简述网站开发工作流程,在线设计平台有什么用,尊云服务器,北京建设数字网站前言#xff1a;Hello大家好#xff0c;我是小哥谈。与传统的注意力机制相比#xff0c;多尺度注意力机制引入了多个尺度的注意力权重#xff0c;让模型能够更好地理解和处理复杂数据。这种机制通过在不同尺度上捕捉输入数据的特征#xff0c;让模型同时关注局部细节和全局… 前言Hello大家好我是小哥谈。与传统的注意力机制相比多尺度注意力机制引入了多个尺度的注意力权重让模型能够更好地理解和处理复杂数据。这种机制通过在不同尺度上捕捉输入数据的特征让模型同时关注局部细节和全局结构以提高对细节和上下文信息的理解达到提升模型的表达能力、泛化性、鲁棒性和定位精度优化资源使用效率的效果。 目录
1.基础概念
2.网络结构
3.添加步骤
4.改进方法
步骤1创建EMA.py文件
步骤2修改tasks.py文件
步骤3创建自定义yaml文件
步骤4新建train.py文件 1.基础概念
这篇论文的作者旨在通过提出一种高效多尺度注意力模块 (Efficient Multi-Scale Attention Module, EMA) 来改进现有的注意力机制。他们的核心思想是在不进行通道降维的情况下通过分组和多尺度并行子网络来有效地捕捉全局和局部的空间依赖关系。此外他们通过跨空间学习方法将全局与局部特征进行融合以提高像素级的配对关系捕捉能力增强模型对复杂视觉任务如图像分类和目标检测的表现同时保持较低的计算开销。这种设计不仅提高了模型的准确性还提升了其计算效率。
创新点 多尺度并行子网络设计论文提出了多尺度的并行子网络结构用于捕捉图像中的短程和长程依赖关系。这个设计使得模型可以在多个尺度上学习更丰富的特征表示。 通道维度的重组与传统的通道降维方式不同该方法通过将部分通道重组到批次维度中避免了通道降维带来的信息损失从而保持每个通道的完整信息。 跨空间学习方法创新性地提出了跨空间学习方法用来融合并行子网络的输出。该方法通过捕捉像素级的成对关系强化了全局上下文信息的表达有助于提升特征的聚合效果。 高效注意力机制与CBAM、坐标注意力等其他注意力机制相比EMA模块在使用较少参数的情况下显著提高了图像分类和目标检测任务的性能并降低了计算开销。
整体结构
EMA模型通过将输入特征按通道维度分组并采用两个并行分支一个分支使用1D全局池化和1×1卷积处理全局信息另一个分支使用3×3卷积捕捉局部空间信息。两分支的输出通过矩阵乘法融合生成注意力图并与输入特征结合最终提升模型对全局和局部信息的捕捉能力同时降低计算复杂度。 论文题目《Efficient Multi-Scale Attention Module with Cross-Spatial Learning》 论文地址 https://arxiv.org/abs/2305.13563v2 代码实现 https://github.com/YOLOonMe/EMA-attention-module 2.网络结构
本文的改进是基于YOLO11关于其网络结构具体如下图所示 本文所做的改进是在YOLO11的网络结构中加入EMA注意力机制。关于改进后的网络结构图具体如下图所示 3.添加步骤
针对本文的改进具体步骤如下所示
步骤1创建EMA.py新文件
步骤2修改tasks.py文件
步骤3创建自定义yaml文件
步骤4新建train.py文件 4.改进方法
步骤1创建EMA.py文件
在目录ultralytics/nn/modules文件下创建EMA.py文件该文件代码如下
import torch
from torch import nn
# By CSDN 小哥谈class EMA_attention(nn.Module):def __init__(self, channels, factor8):super(EMA_attention, self).__init__()self.groups factorassert channels // self.groups 0self.softmax nn.Softmax(-1)self.agp nn.AdaptiveAvgPool2d((1, 1))self.pool_h nn.AdaptiveAvgPool2d((None, 1))self.pool_w nn.AdaptiveAvgPool2d((1, None))self.gn nn.GroupNorm(channels // self.groups, channels // self.groups)self.conv1x1 nn.Conv2d(channels // self.groups, channels // self.groups, kernel_size1, stride1, padding0)self.conv3x3 nn.Conv2d(channels // self.groups, channels // self.groups, kernel_size3, stride1, padding1)def forward(self, x):b, c, h, w x.size()group_x x.reshape(b * self.groups, -1, h, w) # b*g,c//g,h,wx_h self.pool_h(group_x)x_w self.pool_w(group_x).permute(0, 1, 3, 2)hw self.conv1x1(torch.cat([x_h, x_w], dim2))x_h, x_w torch.split(hw, [h, w], dim2)x1 self.gn(group_x * x_h.sigmoid() * x_w.permute(0, 1, 3, 2).sigmoid())x2 self.conv3x3(group_x)x11 self.softmax(self.agp(x1).reshape(b * self.groups, -1, 1).permute(0, 2, 1))x12 x2.reshape(b * self.groups, c // self.groups, -1) # b*g, c//g, hwx21 self.softmax(self.agp(x2).reshape(b * self.groups, -1, 1).permute(0, 2, 1))x22 x1.reshape(b * self.groups, c // self.groups, -1) # b*g, c//g, hwweights (torch.matmul(x11, x12) torch.matmul(x21, x22)).reshape(b * self.groups, 1, h, w)return (group_x * weights.sigmoid()).reshape(b, c, h, w)
步骤2修改tasks.py文件
首先找到parse_model函数935行左右在下图所示位置加入EMA_attention。
关于所加位置如下图所示 然后在该文件头部导入代码
from ultralytics.nn.modules.EMA import EMA_attention
步骤3创建自定义yaml文件
在目录ultralytics/cfg/models/11下创建yolo11_EMA.yaml文件该文件代码如下
# Ultralytics YOLO , AGPL-3.0 license
# YOLO11 object detection model with P3-P5 outputs. For Usage examples see https://docs.ultralytics.com/tasks/detect
# By CSDN 小哥谈# Parameters
nc: 80 # number of classes
scales: # model compound scaling constants, i.e. modelyolo11n.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)- [16, 1, EMA_attention, [256]] # 23- [19, 1, EMA_attention, [512]] # 24- [22, 1, EMA_attention, [1024]] # 25- [[23, 24, 25], 1, Detect, [nc]] # Detect(P3, P4, P5)
步骤4新建train.py文件
在根目录下新建train.py文件该文件代码如下
# -*- coding: utf-8 -*-
# By CSDN 小哥谈
import warnings
import os
os.environ[KMP_DUPLICATE_LIB_OK]TRUE
warnings.filterwarnings(ignore)
from ultralytics import YOLOif __name__ __main__:# model.load(yolo11n.pt) model YOLO(modelrC:\Users\Lenovo\PycharmProjects\ultralytics-main\ultralytics\cfg\models\11\yolo11_EMA.yaml)model.train(datarC:\Users\Lenovo\PycharmProjects\ultralytics-main\ultralytics\cfg\datasets\helmet.yaml,imgsz640,epochs50,batch4,workers0,device,optimizerSGD,close_mosaic10,resumeFalse,projectruns/train,nameexp,single_clsFalse,cacheFalse,)
点击“运行”代码可以正常运行。 关于其他添加位置
添加位置1
# Ultralytics YOLO , AGPL-3.0 license
# YOLO11 object detection model with P3-P5 outputs. For Usage examples see https://docs.ultralytics.com/tasks/detect
# By CSDN 小哥谈# Parameters
nc: 80 # number of classes
scales: # model compound scaling constants, i.e. modelyolo11n.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- [-1, 1, EMA_attention, [1024]] # 11# 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) 添加位置2
# Ultralytics YOLO , AGPL-3.0 license
# YOLO11 object detection model with P3-P5 outputs. For Usage examples see https://docs.ultralytics.com/tasks/detect
# By CSDN 小哥谈# Parameters
nc: 80 # number of classes
scales: # model compound scaling constants, i.e. modelyolo11n.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, EMA_attention, [256]] # 17- [-1, 1, Conv, [256, 3, 2]] # 18- [[-1, 13], 1, Concat, [1]] # cat head P4- [-1, 2, C3k2, [512, False]] # 20 (P4/16-medium)- [-1, 1, EMA_attention, [512]] # 21- [-1, 1, Conv, [512, 3, 2]] #22- [[-1, 10], 1, Concat, [1]] # cat head P5- [-1, 2, C3k2, [1024, True]] # 24 (P5/32-large)- [-1, 1, EMA_attention, [1024]] # 25- [[17, 21, 25], 1, Detect, [nc]] # Detect(P3, P4, P5)
