caffe Getting started with caffe

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Caffe is a library written in C++, to facilitate the experimentation with and use of Convolutional Neural Networks (CNN). Caffe has been developed by Berkeley Vision and Learning Center (BVLC).

Caffe is actually an abbreviation referring to "Convolutional Architectures for Fast Feature Extraction". This acronym encapsulates an important scope of the library. Caffe in the form of a library offers a general programming framework/architecture which can be used to perform efficient training and testing of CNNs. "Efficiency" is a major hallmark of caffe, and stands as a major design objective of Caffe.

Caffe is an open-source library released under BSD 2 Clause license.

Caffe is maintained on GitHub

Caffe can be used to :

  • Efficiently train and test multiple CNN architectures, specifically any architecture that can be represented as a directed acyclic graph (DAG).
  • Utilize multiple GPUs (upto 4) for training and testing. It is recommended that all the GPUs should be of the same type. Otherwise, performance is limited by the limits of the slowest GPU in the system. For example, in case of TitanX and GTX 980, the performance will be limited by the latter. Mixing multiple architectures is not supported, e.g. Kepler and Fermi 3.

Caffe has been written following efficient Object Oriented Programming (OOP) principles.

A good starting point to begin an introduction to caffe is to get a bird's eye view of how caffe works through its fundamental objects.


VersionRelease Date

Enable multithreading with Caffe

Caffe can run on multiple cores. One way is to enable multithreading with Caffe to use OpenBLAS instead of the default ATLAS. To do so, you can follow these three steps:

  1. sudo apt-get install -y libopenblas-dev
  2. Before compiling Caffe, edit Makefile.config , replace BLAS := atlas by BLAS := open
  3. After compiling Caffe, running export OPENBLAS_NUM_THREADS=4 will cause Caffe to use 4 cores.

Installation and setup


Below are detailed instructions to install Caffe, pycaffe as well as its dependencies, on Ubuntu 14.04 x64 or 14.10 x64.

Execute the following script, e.g. "bash" (~30 to 60 minutes on a new Ubuntu).

# This script installs Caffe and pycaffe. 
# CPU only, multi-threaded Caffe.

# Usage: 
# 0. Set up here how many cores you want to use during the installation:
# By default Caffe will use all these cores.

sudo apt-get install -y libprotobuf-dev libleveldb-dev libsnappy-dev 
sudo apt-get install -y libopencv-dev libhdf5-serial-dev
sudo apt-get install -y --no-install-recommends libboost-all-dev
sudo apt-get install -y libatlas-base-dev 
sudo apt-get install -y python-dev 
sudo apt-get install -y python-pip git

# For Ubuntu 14.04
sudo apt-get install -y libgflags-dev libgoogle-glog-dev liblmdb-dev protobuf-compiler 

# Install LMDB
git clone 
cd lmdb/libraries/liblmdb
sudo make 
sudo make install

# More pre-requisites 
sudo apt-get install -y cmake unzip doxygen
sudo apt-get install -y protobuf-compiler
sudo apt-get install -y libffi-dev python-pip python-dev build-essential
sudo pip install lmdb
sudo pip install numpy
sudo apt-get install -y python-numpy
sudo apt-get install -y gfortran # required by scipy
sudo pip install scipy # required by scikit-image
sudo apt-get install -y python-scipy # in case pip failed
sudo apt-get install -y python-nose
sudo pip install scikit-image # to fix

# Get caffe (
mkdir caffe
cd caffe
unzip -o
cd caffe-master

# Prepare Python binding (pycaffe)
cd python
for req in $(cat requirements.txt); do sudo pip install $req; done

# to be able to call "import caffe" from Python after reboot:
echo "export PYTHONPATH=$(pwd):$PYTHONPATH " >> ~/.bash_profile 
source ~/.bash_profile # Update shell 
cd ..

# Compile caffe and pycaffe
cp Makefile.config.example Makefile.config
sed -i '8s/.*/CPU_ONLY := 1/' Makefile.config # Line 8: CPU only
sudo apt-get install -y libopenblas-dev
sed -i '33s/.*/BLAS := open/' Makefile.config # Line 33: to use OpenBLAS
# Note that if one day the Makefile.config changes and these line numbers may change
echo "export OPENBLAS_NUM_THREADS=($NUMBER_OF_CORES)" >> ~/.bash_profile 
mkdir build
cd build
cmake ..
cd ..
make all -j$NUMBER_OF_CORES # 4 is the number of parallel threads for compilation: typically equal to number of physical cores
make pycaffe -j$NUMBER_OF_CORES
make test
make runtest
#make matcaffe
make distribute

# Afew few more dependencies for pycaffe
sudo pip install pydot
sudo apt-get install -y graphviz
sudo pip install scikit-learn

At the end, you need to run "source ~/.bash_profile" manually or start a new shell to be able to do 'python import caffe'.

Regularization loss (weight decay) in Caffe

In the solver file, we can set a global regularization loss using the weight_decay and regularization_type options.

In many cases we want different weight decay rates for different layers. This can be done by setting the decay_mult option for each layer in the network definition file, where decay_mult is the multiplier on the global weight decay rate, so the actual weight decay rate applied for one layer is decay_mult*weight_decay .

For example, the following defines a convolutional layer with NO weight decay regardless of the options in the solver file.

layer {
  name: "Convolution1"
  type: "Convolution"
  bottom: "data"
  top: "Convolution1"
  param {
    decay_mult: 0
  convolution_param {
    num_output: 32
    pad: 0
    kernel_size: 3
    stride: 1
    weight_filler {
      type: "xavier"

See this thread for more information.

Got any caffe Question?