caffe windows

Caffe Brief Introduction: Caffe doesn't have a Windows version yet, so I need to log on to a Linux server Caffe main processing picture/picture sequence data format read by Caffe Read from a dedicated database (Lmdb, LEVELDB) Read pictures directly

encapsulated in the operating system kernel.Some developers will directly use the pthread provided by the Linux kernel for code design, such as Tomas Mikolov Word2vec.This gives the cross-platform to compile the source code, brings the trouble (many people in order to compile Word2vec, install Linux, or look for virtual machine).Caffe uses the multi-threaded function of the boost library by default, and as with the QT design concept, the Boost librar

Caffe: Caffe does not have a Windows version, so I need to remotely log on to the Linux Server Caffe mainly processes image/Image Sequences Data format read by caffe Read from a dedicated database (lmdb, leveldb) Read images directly Read from memo

official website and then made using UltraISO in windows. There is a large article to search for. I will not go into details here.1.2 download cuda7.5. The downloaded version is the run file of ubuntu15.04, which is convenient for you.1.3 download cudnn4.0. If you have registered an nvidia account, click download. Otherwise, you need to register an account. After the registration, you can select a few hooks, the next step is to accept the terms and t

Objective:Body:1. Install the necessary dependent packages:sudo Install libprotobuf-dev libleveldb-dev libsnappy-dev libopencv-dev libhdf5-serial-dev protobuf-Compiler sudoinstall --no-install-recommends libboost-all-devsudo Install libatlas-base-devsudoinstall python-devsudo Install Libgflags-dev libgoogle-glog-dev Liblmdb-devPython requires version 2.7, which is already installed by the operating system itself. The input python2.7--version will display the specific version number instruction

the following link for Ubuntu 12.04 installation CUDA-5.5CUDA7.5 Configuration Caffe Tutorial http://www.linuxidc.com/Linux/2016-07/132859.htm under Ubuntu 16.04 system1. Download the required documents1.1 Ubuntu16.04 in the official website to download, and then under Windows with UltraISO production, related article search has a large, here no longer repeat.1.2 cuda8.0 Download, download version is ubunt

OrderThis article is for beginners who want to learn how to use the Caffe framework, if there are errors in the text, please point out.Since I built this environment to refer to a lot of online tutorials, but no, so the text of the pictures mostly from the network.This article does not install MATLAB steps, so need to install and configure MATLAB classmate please Baidu matlab installation.1. Build Ubuntu14.04 dual system in WIN10 environmentPlease pre

1. Install Blas:$ sudo apt-get install Libatlas-base-dev2. Install the dependencies:$ sudo apt-get install Libprotobuf-dev libleveldb-dev libsnappy-dev libopencv-dev libboost-all-dev Libhdf5-serial-dev Protobuf-compiler Liblmdb-dev3. Install additional dependencies:$ sudo apt-get install Libgflags-dev libgoogle-glog-dev Liblmdb-dev4. Download Caffe:$ git clone git://github.com/bvlc/caffe.gitBecause of the slow download speed, this step can be directly

These two days of graduation design to use the Caffe, in the image preprocessing to call the Python Caffe interface, the results appearImportError: No module named _caffeSo I found a variety of solutions on the Internet, and finally discovered that this was the pit I left when I installed and configured Caffe:Here quote http://blog.sina.com.cn/s/blog_74f32c400102wjli.html This blog post, I read this article

1.079s OK (skipped= 7) Continue execution of install (this step cannot be omitted) Python setup.py Install Output: Finished processing dependenceies for protobuf==3.0.0 After the setup was successful, we tried to import Caffe in Python,The Skimage.io module is missing, indicating that additional scikit-image is required. Pip Install Scikit-image The result was wrong again, What the hell is this? Visual C + + 9.0 is required. How to still need VS

Website:Http://caffe.berkeleyvision.org/installation.html#compilationReference website:Http://www.cnblogs.com/dupuleng/articles/4213834.htmlHttp://www.cnblogs.com/empty16/p/4793404.html--------------------------------------------------------------------------------------------------------------- ----------------------First, install the Build-essentialsInstall some basic packages needed for development1 sudo apt-get Install build-essentialSecond, CUDA installationThis step consists of three parts

Caffegit clone git://github.com/bvlc/caffe.git7. Installing CaffeCP Makefile.config.example Makefile.configBecause there is no GPU here, you need to set cpu_only:= 1 in the Makefile.config file to remove the comment.and then compile Make All Make Test make RuntestAfter installation we can try to run a lenet on the mnist.1. Get Mnist Data firstCD Caffe. /data/mnist/get_mnist. SH2. Then create the lenet, be sure to run the following command at the r

Caffe is generally installed under the Linux system, online about the installation of Windows Tutorial tutorial, and each tutorial is not very full, I am here to summarize the process and all the solutions to the bug.I am the Win10+gtx1080+vs2013,matlab interface I am matlab2016a.1. Install Visual Studio 2013 first. It's not much of a difficulty, just download and install it online.2. Download Caffe.https:/

Opencvconfig.cmakecmakefiles data opencvconfig-version.cmakecmake_install.cmake Doc OpenCVModules.cmakecmake_uninstall.cmake include test-reportscmakevars.txt junk Unix-installcpackconfig.cmake Lib version_string.tmp[Email protected]:~/opencv/build# make-j4[Email protected]:~/opencv/build# sudo make installCaffe[Email protected]:~/

This blog is based on http://www.cnblogs.com/njust-ycc/p/5776286.html this blog modified, made a correction and supplement.The environment of my machine: win7+matlab2014b+vs20131. First go to GitHub to download Microsoft's Caffe package, address: Https://github.com/microsoft/caffeAfter downloading, unzip to get:Copy the CommonSettings.props.example under the Caffe-master\

#Coding=utf-8Importh5pyImportNumPy as NPImportCaffe#1. Import Datafilename ='Testdata.h5'F= H5py. File (filename,'R') N1= F.get ('Data') N1=Np.array (N1)Printn1[0]n2=f.get ('label_1d') N2=Np.array (n2) f.close ()#2. Importing models and Networksdeploy='Gesture_deploy.prototxt' #Deploy fileCaffe_model='Iter_iter_1000.caffemodel' #well-trained CaffemodelNET =caffe.net (Deploy,caffe_model,caffe. TEST) Count=0#statistics The number of predicted value

In the routines provided by Caffe, such as Mnist and Cifar10, the preparation of datasets is done by calling code themselves, and for the ImageNet1000 class database, for the university laboratory, often facing the embarrassment of insufficient computer memory. For the application, it is more important to train and test the data sets that are suitable for their own conditions in Caffe. So it is necessary fo

　　0: Today is 20171114, this tutorial does not know when to be effective1: We installed the GPU version, so to install Cuda, this four platform compatible version is, cuda8+cudnn6. To the official website is the new version of CUDA8. Baidu Cloud: HTTP://PAN.BAIDU.COM/S/1PKKXS51,M2JL2: Because Caffe out of Anaconda installation because of the lack of some dependency error patterns, we use the anaconda to configure the Python interface. Two are compatib

project using a third-party library scheme. In this paper, the design idea of Caffe two I/O is analyzed, and the concrete implementation of Caffe multithreading I/O and multiple pre buffering is explained. On this basis, the design and implementation of Caffe multi-GPU data parallel are analyzed. In view of the disadvantages of

Turn-picked HTTP://WEIBO.COM/P/2304189DB078090102VDVXAlthough the deep learning has not been anything new, but for the reason of the equipment, I have not been involved. It was a pleasure to replace a workstation with a GPU the other day. So eagerly installed an Ubuntu system, began to configure the Deeplearning framework Caffe. It took about two days before and after, and finally it was well-equipped. With all these years of software,