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‎README.md

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# Caffe
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[![Build Status](https://travis-ci.org/BVLC/caffe.svg?branch=master)](https://travis-ci.org/BVLC/caffe)
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[![License](https://img.shields.io/badge/license-BSD-blue.svg)](LICENSE)
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Caffe is a deep learning framework made with expression, speed, and modularity in mind.
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It is developed by the Berkeley Vision and Learning Center ([BVLC](http://bvlc.eecs.berkeley.edu)) and community contributors.
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Check out the [project site](http://caffe.berkeleyvision.org) for all the details like
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- [DIY Deep Learning for Vision with Caffe](https://docs.google.com/presentation/d/1UeKXVgRvvxg9OUdh_UiC5G71UMscNPlvArsWER41PsU/edit#slide=id.p)
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- [Tutorial Documentation](http://caffe.berkeleyvision.org/tutorial/)
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- [BVLC reference models](http://caffe.berkeleyvision.org/model_zoo.html) and the [community model zoo](https://github.com/BVLC/caffe/wiki/Model-Zoo)
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- [Installation instructions](http://caffe.berkeleyvision.org/installation.html)
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and step-by-step examples.
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[![Join the chat at https://gitter.im/BVLC/caffe](https://badges.gitter.im/Join%20Chat.svg)](https://gitter.im/BVLC/caffe?utm_source=badge&utm_medium=badge&utm_campaign=pr-badge&utm_content=badge)
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Please join the [caffe-users group](https://groups.google.com/forum/#!forum/caffe-users) or [gitter chat](https://gitter.im/BVLC/caffe) to ask questions and talk about methods and models.
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Framework development discussions and thorough bug reports are collected on [Issues](https://github.com/BVLC/caffe/issues).
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Happy brewing!
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# SSD: Single Shot MultiBox Detector
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This repository contains merged code issued as pull request to BVLC caffe written by:
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[Wei Liu](http://www.cs.unc.edu/~wliu/), [Dragomir Anguelov](https://www.linkedin.com/in/dragomiranguelov), [Dumitru Erhan](http://research.google.com/pubs/DumitruErhan.html), [Christian Szegedy](http://research.google.com/pubs/ChristianSzegedy.html), [Scott Reed](http://www-personal.umich.edu/~reedscot/), [Cheng-Yang Fu](http://www.cs.unc.edu/~cyfu/), [Alexander C. Berg](http://acberg.com).
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Original branch can be found at https://github.com/weiliu89/caffe/tree/ssd.
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Read our [wiki page](https://github.com/intel/caffe/wiki/SSD:-Single-Shot-MultiBox-Detector) for more details.
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# Intel® Distribution of Caffe*
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This fork is dedicated to improving Caffe performance when running on CPU, in particular Intel® Xeon processors (HSW, BDW, Xeon Phi)
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## Building
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Build procedure is the same as on bvlc-caffe-master branch. Both Make and CMake can be used.
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When OpenMP is available will be used automatically.
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## Running
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Run procedure is the same as on bvlc-caffe-master branch.
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Current implementation uses OpenMP threads. By default the number of OpenMP threads is set
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to the number of CPU cores. Each one thread is bound to a single core to achieve best
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performance results. It is however possible to use own configuration by providing right
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one through OpenMP environmental variables like OMP_NUM_THREADS or GOMP_CPU_AFFINITY.
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If some system tool like numactl is used to control CPU affinity, by default caffe will prevent
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to use more than one thread per core. When less than required cores are specified, caffe will
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limit execution of OpenMP threads to specified cores only.
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## Best performance solution
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Please read [our Wiki](https://github.com/intel/caffe/wiki/Recommendations-to-achieve-best-performance) for our recommendations and configuration to achieve best performance on Intel CPUs.
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## Multinode Training
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Intel® Distribution of Caffe* multi-node allows you to execute deep neural network training on multiple machines.
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To understand how it works and read some tutorials, go to our Wiki. Start from [Multinode guide](https://github.com/intel/caffe/wiki/Multinode-guide).
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## License and Citation
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Caffe is released under the [BSD 2-Clause license](https://github.com/BVLC/caffe/blob/master/LICENSE).
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The BVLC reference models are released for unrestricted use.
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Please cite Caffe in your publications if it helps your research:
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@article{jia2014caffe,
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Author = {Jia, Yangqing and Shelhamer, Evan and Donahue, Jeff and Karayev, Sergey and Long, Jonathan and Girshick, Ross and Guadarrama, Sergio and Darrell, Trevor},
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Journal = {arXiv preprint arXiv:1408.5093},
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Title = {Caffe: Convolutional Architecture for Fast Feature Embedding},
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Year = {2014}
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}
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***
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*Other names and brands may be claimed as the property of others
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# Caffe
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[![Build Status](https://travis-ci.org/BVLC/caffe.svg?branch=master)](https://travis-ci.org/BVLC/caffe)
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[![License](https://img.shields.io/badge/license-BSD-blue.svg)](LICENSE)
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Caffe is a deep learning framework made with expression, speed, and modularity in mind.
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It is developed by the Berkeley Vision and Learning Center ([BVLC](http://bvlc.eecs.berkeley.edu)) and community contributors.
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Check out the [project site](http://caffe.berkeleyvision.org) for all the details like
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- [DIY Deep Learning for Vision with Caffe](https://docs.google.com/presentation/d/1UeKXVgRvvxg9OUdh_UiC5G71UMscNPlvArsWER41PsU/edit#slide=id.p)
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- [Tutorial Documentation](http://caffe.berkeleyvision.org/tutorial/)
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- [BVLC reference models](http://caffe.berkeleyvision.org/model_zoo.html) and the [community model zoo](https://github.com/BVLC/caffe/wiki/Model-Zoo)
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- [Installation instructions](https://github.com/intel/caffe/wiki/Installation)
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and step-by-step examples.
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[![Join the chat at https://gitter.im/BVLC/caffe](https://badges.gitter.im/Join%20Chat.svg)](https://gitter.im/BVLC/caffe?utm_source=badge&utm_medium=badge&utm_campaign=pr-badge&utm_content=badge)
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Please join the [caffe-users group](https://groups.google.com/forum/#!forum/caffe-users) or [gitter chat](https://gitter.im/BVLC/caffe) to ask questions and talk about methods and models.
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Framework development discussions and thorough bug reports are collected on [Issues](https://github.com/BVLC/caffe/issues).
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Happy brewing!
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# SSD: Single Shot MultiBox Detector
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This repository contains merged code issued as pull request to BVLC caffe written by:
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[Wei Liu](http://www.cs.unc.edu/~wliu/), [Dragomir Anguelov](https://www.linkedin.com/in/dragomiranguelov), [Dumitru Erhan](http://research.google.com/pubs/DumitruErhan.html), [Christian Szegedy](http://research.google.com/pubs/ChristianSzegedy.html), [Scott Reed](http://www-personal.umich.edu/~reedscot/), [Cheng-Yang Fu](http://www.cs.unc.edu/~cyfu/), [Alexander C. Berg](http://acberg.com).
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Original branch can be found at https://github.com/weiliu89/caffe/tree/ssd.
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Read our [wiki page](https://github.com/intel/caffe/wiki/SSD:-Single-Shot-MultiBox-Detector) for more details.
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# Intel® Distribution of Caffe*
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This fork is dedicated to improving Caffe performance when running on CPU, in particular Intel® Xeon processors (HSW, BDW, Xeon Phi)
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## Building
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Build procedure is the same as on bvlc-caffe-master branch. Both Make and CMake can be used.
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When OpenMP is available will be used automatically.
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## Running
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Run procedure is the same as on bvlc-caffe-master branch.
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42+
Current implementation uses OpenMP threads. By default the number of OpenMP threads is set
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to the number of CPU cores. Each one thread is bound to a single core to achieve best
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performance results. It is however possible to use own configuration by providing right
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one through OpenMP environmental variables like OMP_NUM_THREADS or GOMP_CPU_AFFINITY.
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If some system tool like numactl is used to control CPU affinity, by default caffe will prevent
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to use more than one thread per core. When less than required cores are specified, caffe will
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limit execution of OpenMP threads to specified cores only.
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## Best performance solution
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Please read [our Wiki](https://github.com/intel/caffe/wiki/Recommendations-to-achieve-best-performance) for our recommendations and configuration to achieve best performance on Intel CPUs.
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## Multinode Training
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Intel® Distribution of Caffe* multi-node allows you to execute deep neural network training on multiple machines.
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To understand how it works and read some tutorials, go to our Wiki. Start from [Multinode guide](https://github.com/intel/caffe/wiki/Multinode-guide).
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## License and Citation
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Caffe is released under the [BSD 2-Clause license](https://github.com/BVLC/caffe/blob/master/LICENSE).
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The BVLC reference models are released for unrestricted use.
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Please cite Caffe in your publications if it helps your research:
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@article{jia2014caffe,
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Author = {Jia, Yangqing and Shelhamer, Evan and Donahue, Jeff and Karayev, Sergey and Long, Jonathan and Girshick, Ross and Guadarrama, Sergio and Darrell, Trevor},
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Journal = {arXiv preprint arXiv:1408.5093},
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Title = {Caffe: Convolutional Architecture for Fast Feature Embedding},
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Year = {2014}
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}
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***
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*Other names and brands may be claimed as the property of others
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