This fork of BVLC/Caffe is dedicated to improving performance of this deep learning framework when running on CPU, in particular Intel® Xeon processors (HSW+) and Intel® Xeon Phi processors
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examples fix wrong prototxt file path Mar 15, 2018
external Fix the prepare MLSL script issue on Ubuntu. Apr 4, 2018
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CMakeLists.txt fix crash issue of async sgd; make USE_MLSL as default build flag Feb 28, 2018 [docs] add which will appear on GitHub new Issue/PR p… Jul 30, 2015 clarify the license and copyright terms of the project Aug 7, 2014 installation questions -> caffe-users Oct 19, 2015
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Makefile.config.example fix crash issue of async sgd; make USE_MLSL as default build flag Feb 28, 2018
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Makefile.mkldnn Make support not downloading MKLDNN if there is no network connection. Jan 20, 2018 Refine README May 2, 2018
caffe.cloc [fix] stop cloc complaint about cu type Sep 4, 2014
mkldnn.commit Merge from remotes/internal/release_1.1.1 Mar 26, 2018

Intel® Distribution of Caffe*

This fork Caffe is dedicated to improving Caffe performance when running on CPU, in particular Intel® Xeon processors (HSW, BDW, Xeon Phi)


Build procedure is the same as on bvlc-caffe-master branch, see section "Caffe". Both Make and CMake can be used. When OpenMP is available will be used automatically.


Run procedure is the same as on bvlc-caffe-master branch.

Current implementation uses OpenMP threads. By default the number of OpenMP threads is set to the number of CPU cores. Each one thread is bound to a single core to achieve best performance results. It is however possible to use own configuration by providing right one through OpenMP environmental variables like OMP_NUM_THREADS or GOMP_CPU_AFFINITY.

If some system tool like numactl is used to control CPU affinity, by default caffe will prevent to use more than one thread per core. When less than required cores are specified, caffe will limit execution of OpenMP threads to specified cores only.

Best performance solution

Please read our Wiki for our recommendations and configuration to achieve best performance on Intel CPUs. Please find the performance and convergence test result at

Multinode Training

Intel® Distribution of Caffe* multi-node allows you to execute deep neural network training on multiple machines.

To understand how it works and read some tutorials, go to our Wiki. Start from Multinode guide.

License and Citation

Caffe is released under the BSD 2-Clause license. The BVLC reference models are released for unrestricted use.

Please cite Caffe in your publications if it helps your research:

  Author = {Jia, Yangqing and Shelhamer, Evan and Donahue, Jeff and Karayev, Sergey and Long, Jonathan and Girshick, Ross and Guadarrama, Sergio and Darrell, Trevor},
  Journal = {arXiv preprint arXiv:1408.5093},
  Title = {Caffe: Convolutional Architecture for Fast Feature Embedding},
  Year = {2014}

*Other names and brands may be claimed as the property of others

SSD: Single Shot MultiBox Detector

This repository contains merged code issued as pull request to BVLC caffe written by: Wei Liu, Dragomir Anguelov, Dumitru Erhan, Christian Szegedy, Scott Reed, Cheng-Yang Fu, Alexander C. Berg.

Original branch can be found at

Read our wiki page for more details.


Build Status License Caffe is a deep learning framework made with expression, speed, and modularity in mind. It is developed by the Berkeley Vision and Learning Center (BVLC) and community contributors.

Check out the project site for all the details like

and step-by-step examples.

Join the chat at

Please join the caffe-users group or gitter chat to ask questions and talk about methods and models. Framework development discussions and thorough bug reports are collected on Issues.

Happy brewing!