Installation and test __caffe of Caffe

Recommendation system and hardware combination: Ubuntu14.04+cuda 7.5 1, installation Cuda

Install Caffe:git clone Https://github.com/BVLC/caffe

Install the Caffe Dependency pack: http://caffe.berkeleyvision.org/

->installation Instructions

->ubuntu Installation

->sudo apt-get Install Libprotobuf-dev Libleveldb-dev Libsnappy-devlibopencv-dev Protobuf-compiler

->sudo apt-get Install--no-install-recommends Libboost-all-dev

->sudo apt-get Install Libgflags-dev libgoogle-glog-dev liblmdb-dev 2, installing Python dependency packs

cython>=0.19.2

numpy>=1.7.1

scipy>=0.13.2

scikit-image>=0.9.3

matplotlib>=1.3.1

ipython>=3.0.0

h5py>=2.2.0

leveldb>=0.191

networkx>=1.8.1

nose>=1.3.0

pandas>=0.12.0

Python-dateutil>=1.4,<2

protobuf>=2.5.0

python-gflags>=2.0

pyyaml>=3.10

pillow>=2.3.0

six>=1.1.0

Cd caffeàless Python/requirements.txt

Sudo Apt-getinstall ... Each dependent package

Eg:sudo apt-getinstall Cython python-numpy python-scipy python-scikits-learn Python-matplotlibipython python-h5py Python-leveldb python-networkx python-nose python-pandaspython-dateutil python-protobuf python-gflags Python-six

Makefile configuration file Configuration

1, copy the Makefile configuration file under the Caffe folder MAKEFILE.CONFIG-EXAMPLE:CP Makefile.config-examplemakefile.config

>>vimmakefile.config the file for configuration changes

Use_leveldb

Use_lmdb These two are Caffe training knowledge, because Caffe training data needs to be read from the database, this database is done by LEVELDB and Lmdb.

>>make or make-j* (indicates that several threads are compiling Caffe, which is faster)

2, the ~/caffe/build folder with CMake for the compilation is entered >>cmake. (Failure modifies vim. /cmake/dependencies.cmake need RM CMakeCache.txt, then cmake again.

>>make or make-j* (indicates that several threads are compiling Caffe, which is faster) 3, testing

./build/examples/cpp_classification/classification.bin

Definition of Models/bvlc_reference_caffenet/deploy.prototxt//model network structure

Weights in the Models/bvlc_reference_caffenet/bvlc_reference_caffenet.caffemodel//network

Data/ilsvrc12/imagenet_mean.binaryproto//image preprocessing, image to mean (normalized)

Data/ilsvrc12/synset_words.txt//class name of each class

Examples/images/cat.jpg//Input image