YOLO11改进 | 注意力机制 | 添加双重注意力机制 DoubleAttention【附代码+小白必备】
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在本文中,给大家带来的教程是在原来的网络的基础上添加DoubleAttention。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。
目录
2.1 将DoubleAttention添加到YOLO11中
1.论文
官方论文:A2 -Nets: Double Attention Networks——点击即可跳转
官方代码:A2 -Nets: Double Attention Networks官方代码仓库——点击即可跳转
2. DoubleAttention代码实现
2.1 将DoubleAttention添加到YOLO11中
关键步骤一:将下面代码粘贴到在/ultralytics/ultralytics/nn/modules/block.py中
from torch import nn
import torch
from torch.autograd import Variable
import torch.nn.functional as F
class DoubleAttentionLayer(nn.Module):
"""
Implementation of Double Attention Network. NIPS 2018
"""
def __init__(self, in_channels: int, c_m: int, c_n: int, reconstruct=False):
"""
Parameters
----------
in_channels
c_m
c_n
reconstruct: `bool` whether to re-construct output to have shape (B, in_channels, L, R)
"""
super(DoubleAttentionLayer, self).__init__()
self.c_m = c_m
self.c_n = c_n
self.in_channels = in_channels
self.reconstruct = reconstruct
self.convA = nn.Conv2d(in_channels, c_m, kernel_size=1)
self.convB = nn.Conv2d(in_channels, c_n, kernel_size=1)
self.convV = nn.Conv2d(in_channels, c_n, kernel_size=1)
if self.reconstruct:
self.conv_reconstruct = nn.Conv2d(c_m, in_channels, kernel_size=1)
def forward(self, x: torch.Tensor):
"""
Parameters
----------
x: `torch.Tensor` of shape (B, C, H, W)
Returns
-------
"""
batch_size, c, h, w = x.size()
assert c == self.in_channels, 'input channel not equal!'
A = self.convA(x) # (B, c_m, h, w) because kernel size is 1
B = self.convB(x) # (B, c_n, h, w)
V = self.convV(x) # (B, c_n, h, w)
tmpA = A.view(batch_size, self.c_m, h * w)
attention_maps = B.view(batch_size, self.c_n, h * w)
attention_vectors = V.view(batch_size, self.c_n, h * w)
# softmax on the last dimension to create attention maps
attention_maps = F.softmax(attention_maps, dim=-1) # 对hxw维度进行softmax
# step 1: feature gathering
global_descriptors = torch.bmm( # attention map(V)和tmpA进行
tmpA, attention_maps.permute(0, 2, 1)) # (B, c_m, c_n)
# step 2: feature distribution
# (B, c_n, h * w) attention on c_n dimension - channel wise
attention_vectors = F.softmax(attention_vectors, dim=1)
tmpZ = global_descriptors.matmul(
attention_vectors) # B, self.c_m, h * w
tmpZ = tmpZ.view(batch_size, self.c_m, h, w)
if self.reconstruct:
tmpZ = self.conv_reconstruct(tmpZ)
return tmpZ
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_DoubleAttention.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 GFLOPs
s: [0.50, 0.50, 1024] # summary: 319 layers, 9458752 parameters, 9458736 gradients, 21.7 GFLOPs
m: [0.50, 1.00, 512] # summary: 409 layers, 20114688 parameters, 20114672 gradients, 68.5 GFLOPs
l: [1.00, 1.00, 512] # summary: 631 layers, 25372160 parameters, 25372144 gradients, 87.6 GFLOPs
x: [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, DoubleAttentionLayer, [1024, 1]]
- [-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, 14], 1, Concat, [1]] # cat head P4
- [-1, 2, C3k2, [512, False]] # 19 (P4/16-medium)
- [-1, 1, Conv, [512, 3, 2]]
- [[-1, 11], 1, Concat, [1]] # cat head P5
- [-1, 2, C3k2, [1024, True]] # 22 (P5/32-large)
- [[17, 20, 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 GFLOPs
s: [0.50, 0.50, 1024] # summary: 319 layers, 9458752 parameters, 9458736 gradients, 21.7 GFLOPs
m: [0.50, 1.00, 512] # summary: 409 layers, 20114688 parameters, 20114672 gradients, 68.5 GFLOPs
l: [1.00, 1.00, 512] # summary: 631 layers, 25372160 parameters, 25372144 gradients, 87.6 GFLOPs
x: [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, DoubleAttentionLayer, [1024, 1]]
- [-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, 14], 1, Concat, [1]] # cat head P4
- [-1, 2, C3k2, [512, False]] # 19 (P4/16-medium)
- [-1, 1, Conv, [512, 3, 2]]
- [[-1, 11], 1, Concat, [1]] # cat head P5
- [-1, 2, C3k2, [1024, True]] # 22 (P5/32-large)
- [[17, 20, 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 GFLOPs
s: [0.50, 0.50, 1024] # summary: 319 layers, 9458752 parameters, 9458736 gradients, 21.7 GFLOPs
m: [0.50, 1.00, 512] # summary: 409 layers, 20114688 parameters, 20114672 gradients, 68.5 GFLOPs
l: [1.00, 1.00, 512] # summary: 631 layers, 25372160 parameters, 25372144 gradients, 87.6 GFLOPs
x: [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, DoubleAttentionLayer, [1024, 1]]
- [-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, 14], 1, Concat, [1]] # cat head P4
- [-1, 2, C3k2, [512, False]] # 19 (P4/16-medium)
- [-1, 1, Conv, [512, 3, 2]]
- [[-1, 11], 1, Concat, [1]] # cat head P5
- [-1, 2, C3k2, [1024, True]] # 22 (P5/32-large)
- [[17, 20, 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 在task.py中进行注册
关键步骤四:在parse_model函数中进行注册,添加DoubleAttention
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DoubleAttention
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_DoubleAttention.yaml的路径即可
from ultralytics import YOLO
import warnings
warnings.filterwarnings('ignore')
from pathlib import Path
if __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 model
results = 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 arguments
0 -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 66306 ultralytics.nn.modules.block.DoubleAttentionLayer[256, 256, 1]
10 -1 1 164608 ultralytics.nn.modules.block.SPPF [256, 256, 5]
11 -1 1 249728 ultralytics.nn.modules.block.C2PSA [256, 256, 1]
12 -1 1 0 torch.nn.modules.upsampling.Upsample [None, 2, 'nearest']
13 [-1, 6] 1 0 ultralytics.nn.modules.conv.Concat [1]
14 -1 1 111296 ultralytics.nn.modules.block.C3k2 [384, 128, 1, False]
15 -1 1 0 torch.nn.modules.upsampling.Upsample [None, 2, 'nearest']
16 [-1, 4] 1 0 ultralytics.nn.modules.conv.Concat [1]
17 -1 1 32096 ultralytics.nn.modules.block.C3k2 [256, 64, 1, False]
18 -1 1 36992 ultralytics.nn.modules.conv.Conv [64, 64, 3, 2]
19 [-1, 14] 1 0 ultralytics.nn.modules.conv.Concat [1]
20 -1 1 86720 ultralytics.nn.modules.block.C3k2 [192, 128, 1, False]
21 -1 1 147712 ultralytics.nn.modules.conv.Conv [128, 128, 3, 2]
22 [-1, 11] 1 0 ultralytics.nn.modules.conv.Concat [1]
23 -1 1 378880 ultralytics.nn.modules.block.C3k2 [384, 256, 1, True]
24 [17, 20, 23] 1 464912 ultralytics.nn.modules.head.Detect [80, [64, 128, 256]]
YOLO11_DoubleAttention summary: 323 layers, 2,690,386 parameters, 2,690,370 gradients, 6.7 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
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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。
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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。
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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。
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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:
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对目标检测、YOLO系列网络有深厚兴趣的同学
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希望在用YOLO算法写论文的同学
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对YOLO算法感兴趣的同学等
原文地址:https://blog.csdn.net/m0_67647321/article/details/143061978
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