This branch of Caffe extends [BVLC-led Caffe](https://github.com/BVLC/caffe) by adding Windows support and other functionalities commonly used by Microsoft's researchers, such as managed-code wrapper, [Faster-RCNN](https://papers.nips.cc/paper/5638-faster-r-cnn-towards-real-time-object-detection-with-region-proposal-networks.pdf), [R-FCN](https://arxiv.org/pdf/1605.06409v2.pdf), etc. **Update**: this branch is not actively maintained. Please checkout [this](https://github.com/BVLC/caffe/tree/windows) for more active Windows support. --- # Caffe | **`Linux (CPU)`** | **`Windows (CPU)`** | |-------------------|----------------------| | [](https://travis-ci.org/Microsoft/caffe) | [](https://ci.appveyor.com/project/zer0n/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. ## Windows Setup **Requirements**: Visual Studio 2013 ### Pre-Build Steps Copy `.\windows\CommonSettings.props.example` to `.\windows\CommonSettings.props` By defaults Windows build requires `CUDA` and `cuDNN` libraries. Both can be disabled by adjusting build variables in `.\windows\CommonSettings.props`. Python support is disabled by default, but can be enabled via `.\windows\CommonSettings.props` as well. 3rd party dependencies required by Caffe are automatically resolved via NuGet. ### CUDA Download `CUDA Toolkit 7.5` [from nVidia website](https://developer.nvidia.com/cuda-toolkit). If you don't have CUDA installed, you can experiment with CPU_ONLY build. In `.\windows\CommonSettings.props` set `CpuOnlyBuild` to `true` and set `UseCuDNN` to `false`. ### cuDNN Download `cuDNN v4` or `cuDNN v5` [from nVidia website](https://developer.nvidia.com/cudnn). Unpack downloaded zip to %CUDA_PATH% (environment variable set by CUDA installer). Alternatively, you can unpack zip to any location and set `CuDnnPath` to point to this location in `.\windows\CommonSettings.props`. `CuDnnPath` defined in `.\windows\CommonSettings.props`. Also, you can disable cuDNN by setting `UseCuDNN` to `false` in the property file. ### Python To build Caffe Python wrapper set `PythonSupport` to `true` in `.\windows\CommonSettings.props`. Download Miniconda 2.7 64-bit Windows installer [from Miniconda website] (http://conda.pydata.org/miniconda.html). Install for all users and add Python to PATH (through installer). Run the following commands from elevated command prompt: ``` conda install --yes numpy scipy matplotlib scikit-image pip pip install protobuf ``` #### Remark After you have built solution with Python support, in order to use it you have to either: * set `PythonPath` environment variable to point to `<caffe_root>\Build\x64\Release\pycaffe`, or * copy folder `<caffe_root>\Build\x64\Release\pycaffe\caffe` under `<python_root>\lib\site-packages`. ### Matlab To build Caffe Matlab wrapper set `MatlabSupport` to `true` and `MatlabDir` to the root of your Matlab installation in `.\windows\CommonSettings.props`. #### Remark After you have built solution with Matlab support, in order to use it you have to: * add the generated `matcaffe` folder to Matlab search path, and * add `<caffe_root>\Build\x64\Release` to your system path. ### Build Now, you should be able to build `.\windows\Caffe.sln` ## 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} }