caffe install

;(3) Convert the positive and negative samples to the Lmdb format :There is a convert_imageset.exe file in Build->x64->debug under the Windows downgrade Caffe install root directory to make Lmdb files (some people may only Have. cpp, Then you will need to build The. exe via vs Compilation)Under linux, call the create_imagenet.sh file in examples->imagenet and rewrite it (see related blog online)Here I descr

Enable the data set generated by Caffe to run directly on Theano (1)-lmdb, protobuf, and caffetheano No matter which framework is used for CNNs training, there are three types of datasets: The Training Set is used to train the network. The Validation Set is used to test the network accuracy during training. Test Set is used to Test the final accuracy after network training is completed. Caffe generates dat

System: ubuntu16.04 Graphics card: GTX1060 cuda8.0,cudnn8.0, opencv3.1 Before the caffe,linux in Windows have tried, but did not succeed, so take advantage of this period of time to tackle the key. After several toss, finally succeeded in setting up a good caffe in Ubuntu, this record the hole encountered, for inspection. Please post the reference post, thank the experience of the great God http://blog.csd

First install Cuda:Download from the NVIDIA official website: Cuda-repo-ubuntu1404-7-0-local_7.0-28_amd64.deb, there are two types of run and Deb, heavily recommended Deb format, easy to installCD to the directory where Cuda-repo-ubuntu1404-7-0-local_7.0-28_amd64.deb is located, such as mine:CD ~/software/cuda-repo-ubuntu1404-7-0-local_7.0-28_amd64.debPerform:sudo dpkg-i cuda-repo-ubuntu1404-7-0-local_7.0-28_amd64.deb;sudo apt-get updatesudo apt-get-y

First, Cpu-only installation method Detailed reference: http://hanzratech.in/2015/07/27/installing-caffe-on-ubuntu.html The approximate steps are as follows: 1. Install a variety of dependencies and environments (no GPU required, can skip Cuda installation) 2. Install, compile Caffe (modify Makefile.config file) I

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 memory (will occupy a lot of memory) Rea

Windows Caffe in the GPU compilation processGeForce8800 gts512:cc=1.1CUDA6.5Question one:SRC/CAFFE/LAYERS/CONV_LAYER.CU: Error:too Few arguments in function callError in Conv_layer.cu:forward_gpu_gemm needs the argument Skip_im2col #1962Solve:https://github.com/BVLC/caffe/issues/1962As @liqing-ustc replied, just add "false" as the fourth argument.Question two:1>d

I've been running Caffe training data for a while ago. Before using the trained Caffemodel to classify the pictures are command-line instructions, and then think of their own new project to call Caffe, combined with classification code to classify the image. Internet access to a lot of information, the most detailed article is: http://blog.csdn.net/qq_14845119/article/details/52541622#reply.First, step desc

I mainly analyze how to use Caffe pre-trained model for image classificationCaffe's examples the specific program of the task, to understand the process, as long as you read the program can Configure the Python environment, import NumPy, and set the display section # set up Python environment:numpy for numerical routines, and matplotlib for plotting import NumPy as NP import m Atplotlib.pyplot as PLT # display plots in this notebook %matplotlib inlin

Problem: Segnet (Tpami 2017) The official release code is implemented under the Caffe framework. But the original Caffe code needs to be reformed, see Caffe-segnet-cudnn5. And the transformation of the Caffe himself in the use of the time encountered a less useful place: the need to be in the. Prototxt to specify the

Preface Layer structure is the most basic unit of neural Network (neural Networks) modeling and computation. Because the neural network has different layer structure, different types of layers have different parameters. Therefore, each layer of caffe configuration is different, and the layer structure and parameters are predefined in the Prototxt file, here, we have the latest version of the Caffe model of

1. Download the source code from GitHubgit clone https://github.com/BVLC/caffe.git2. Installing the BLAS LibrarySelect Install MKL, download the student version on the official website, unzip to the storage directory. Authorize the extracted files firstchmod a+x parallel_studio_xe_2015 -RThen execute with root permissionsudo ./install.sh（一般都选择默认的选项）sudo vim /etc/ld.so.conf.d/intel_mkl.confConfigure the environment, add the following content/opt/intel/

The Caffe operation provides three interfaces: C + + interface (command line), Python interface, and MATLAB interface. This article first parses the command line, followed by the other two interfaces.Caffe's C + + main program (CAFFE.CPP) is placed in the Tools folder under the root directory, and of course there are some other feature files, such as: Convert_imageset.cpp, Train_net.cpp, Test_ Net.cpp, etc. are also placed in this folder. After compil

Reproduced from: Caffe code Reading-hierarchy-painless machine learning-Know the columns https://zhuanlan.zhihu.com/p/21796890 Caffe Source Reading--net Assembly-painless machine learning-Know the column https://zhuanlan.zhihu.com/p/21875025 Caffe code reading--solver-Painless machine learning-Know the column https://zhuanlan.zhihu.com/p/21800004 1.

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