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YOLOv5是一种流行的计算机视觉算法，用于实时目标检测和识别任务。激活函数在神经网络中起着重要的作用，它们引入了非线性性质，帮助网络模型学习复杂的特征表示。在YOLOv5中，选择合适的激活函数对于提高模型的性能和准确性至关重要。
本文将讨论改进YOLOv5的激活函数选择。我们将介绍SiLU、ReLU、ELU、Hardswish、Mish、Softplus和AconC系列等常用激活函数，并提供相应的源代码示例。
import torch
import torch.nn as nn
class SiLU(nn.ModuleYOLOv5的Tricks | 【Trick1】关于激活函数Activation的改进汇总
如有错误，恳请指出。在yolov5模型搭建的过程实现中，额外实现了很多非常新奇有趣的激活函数，不再是的简单nn.ReLU等，所以这里使用这篇笔记来对这一些列的激活函数进行总结归纳。关于激活函数，在很早之前我写过一篇笔记介绍，笔记链接：激活函数与Loss函数求导，在这里主要介绍的是目标检测yolov5算法中实现的一些的激活函数，主要设计ReLU函数及其变体，包含ReLU，PReLU，RReLU，FReLU，Swish，Mish，Acon系列，Dynamic ReLU系列。ReLU相比之前的 Sigmoid/t
YOLOv5改进系列（13）——更换激活函数之SiLU，ReLU，ELU，Hardswish，Mish，Softplus，AconC系列等
手把手教你学会YOLOv5改进系列之如何更换激活函数，内含SiLU，ReLU，ELU，Hardswish，Mish，Softplus，AconC系列等详细讲解及改进方法！
YOLOv5改进系列(二) 本文(1.万字) | 解读各种激活函数 | 更换激活函数 | SiLU | ReLU | ELU | Hardswish | Mish | Softplus | 等 |
在接触到深度学习（Deep Learning）后，特别是神经网络中，我们会发现在每一层的神经网络输出后都会使用一个函数（比如sigmoid，tanh，Relu等等）对结果进行运算，这个函数就是激活函数（Activation Function）。那么为什么需要添加激活函数呢？如果不添加又会产生什么问题呢？首先，我们知道神经网络模拟了人类神经元的工作机理，**激活函数（Activation Function）是一种添加到人工神经网络中的函数，旨在帮助网络学习数据中的复杂模式。**
YOLOv5基础知识入门（6）— 激活函数（Mish、Sigmoid、Tanh、ReLU、Softmax、SiLU等）
YOLOv5训练过程中使用了激活函数，可以改善模型的训练速度和准确性，本节课就给大家介绍一下激活函数的概念以及常见的激活函数！
YOLOv5算法改进（18）— 手把手教你去更换激活函数（SiLU/ReLU/ELU/Hardswish/Mish/Softplus）
在YOLOv5中，使用了不同的激活函数。本节课就简单介绍一下常见的激活函数并重点讲解如何去更换激活函数！
YOLOv8改进 | 激活函数 | 十余种常见的激活函数一键替换【完整代码】
yolov8改进，激活函数，yolov8
YOLOv5/v7 更换激活函数
教你YOLOv5/v7更换激活函数
SRCNN超分辨率Pytorch实现，代码逐行讲解，附源码
超分辨率，就是把低分辨率(LR, Low Resolution)图片放大为高分辨率(HR, High Resolution)的过程。通过CNN将图像Y 的特征提取出来存到向量中。用一层的CNN以及ReLU去将图像Y 变成一堆堆向量，即feature map。把提取到的特征进一步做非线性映射，加大网络深度，提高网络复杂性。结合了前面得到的补丁来产生最终的高分辨率图像。实验步骤。
PointMLP实战：5分钟用PyTorch搭建点云分类模型（附GitHub代码）
本文提供了基于ICLR 2022论文PointMLP的点云分类模型实战教程。通过PyTorch框架，详细介绍了从环境搭建、数据预处理、模型架构解析到训练推理的全流程，并分享了性能调优技巧。该模型利用残差MLP模块构建，结构清晰高效，适合开发者快速上手并应用于实际点云处理项目。
erdas监督分类（二）
去云 用一张没有云的影像替代有云影像 构建aoi图层 将云和云影编码合并 重编码工作 在监督分类做的差不多以后（纠正无误，无明显错误），就要对监督分类图像进行重编码和颜色调整。 在中选中Recode,对原来分类的地物类别进行重编码，（为彻底修改，记得备份）选中一列，在New Value输入新的编码，依次编码后点击应用apply，即可实现。 这时的图像如果是全黑的或者只有一两类的颜色也没有关系，新的编码覆盖掉了原来的编码，但是颜色并没有改变才会这样。可以点击图像，右击...
Centos7 XFS（dm-0）:Internal error XFS_WANT_CORRUPTED_GOTO
在k8s的道路上我们都是小白，每天启动虚机都会遇到各种各样的问题，这不。部署的k8s虚机启动发现操作系统启动异常，提示如下报错信息。进入/dev/mapper目录下，查询目录下路径信息。可以通过对文件系统进行修复操作，操作如下。重启完成后虚机恢复正常登录。
【Bug已解决】Context window exceeded / Token limit reached — Claude Code 上下文窗口超限解决方案
Claude Code 长会话中报上下文窗口/Token 超限，本质是对话历史、文件内容、工具结果累积超过 200K 上限。文中给出 /clear、/compact、分批读大文件、限制工具输出、配置 autoCompact 等完整方案。
01 - 什么是SVM
摘要：SVM（共享虚拟内存）技术使CPU和GPU能共享同一虚拟地址空间，简化异构计算编程。传统模型需手动管理CPU/GPU内存和拷贝数据，而SVM允许直接传递指针，自动处理页面迁移和一致性维护。其核心包括统一地址空间、按需迁移、页面故障处理和一致性管理，显著降低编程复杂度并提升性能。通过HMM框架、MMU通知和GPU页面异常等机制实现高效内存共享，支持复杂数据结构，为异构计算带来便利。
A2A 协议详解：Agent Card 的设计与实现
Agent Card 是 Agent2Agent (A2A) 协议中实现智能体发现与互操作性的核心组件。本文将深入剖析 Agent Card 的设计理念、关键字段及其在智能体生态系统中的作用。我们将通过详细的 JSON Schema 解析和 Python SDK 示例，帮助您理解如何精确地定义和发布智能体能力，为构建可发现、可协作的 AI 智能体奠定基础。当您使用a2a.Agent类初始化一个智能体时，SDK 会根据您提供的参数（nameversion等）自动构建内部的 Agent Card 表示。
如何通过Davinci Configurator来新增一个BswM仲裁规则
详细介绍如何通过Davinci Configurator来新增一个BswM仲裁规则。
假人有限元模型
假人模型，车辆碰撞假人有限元模型，适合做乘员安全的研究者利用
深度学习工具包 DeepLearningToolbox
深度学习工具包Deprecation notice.-----This toolbox is outdated and no longer maintained.There are much better tools available for deep learning than this toolbox, e.g. [Theano](http://deeplearning.net/software/theano/), [torch](http://torch.ch/) or [tensorflow](http://www.tensorflow.org/)I would suggest you use one of the tools mentioned above rather than use this toolbox.Best, Rasmus.DeepLearnToolbox================A Matlab toolbox for Deep Learning.Deep Learning is a new subfield of machine learning that focuses on learning deep hierarchical models of data.It is inspired by the human brain's apparent deep (layered, hierarchical) architecture.A good overview of the theory of Deep Learning theory is[Learning Deep Architectures for AI](http://www.iro.umontreal.ca/~bengioy/papers/ftml_book.pdf)For a more informal introduction, see the following videos by Geoffrey Hinton and Andrew Ng.* [The Next Generation of Neural Networks](http://www.youtube.com/watch?v=AyzOUbkUf3M) (Hinton, 2007)* [Recent Developments in Deep Learning](http://www.youtube.com/watch?v=VdIURAu1-aU) (Hinton, 2010)* [Unsupervised Feature Learning and Deep Learning](http://www.youtube.com/watch?v=ZmNOAtZIgIk) (Ng, 2011)If you use this toolbox in your research please cite [Prediction as a candidate for learning deep hierarchical models of data](http://www2.imm.dtu.dk/pubdb/views/publication_details.php?id=6284)```@MASTERSTHESIS\{IMM2012-06284, author = "R. B. Palm", title = "Prediction as a candidate for learning deep hierarchical models of data", year = "2012",}```Contact: rasmusbergpalm at gmail dot comDirectories included in the toolbox-----------------------------------`NN/` - A library for Feedforward Backpropagation Neural Networks`CNN/` - A library for Convolutional Neural Networks`DBN/` - A library for Deep Belief Networks`SAE/` - A library for Stacked Auto-Encoders`CAE/` - A library for Convolutional Auto-Encoders`util/` - Utility functions used by the libraries`data/` - Data used by the examples`tests/` - unit tests to verify toolbox is workingFor references on each library check REFS.mdSetup-----1. Download.2. addpath(genpath('DeepLearnToolbox'));Example: Deep Belief Network---------------------```matlabfunction test_example_DBNload mnist_uint8;train_x = double(train_x) / 255;test_x = double(test_x) / 255;train_y = double(train_y);test_y = double(test_y);%% ex1 train a 100 hidden unit RBM and visualize its weightsrand('state',0)dbn.sizes = [100];opts.numepochs = 1;opts.batchsize = 100;opts.momentum = 0;opts.alpha = 1;dbn = dbnsetup(dbn, train_x, opts);dbn = dbntrain(dbn, train_x, opts);figure; visualize(dbn.rbm{1}.W'); % Visualize the RBM weights%% ex2 train a 100-100 hidden unit DBN and use its weights to initialize a NNrand('state',0)%train dbndbn.sizes = [100 100];opts.numepochs = 1;opts.batchsize = 100;opts.momentum = 0;opts.alpha = 1;dbn = dbnsetup(dbn, train_x, opts);dbn = dbntrain(dbn, train_x, opts);%unfold dbn to nnnn = dbnunfoldtonn(dbn, 10);nn.activation_function = 'sigm';%train nnopts.numepochs = 1;opts.batchsize = 100;nn = nntrain(nn, train_x, train_y, opts);[er, bad] = nntest(nn, test_x, test_y);assert(er < 0.10, 'Too big error');```Example: Stacked Auto-Encoders---------------------```matlabfunction test_example_SAEload mnist_uint8;train_x = double(train_x)/255;test_x = double(test_x)/255;train_y = double(train_y);test_y = double(test_y);%% ex1 train a 100 hidden unit SDAE and use it to initialize a FFNN% Setup and train a stacked denoising autoencoder (SDAE)rand('state',0)sae = saesetup([784 100]);sae.ae{1}.activation_function = 'sigm';sae.ae{1}.learningRate = 1;sae.ae{1}.inputZeroMaskedFraction = 0.5;opts.numepochs = 1;opts.batchsize = 100;sae = saetrain(sae, train_x, opts);visualize(sae.ae{1}.W{1}(:,2:end)')% Use the SDAE to initialize a FFNNnn = nnsetup([784 100 10]);nn.activation_function = 'sigm';nn.learningRate = 1;nn.W{1} = sae.ae{1}.W{1};% Train the FFNNopts.numepochs = 1;opts.batchsize = 100;nn = nntrain(nn, train_x, train_y, opts);[er, bad] = nntest(nn, test_x, test_y);assert(er < 0.16, 'Too big error');```Example: Convolutional Neural Nets---------------------```matlabfunction test_example_CNNload mnist_uint8;train_x = double(reshape(train_x',28,28,60000))/255;test_x = double(reshape(test_x',28,28,10000))/255;train_y = double(train_y');test_y = double(test_y');%% ex1 Train a 6c-2s-12c-2s Convolutional neural network %will run 1 epoch in about 200 second and get around 11% error. %With 100 epochs you'll get around 1.2% errorrand('state',0)cnn.layers = { struct('type', 'i') %input layer struct('type', 'c', 'outputmaps', 6, 'kernelsize', 5) %convolution layer struct('type', 's', 'scale', 2) %sub sampling layer struct('type', 'c', 'outputmaps', 12, 'kernelsize', 5) %convolution layer struct('type', 's', 'scale', 2) %subsampling layer};cnn = cnnsetup(cnn, train_x, train_y);opts.alpha = 1;opts.batchsize = 50;opts.numepochs = 1;cnn = cnntrain(cnn, train_x, train_y, opts);[er, bad] = cnntest(cnn, test_x, test_y);%plot mean squared errorfigure; plot(cnn.rL);assert(er<0.12, 'Too big error');```Example: Neural Networks---------------------```matlabfunction test_example_NNload mnist_uint8;train_x = double(train_x) / 255;test_x = double(test_x) / 255;train_y = double(train_y);test_y = double(test_y);% normalize[train_x, mu, sigma] = zscore(train_x);test_x = normalize(test_x, mu, sigma);%% ex1 vanilla neural netrand('state',0)nn = nnsetup([784 100 10]);opts.numepochs = 1; % Number of full sweeps through dataopts.batchsize = 100; % Take a mean gradient step over this many samples[nn, L] = nntrain(nn, train_x, train_y, opts);[er, bad] = nntest(nn, test_x, test_y);assert(er < 0.08, 'Too big error');%% ex2 neural net with L2 weight decayrand('state',0)nn = nnsetup([784 100 10]);nn.weightPenaltyL2 = 1e-4; % L2 weight decayopts.numepochs = 1; % Number of full sweeps through dataopts.batchsize = 100; % Take a mean gradient step over this many samplesnn = nntrain(nn, train_x, train_y, opts);[er, bad] = nntest(nn, test_x, test_y);assert(er < 0.1, 'Too big error');%% ex3 neural net with dropoutrand('state',0)nn = nnsetup([784 100 10]);nn.dropoutFraction = 0.5; % Dropout fraction opts.numepochs = 1; % Number of full sweeps through dataopts.batchsize = 100; % Take a mean gradient step over this many samplesnn = nntrain(nn, train_x, train_y, opts);[er, bad] = nntest(nn, test_x, test_y);assert(er < 0.1, 'Too big error');%% ex4 neural net with sigmoid activation functionrand('state',0)nn = nnsetup([784 100 10]);nn.activation_function = 'sigm'; % Sigmoid activation functionnn.learningRate = 1; % Sigm require a lower learning rateopts.numepochs = 1; % Number of full sweeps through dataopts.batchsize = 100; % Take a mean gradient step over this many samplesnn = nntrain(nn, train_x, train_y, opts);[er, bad] = nntest(nn, test_x, test_y);assert(er < 0.1, 'Too big error');%% ex5 plotting functionalityrand('state',0)nn = nnsetup([784 20 10]);opts.numepochs = 5; % Number of full sweeps through datann.output = 'softmax'; % use softmax outputopts.batchsize = 1000; % Take a mean gradient step over this many samplesopts.plot = 1; % enable plottingnn = nntrain(nn, train_x, train_y, opts);[er, bad] = nntest(nn, test_x, test_y);assert(er < 0.1, 'Too big error');%% ex6 neural net with sigmoid activation and plotting of validation and training error% split training data into training and validation datavx = train_x(1:10000,:);tx = train_x(10001:end,:);vy = train_y(1:10000,:);ty = train_y(10001:end,:);rand('state',0)nn = nnsetup([784 20 10]); nn.output = 'softmax'; % use softmax outputopts.numepochs = 5; % Number of full sweeps through dataopts.batchsize = 1000; % Take a mean gradient step over this many samplesopts.plot = 1; % enable plottingnn = nntrain(nn, tx, ty, opts, vx, vy); % nntrain takes validation set as last two arguments (optionally)[er, bad] = nntest(nn, test_x, test_y);assert(er < 0.1, 'Too big error');```[](https://bitdeli.com/free "Bitdeli Badge")
蓝牙模块HM-10手册
蓝牙模块HM-10手册，该手册对蓝牙模块HM-10进行了详细的介绍和使用方法以及一些AT指令。
VB摄像头监控
该VB模块可对摄像头捕获的画面进行录像等，可在创建的窗口进行控制
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