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| # Caffe | ||
| [](https://travis-ci.org/BVLC/caffe) | ||
| [](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](http://bvlc.eecs.berkeley.edu)) and community contributors. | ||
|
|
||
| Check out the [project site](http://caffe.berkeleyvision.org) for all the details like | ||
| - [DIY Deep Learning for Vision with Caffe](https://docs.google.com/presentation/d/1UeKXVgRvvxg9OUdh_UiC5G71UMscNPlvArsWER41PsU/edit#slide=id.p) | ||
| - [Tutorial Documentation](http://caffe.berkeleyvision.org/tutorial/) | ||
| - [BVLC reference models](http://caffe.berkeleyvision.org/model_zoo.html) and the [community model zoo](https://github.com/BVLC/caffe/wiki/Model-Zoo) | ||
| - [Installation instructions](http://caffe.berkeleyvision.org/installation.html) | ||
|
|
||
| and step-by-step examples. | ||
|
|
||
| [](https://gitter.im/BVLC/caffe?utm_source=badge&utm_medium=badge&utm_campaign=pr-badge&utm_content=badge) | ||
|
|
||
| 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. | ||
| Framework development discussions and thorough bug reports are collected on [Issues](https://github.com/BVLC/caffe/issues). | ||
|
|
||
| Happy brewing! | ||
|
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||
|
|
||
| # SSD: Single Shot MultiBox Detector | ||
| This repository contains merged code issued as pull request to BVLC caffe written by: | ||
| [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). | ||
|
|
||
| Original branch can be found at https://github.com/weiliu89/caffe/tree/ssd. | ||
|
|
||
| Read our [wiki page](https://github.com/intel/caffe/wiki/SSD:-Single-Shot-MultiBox-Detector) for more details. | ||
|
|
||
| # Intel® Distribution of Caffe* | ||
| This fork is dedicated to improving Caffe performance when running on CPU, in particular Intel® Xeon processors (HSW, BDW, Xeon Phi) | ||
|
|
||
| ## Building | ||
| Build procedure is the same as on bvlc-caffe-master branch. Both Make and CMake can be used. | ||
| When OpenMP is available will be used automatically. | ||
|
|
||
| ## Running | ||
| 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](https://github.com/intel/caffe/wiki/Recommendations-to-achieve-best-performance) for our recommendations and configuration to achieve best performance on Intel CPUs. | ||
|
|
||
| ## 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](https://github.com/intel/caffe/wiki/Multinode-guide). | ||
|
|
||
| ## License and Citation | ||
| Caffe is released under the [BSD 2-Clause license](https://github.com/BVLC/caffe/blob/master/LICENSE). | ||
| The BVLC reference models are released for unrestricted use. | ||
|
|
||
| Please cite Caffe in your publications if it helps your research: | ||
|
|
||
| @article{jia2014caffe, | ||
| 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 | ||
|
|
||
|
|
||
|
|
||
| # Caffe | ||
| [](https://travis-ci.org/BVLC/caffe) | ||
| [](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](http://bvlc.eecs.berkeley.edu)) and community contributors. | ||
|
|
||
| Check out the [project site](http://caffe.berkeleyvision.org) for all the details like | ||
| - [DIY Deep Learning for Vision with Caffe](https://docs.google.com/presentation/d/1UeKXVgRvvxg9OUdh_UiC5G71UMscNPlvArsWER41PsU/edit#slide=id.p) | ||
| - [Tutorial Documentation](http://caffe.berkeleyvision.org/tutorial/) | ||
| - [BVLC reference models](http://caffe.berkeleyvision.org/model_zoo.html) and the [community model zoo](https://github.com/BVLC/caffe/wiki/Model-Zoo) | ||
| - [Installation instructions](https://github.com/intel/caffe/wiki/Installation) | ||
|
|
||
| and step-by-step examples. | ||
|
|
||
| [](https://gitter.im/BVLC/caffe?utm_source=badge&utm_medium=badge&utm_campaign=pr-badge&utm_content=badge) | ||
|
|
||
| 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. | ||
| Framework development discussions and thorough bug reports are collected on [Issues](https://github.com/BVLC/caffe/issues). | ||
|
|
||
| Happy brewing! | ||
|
|
||
|
|
||
| # SSD: Single Shot MultiBox Detector | ||
| This repository contains merged code issued as pull request to BVLC caffe written by: | ||
| [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). | ||
|
|
||
| Original branch can be found at https://github.com/weiliu89/caffe/tree/ssd. | ||
|
|
||
| Read our [wiki page](https://github.com/intel/caffe/wiki/SSD:-Single-Shot-MultiBox-Detector) for more details. | ||
|
|
||
| # Intel® Distribution of Caffe* | ||
| This fork is dedicated to improving Caffe performance when running on CPU, in particular Intel® Xeon processors (HSW, BDW, Xeon Phi) | ||
|
|
||
| ## Building | ||
| Build procedure is the same as on bvlc-caffe-master branch. Both Make and CMake can be used. | ||
| When OpenMP is available will be used automatically. | ||
|
|
||
| ## Running | ||
| 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](https://github.com/intel/caffe/wiki/Recommendations-to-achieve-best-performance) for our recommendations and configuration to achieve best performance on Intel CPUs. | ||
|
|
||
| ## 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](https://github.com/intel/caffe/wiki/Multinode-guide). | ||
|
|
||
| ## License and Citation | ||
| Caffe is released under the [BSD 2-Clause license](https://github.com/BVLC/caffe/blob/master/LICENSE). | ||
| The BVLC reference models are released for unrestricted use. | ||
|
|
||
| Please cite Caffe in your publications if it helps your research: | ||
|
|
||
| @article{jia2014caffe, | ||
| 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 | ||
|
|
||
|
|
||
|
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