yolo caffe

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

#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

The full name of Caffe should be convolutional Architecture for Fast Feature embedding, which is a clear and efficient deep learning framework, which is open source, the core language is C + +, It supports command line, Python, and MATLAB interfaces, which can be run on the CPU or on the GPU. Its license is the BSD 2-clause.One reason why deep learning is popular is that it is able to learn from data autonomously to useful feature. Especially for

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]:~/

Transfer from website:http://blog.csdn.net/muyiyushan/article/details/705780771. Preparing the DataUsing the Dog/cat dataset, separate the train and Val folders under the training project root as the location where the training data and validation data are saved. Each of the train and Val folders has two folders: Dogs and cats, respectively, to save pictures of dog and cat. Dog and cat have 1000 training images and 400 test images respectively.Write a Python script file, Traverse train and Val t

Tags: markdown keyword root directory attribute read Process ALS sub folderConvert your own image data to Caffe required db (Leveldb/lmdb) fileAfter setting up the Caffe environment, we often need to train/test our image data, our image data often when the picture file, such as Jpg,jpeg,png, but in Caffe we need to use the type of data is Lmdb or LEVELDB, For exa

Reference URL: http://www.cnblogs.com/empty16/p/4828476.htmlSolemn statement:When using command-line operations under Linux, be sure to know what the command line means and then execute it, or enter a command if you don't know what happens next, and Linux is likely to collapse.Because under Linux, when you use sudo and root privileges, you can manipulate any file, even the system files that are in use.Caffe the following problems appear in the installation process some of the steps did not follo

1. Preparatory work 1). Download the pre-trained model Http://cs.unc.edu/~wliu/projects/ParseNet/VGG_ILSVRC_16_layers_fc_reduced.caffemodel Put this file in the/home/software/caffe/models/vggnet/directory, if so, it is best to back up. 2). Download the VOC2007 data set wget Http://host.robots.ox.ac.uk/pascal/VOC/voc2012/VOCtrainval_11-May-2012.tar wget/http Host.robots.ox.ac.uk/pascal/voc/voc2007/voctrainval_06-nov-2007.tar wget http://host.robot

excellent deep learning open-source framework code learning, is also a good point of neural network learning. Here to organize and share is the use of a very wide range of deep learning framework Caffe, this is a set of the earliest originated in Berkeley deep Learning framework, widely used in the task of neural networks, a large number of paper experiments are done with it, And the domestic e-commerce and other Internet companies a large number of

install Caffe with Python3 on Ubuntu (CPU only) Now the online installation Caffe version is basically based on Python 2.7, this tutorial is designed to use Anaconda 3 to install the Caffe CPU, the process is very difficult, continuous three days to finally have a complete tutorial. My system environment: Ubuntu 16.04, Anaconda 3.5.2 (that is, Python 3.5.2), low

This part of the content from the CDA in-depth learning combat classroom, taught by Tang Yudi if you attempt to use the CPU to train the model, then you are crazy ... In the training model, the most time-consuming factor is the size of the image, the general 227*227 with the CPU to train, training 10,000 times may be more than 1 weeks time. Different network structure, there may be different picture size needs, so before training need to understand, in the generation of Lmdb link directly confor

Caffe + Ubuntu 15.04 + CUDA 7.5 Novice Installation Configuration GuideSpecial:0. Caffe website address: http://caffe.berkeleyvision.org/1. This article is for the author to complete the experiment, but only for the use of academic exchange, the use of this guide any adverse consequences of the user's own responsibility, not related to the author of this article, thank you! In order to ensure timely updates

4. Caffe Multi-GPU parallel scenario 4.1 Multi-GPU Parallelism Overview Thanks to the explosive growth of training data and the tremendous increase in computational performance, deep learning algorithms can learn the distribution of data and hierarchical feature representations to better solve the tasks of pattern analysis and classification. In the face of huge data scale and complex deep learning model, the current mainstream single GPU training m

Caffe's project architecture and source code analysis Caffe is a deep learning framework based on the c ++/cuda language. developers can use it to freely build the desired network. Currently, convolutional Neural Networks and fully-Connected Neural Networks (Artificial Neural Networks) are supported ). On Linux, c ++ can operate interfaces through command lines. matlab and python have special interfaces, and Computing supports gpu and cpu. The current

Recently participated in a recognized competition, the project involved in a number of categories, originally intended to a large category training a classification model, but this will be more troublesome, for the same image classification will be repeated calculation of the classification network convolutional layer, waste computing time and efficiency. Later found that multi-tasking learning in deep learning can achieve multi-label classification, all categories only need to train a classific

After the guidance of friends, and finally successfully compiled the Caffe on Windows7, where the compilation process is recorded Installation file Preparation Installing Visual Studio 2013 Installing Cuda75 Compiling Caffe 1 extracting the downloaded caffe-windows file 2 Go to Windows folder 3 Copy the configuration fi

File Description: 1. Caffe Run Example Cifar10 2. Analyzing the data collection model structure of CIFAR10 3. Give the Ciffar10 operation steps Operating Environment: Windows7 X86 + Caffe + VS2013 Resources: 1. http://blog.csdn.net/maweifei/article/details/52981425 2. http://www.cs.toronto.edu/~kriz/cifar.html (binary database) 3. Http://groups.csail.mit.edu/vision/TinyImages (Image database) 4. Http://ww

As a result of work handover needs, the Caffe use method and the overall structure are described clearly. In view of the students have asked me related content, decided to write a simple tutorial in this article, convenient for everyone to reference.This article simply tells a few things: what Caffe can do. Why Choose Caffe? Environment integral structure Protoco

A brief description of the process of lmdb generation 1, organize and constrain the size, folder. The picture is placed under different folders, note that the size of the picture needs to be in a uniform format, otherwise the calculation of the mean file will be an error. 2. Put the content generation list into the TXT file. Two txt files, train training file, Val test file. The train inside is your classification. 3, the formation of LMDB data set. 4, form the training set

Caffe is the deeplearning common frame, is currently doing CNN the mainstream method, official website reference http://caffe.berkeleyvision.org/(1) Caffe Introduction:Caffe has the following features: 1 expression: Caffe is mainly composed of modles+optimizations, models explains how each layer of the depth network is defined and connected, generally de