This branch of Caffe extends BVLC-led Caffe by adding Windows support and other functionalities commonly used by Microsoft's researchers, such as managed-code wrapper, Faster-RCNN, R-FCN, etc.
Update: this branch is not actively maintained. Please checkout this for more active Windows support.
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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
- DIY Deep Learning for Vision with Caffe
- Tutorial Documentation
- BVLC reference models and the community model zoo
- Installation instructions
and step-by-step examples.
Requirements: Visual Studio 2013
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.
Download CUDA Toolkit 7.5 from nVidia website.
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.
Download cuDNN v4 or cuDNN v5 from nVidia website.
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.
To build Caffe Python wrapper set PythonSupport to true in .\windows\CommonSettings.props.
Download Miniconda 2.7 64-bit Windows installer from Miniconda website.
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
After you have built solution with Python support, in order to use it you have to either:
- set
PythonPathenvironment variable to point to<caffe_root>\Build\x64\Release\pycaffe, or - copy folder
<caffe_root>\Build\x64\Release\pycaffe\caffeunder<python_root>\lib\site-packages.
To build Caffe Matlab wrapper set MatlabSupport to true and MatlabDir to the root of your Matlab installation in .\windows\CommonSettings.props.
After you have built solution with Matlab support, in order to use it you have to:
- add the generated
matcaffefolder to Matlab search path, and - add
<caffe_root>\Build\x64\Releaseto your system path.
Now, you should be able to build .\windows\Caffe.sln
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:
@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}
}
Microsoft/caffe + weiliu89/caffe/examples/ssd
VGG_VOC0712_SSD_300x300_iter_120000.caffemodel Download