相机重定位的开篇之作，机器视觉

involution.pytorch（） 一个PyTorch实现使用过密的 对合的非官方pytorch实现。 官方实现可以在找到。 特征 该层可以处理任意输入和输出通道，内核大小，步幅和减速比。 但是，输入通道应按组划分。 要求 pytorch >= 1.4.0 einops >= 0.3.0 用法 一个例子： >>> import torch >>> from involution import Involution >>> >>> x = torch.rand(2,8,5,5) >>> i = Involution(in_channels=8, out_channels=4, groups=4, kernel_size=3, stride=2, reduction_ratio=2) >>> i(x).size () torch.Size([2, 4, 3, 3]) 去做 Ima

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ELECTRA 中文 预训练 ELECTREA 模型: 基于对抗学习 pretrain Chinese Model code Repost from google official code: 具体使用说明：参考 官方链接 Electra Chinese tiny模型路径 google drive electra-tiny baidu drive electra-tiny code:rs99 模型说明 与 tinyBERT 的 配置相同 generator 为 discriminator的 1/4 How to use official code Steps 修改 configure_pretraining.py 里面的 数据路径、tpu、gpu 配置 修改 model_size：可在 code/util/training_utils.py 里面 自行定义模型大小 数据输入格式：原始的

ResneXt网络论文

该资源给出了论文 Squeeze-and-Excitation Networks 整体的思维导图情况，可以更方便的加深对论文的理解与应用，所使用的软件是 XMind zen.

NeuralNetStudio：开源递归神经网络程序（RNN）。 [MATLAB]

This accessible book provides an introduction to the analysis and design of dynamic multiagent networks. Such networks are of great interest in a wide range of areas in science and engineering, including: mobile sensor networks, distributed robotics such as formation flying and swarming, quantum networks, networked economics, biological synchronization, and social networks. Focusing on graph theoretic methods for the analysis and synthesis of dynamic multiagent networks, the book presents a powerful new formalism and set of tools for networked systems. The book's three sections look at foundations, multiagent networks, and networks as systems. The authors give an overview of important ideas from graph theory, followed by a detailed account of the agreement protocol and its various extensions, including the behavior of the protocol over undirected, directed, switching, and random networks. They cover topics such as formation control, coverage, distributed estimation, social networks, and games over networks. And they explore intriguing aspects of viewing networks as systems, by making these networks amenable to control-theoretic analysis and automatic synthesis, by monitoring their dynamic evolution, and by examining higher-order interaction models in terms of simplicial complexes and their applications. The book will interest graduate students working in systems and control, as well as in computer science and robotics. It will be a standard reference for researchers seeking a self-contained account of system-theoretic aspects of multiagent networks and their wide-ranging applications. This book has been adopted as a textbook at the following universities: University of Stuttgart, Germany Royal Institute of Technology, Sweden Georgia Tech, USA University of Washington, USA Ohio University, USA

Computer and Communication Networks(2nd) 英文无水印原版pdf 第2版 pdf所有页面使用FoxitReader、PDF-XChangeViewer、SumatraPDF和Firefox测试都可以打开 本资源转载自网络，如有侵权，请联系上传者或csdn删除 查看此书详细信息请在美国亚马逊官网搜索此书

在PyTorch中检索CNN图像：在PyTorch中训练和评估CNN以进行图像检索 这是一个Python工具箱，用于实现对本文所述方法的培训和测试： 无需人工注释即可对CNN图像进行微调， RadenovićF.，Tolias G.，Chum O.，TPAMI 2018 [ ] CNN图像检索从BoW获悉：无监督的微调，并附有困难的示例， RadenovićF.，Tolias G.，Chum O.，ECCV 2016 [ ] 它是什么？ 该代码实现： 训练（微调）CNN进行图像检索 学习CNN图像表示的监督美白 在牛津和巴黎数据集上测试CNN图像检索 先决条件 为了运行此工具箱，您将需要： Python3（在Debian 8.1上使用Python 3.7.0进行了测试） PyTorch深度学习框架（已通过1.0.0版测试） 其余所有（数据+网络）将通过我们的脚本自动下载