yolo caffe

This article describes how to use the well-trained model for image classification in MATLAB. Will take mnist as an example, the main use of Caffe-master\matlab\demo under the CLASSIFICATION_DEMO.M, can refer to my previous blog "Caffe-windows" Caffe-master CLASSFICATION_DEMO.M Ultra-Detailed analysis (http://blog.csdn.net/u011995719/article/details/54135189)First

Main reference HTTPS://GITHUB.COM/WEILIU89/CAFFE/TREE/SSD get SSD code, download complete with a Caffe folder git clone https://github.com/weiliu89/caffe.git cd caffe git Checkout SSDGo to the downloaded Caffe directory and copy the configuration file CD Caffe CP Makefile.co

Mini-caffe is a running version of the minimized Caffe, used only for forward, high efficiency and small footprint, so it is extremely suitable for online testing. However, if you implement the unofficial Caffe layer yourself, you also need to implement the corresponding calculation code in Mini-caffe. This article com

How to Use Caffe in a program for image classification and caffe image classification Caffe is an open-source library with excellent deep learning capabilities. It samples c ++ and CUDA implementations and has the advantages of fast speed and convenient model definition. After studying for a few days, I found that there is also an inconvenient point, that is, the

Caffe is a framework for deep learning, written by C + + and Python, and the bottom is C + + source. First, Caffe-master source code large framework: The key documents are as follows:-Data: Used to store the raw information (images, etc.) required for a program in Caffe-master-Docs: For storing help documents-Examples: for storing code-Include/

Pre-Installation Preparation work: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 the python-devsudo Install Libgflags-dev libgoogle-glog-dev Liblmdb-dev "Optional" Installation Cuda and anaconda, see Ubuntu16.04+theano Environment in detail Download Caffe:git clone https://github.com/bvlc/caffe.git To modify a configuration file:CD

Ubuntu, the C + + classification interface uses the method, as follows: This blog is a broadcast of the blog ... The author realized that after using Caffe training model, how to call the model in the program is a problem that many friends pay attention to, therefore, the author intends to explain how to use C + + to call Caffe training model in the program, the following start body. in your friends from

/lib/intel644. Complete the Lib file connection operation, execute:sudo ldconfig–v 3. Install Caffe1. Installation dependencies:sudo apt-get install libprotobuf-dev libleveldb-dev libsnappy-dev Libopencv-dev Libboost-all-dev Libhdf5-serial-dev 2. Edit Makefile.config Switch to Caffe file directory:cd/home/fische/caffe-master Copy Makefile.config.examples file:cp Makefile.config.examples Makefile.config Edit

Recently, in learning deep learning, the tool used is caffe, easier to use, not long-winded, first of all, said the configuration and compilation of the environment. the platform of the system is win10+matlab2014b+vs2013. Before starting, to install the Cuda driver, I use the Cuda 7.5 version (to sync with the version used inside the Caffe). First, in https://github.com/happynear/

Caffe installation is very troublesome, especially my last choice is to install on Windows, really not easy. Caffe in Ubuntu installation is relatively simple, a lot of information, later installed and then write the installation process. Here are some references to the Caffe installation process on Windows. Https://github.com/happynear/

1. First need to match a good caffe of the operating environment, can refer to my another blog: http://blog.csdn.net/llwjason5555/article/details/62424085 2. Open Codeblocks, set up engineering, right click Engineering, select Build Options,linker setting left add OpenCV Dynamic Library and/caffe/build/lib/libcaffe.so, add to right -pthread -lcaffe-lglog-lgflags-lprotobuf-lboost_system-lboost_filesystem -

Tags: end ORC step Installation tutorial proc IPY Post Network flagsThis is the second part of the Caffe Beginner series , designed to help more students who are interested in deep learning! The first section can refer to the following address:Caffe Beginner First: Detailed procedure for installing Caffe (CPU) +python on Ubuntu14.04 (pro-Test success, 20180524 update)OK, let's start our tutorial!Objective:B

Transferred from: http://blog.csdn.net/qq_14975217/article/details/51524042Blob:4 dimensions n x C x H x W;Bottom[0], bottom[1] represents several inputs for the layer.Bottom[0]->count (): Input, total number of dimensions (number of elements)Bottom[0]->nums (): input, the number of blocks (block), the parameter also corresponds to Batch_size, that is, several pictures are entered at the same timeC: Is the number of convolution cores (filter), each convolution core produces a channel output, in

Someone has been on Caffe does all the bookkeeping for any DAG of layers to ensure correctness of the forward and backward. This sentence has doubts. I give an explanation: First, the whole process of determining caffe and retransmission is given: first, the creator function of the layer is obtained from the string of the parameter file to the registry of the layer, then the instance of the layer is creat

First, look at your own system, UBUNTU16.04,CPU, no nvidia, no OPENCVSecond, install the dependency package Install PROTOBUF,LEVELDB,SNAPPY,OPENCV,HDF5, protobuf compiler andboost: sudo apt-get install Libprotobuf-dev libleveldb-dev libsnappy-dev libopencv-dev libhdf5-serial-dev protobuf- Compilersudo apt-get install--no-install-recommends Libboost-all-dev Install Gflags,glogs, Lmdb Andatlas. sudo apt-get install Libgflags-dev libgoogle-glog-dev liblmdb-devsudo apt-ge

One way to learn knowledge is to use it first and then ask why.After the installation is complete Caffe, according to Caffe tips download mnist training test data, and run Lenet training model, the question is how I use Caffe training their data ah, mnist data through the script can download the creation of Lmdb, What do I do to train my own data set?To train you

Go to official github to download the Caffe zip file and unzip itCD to Caffe-master folder, generate Makefile.config configuration file, execute:$ CP Makefile.config.example Makefile.configConfigure Makefile.config file (only the modified parts are listed)A. If you enable CUDNN, remove the "#" in front of itUSE_CUDNN: = 1B. Configure some reference files (the additional part is mainly to solve the problem o

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

Dump: LMDBE:\ machine learning 2\caffe data \caffe_root\caffe-master\build\x64\release>convert_imageset.exe e:/machine learning 2/caffe Data/caffe_root/ Caffe-master/examples/myfile/train e:/Machine learning 2/caffe data/caffe_root/caffe