottleneckns(c_, c_, e=1.0) for _ in range(n)))class VoVGSCSPC(VoVGSCSP):# cheap VoVGSCSP module with GSBottleneckdef __init__(self, c1, c2, n=1, shortcut=True, g=1, e=0.5):super().__init__(c1, c2)c_ = int(c2 * 0.5)  # hidden channelsself.gsb = GSBottleneckC(c_, c_, 1, 1)

​

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 添加yaml文件

关键步骤三：在/ultralytics/ultralytics/cfg/models/11下面新建文件yolo11_slim-neck.yaml文件，粘贴下面的内容

  • 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, GSConv, [64, 3, 2]] # 0-P1/2- [-1, 1, GSConv, [128, 3, 2]] # 1-P2/4- [-1, 2, C3k2, [256, False, 0.25]]- [-1, 1, GSConv, [256, 3, 2]] # 3-P3/8- [-1, 2, C3k2, [512, False, 0.25]]- [-1, 1, GSConv, [512, 3, 2]] # 5-P4/16- [-1, 2, C3k2, [512, True]]- [-1, 1, GSConv, [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, VoVGSCSP, [512, False]] # 13- [-1, 1, nn.Upsample, [None, 2, "nearest"]]- [[-1, 4], 1, Concat, [1]] # cat backbone P3- [-1, 2, VoVGSCSP, [256, False]] # 16 (P3/8-small)- [-1, 1, GSConv, [256, 3, 2]]- [[-1, 13], 1, Concat, [1]] # cat head P4- [-1, 2, VoVGSCSP, [512, False]] # 19 (P4/16-medium)- [-1, 1, GSConv, [512, 3, 2]]- [[-1, 10], 1, Concat, [1]] # cat head P5- [-1, 2, VoVGSCSP, [1024, True]] # 22 (P5/32-large)- [[16, 19, 22], 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, GSConv, [64, 3, 2]] # 0-P1/2- [-1, 1, GSConv, [128, 3, 2]] # 1-P2/4- [-1, 2, C3k2, [256, False, 0.25]]- [-1, 1, GSConv, [256, 3, 2]] # 3-P3/8- [-1, 2, C3k2, [512, False, 0.25]]- [-1, 1, GSConv, [512, 3, 2]] # 5-P4/16- [-1, 2, C3k2, [512, True]]- [-1, 1, GSConv, [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, VoVGSCSP, [512, False]] # 13- [-1, 1, nn.Upsample, [None, 2, "nearest"]]- [[-1, 4], 1, Concat, [1]] # cat backbone P3- [-1, 2, VoVGSCSP, [256, False]] # 16 (P3/8-small)- [-1, 1, GSConv, [256, 3, 2]]- [[-1, 13], 1, Concat, [1]] # cat head P4- [-1, 2, VoVGSCSP, [512, False]] # 19 (P4/16-medium)- [-1, 1, GSConv, [512, 3, 2]]- [[-1, 10], 1, Concat, [1]] # cat head P5- [-1, 2, VoVGSCSP, [1024, True]] # 22 (P5/32-large)- [[16, 19, 22], 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, GSConv, [64, 3, 2]] # 0-P1/2- [-1, 1, GSConv, [128, 3, 2]] # 1-P2/4- [-1, 2, C3k2, [256, False, 0.25]]- [-1, 1, GSConv, [256, 3, 2]] # 3-P3/8- [-1, 2, C3k2, [512, False, 0.25]]- [-1, 1, GSConv, [512, 3, 2]] # 5-P4/16- [-1, 2, C3k2, [512, True]]- [-1, 1, GSConv, [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, VoVGSCSP, [512, False]] # 13- [-1, 1, nn.Upsample, [None, 2, "nearest"]]- [[-1, 4], 1, Concat, [1]] # cat backbone P3- [-1, 2, VoVGSCSP, [256, False]] # 16 (P3/8-small)- [-1, 1, GSConv, [256, 3, 2]]- [[-1, 13], 1, Concat, [1]] # cat head P4- [-1, 2, VoVGSCSP, [512, False]] # 19 (P4/16-medium)- [-1, 1, GSConv, [512, 3, 2]]- [[-1, 10], 1, Concat, [1]] # cat head P5- [-1, 2, VoVGSCSP, [1024, True]] # 22 (P5/32-large)- [[16, 19, 22], 1, OBB, [nc, 1]] # OBB(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task.py的parse_model函数添加 GSConv, VoVGSCSP, VoVGSCSPC,

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GSConv, VoVGSCSP, VoVGSCSPC,

 还是这个函数，靠下面几行 添加

2.5 执行程序

关键步骤五: 在ultralytics文件中新建train.py，将model的参数路径设置为yolo11_slim-neck.yaml的路径即可 【注意是在外边的Ultralytics下新建train.py】

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       448  ultralytics.nn.modules.models.slim_neck.GSConv        [3, 16, 3, 2]1                  -1  1      2768  ultralytics.nn.modules.models.slim_neck.GSConv        [16, 32, 3, 2]2                  -1  1      6640  ultralytics.nn.modules.block.C3k2                     [32, 64, 1, False, 0.25]3                  -1  1     19360  ultralytics.nn.modules.models.slim_neck.GSConv        [64, 64, 3, 2]4                  -1  1     26080  ultralytics.nn.modules.block.C3k2                     [64, 128, 1, False, 0.25]5                  -1  1     75584  ultralytics.nn.modules.models.slim_neck.GSConv        [128, 128, 3, 2]6                  -1  1     87040  ultralytics.nn.modules.block.C3k2                     [128, 128, 1, True]7                  -1  1    151168  ultralytics.nn.modules.models.slim_neck.GSConv        [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    129600  ultralytics.nn.modules.models.slim_neck.VoVGSCSP      [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     37152  ultralytics.nn.modules.models.slim_neck.VoVGSCSP      [256, 64, 1, False]17                  -1  1     19360  ultralytics.nn.modules.models.slim_neck.GSConv        [64, 64, 3, 2]18            [-1, 13]  1         0  ultralytics.nn.modules.conv.Concat                    [1]19                  -1  1    105024  ultralytics.nn.modules.models.slim_neck.VoVGSCSP      [192, 128, 1, False]20                  -1  1     75584  ultralytics.nn.modules.models.slim_neck.GSConv        [128, 128, 3, 2]21            [-1, 10]  1         0  ultralytics.nn.modules.conv.Concat                    [1]22                  -1  1    414848  ultralytics.nn.modules.models.slim_neck.VoVGSCSP      [384, 256, 1, True]23        [16, 19, 22]  1    464912  ultralytics.nn.modules.head.Detect                    [80, [64, 128, 256]]
YOLO11_slim_neck summary: 410 layers, 2,376,016 parameters, 2,376,000 gradients, 5.6 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——<专栏地址：YOLO11入门 + 改进涨点——点击即可跳转 欢迎订阅>。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？ ——专栏地址：YOLO11入门 + 改进涨点——点击即可跳转 欢迎订阅

  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

​


http://www.mrgr.cn/news/63696.html

相关文章：

  • linux 原子操作
  • ERROR: Failed cleaning build dir for numpy Failed ＞ to build numpy ERROR
  • 计算机四级嵌入式·操作系统知识点总结（一）
  • 在MySQL中存储IP地址的最佳实践
  • 区块链-C++挖矿软件XMRIG源码分析
  • 夯实根基之MySql从入门到精通（一）
  • C++之多态（上）
  • 推荐一款音乐制作软件：Ableton Live Suite
  • 不可能的任务：这款浏览器竟然可以同时满足速度与隐私
  • 深入解析 Transformers 框架（三）：Qwen2.5 大模型的 AutoTokenizer 技术细节
  • HJ38 求小球落地5次后所经历的路程和第5次反弹的高度
  • ArcGIS影像调色（三原色）三原色调整
  • 基于SpringBoot和PostGIS的世界各国邻国可视化实践
  • buu easyRE
  • pandas计算相关性并画热力图
  • 文件描述符fd 和 缓冲区
  • javaScript-----一维数组和数组对象去重的多种方法
  • 【数据结构】guard
  • 【英特尔IA-32架构软件开发者开发手册第3卷：系统编程指南】2001年版翻译，2-10
  • 虚拟机 Ubuntu 扩容
  • 二十八、Python基础语法（面向对象-下）
  • C. Gorilla and Permutation
  • 打响反对人工智能的第一枪
  • 多处理机调度（李昂学长视频总结）25新增考点
  • 音视频入门基础：FLV专题（22）——FFmpeg源码中，获取FLV文件音频信息的实现（中）
  • 奇瑞不客气智驾 晚不晚？