Skip to content
forked from naibaf7/caffe

Caffe: a fast open framework for deep learning. With OpenCL and CUDA support.

License

Notifications You must be signed in to change notification settings

codebots-ltd/caffe

 
 

Repository files navigation

Caffe

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 https://gitter.im/BVLC/caffe

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!

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:

@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}
}

Additional Notes

This fork of Caffe contains an OpenCL backend and additional layers for fast image segmentation. This work is partially supported by:

  • AMD
  • HHMI Janelia
  • UZH, INI
  • ETH Zurich

For a C++ frontend and models to use for image segmentation with this fork, see:

OpenCL Backend

The backend is supposed to work with all vendors. Note however there may be problems with libOpenCL.so provided by nVidia. It is therefore recommended to install another OpenCL implementation after installing nVidia drivers. Possibilities are:

  • Intel OpenCL, recommended if you have an Intel CPU along the nVidia GPU.
  • AMD APP SDK (OpenCL), recommended if you have an AMD GPU or CPU.

About

Caffe: a fast open framework for deep learning. With OpenCL and CUDA support.

Resources

License

Stars

Watchers

Forks

Packages

No packages published

Languages

  • C++ 76.2%
  • Cuda 8.0%
  • Python 7.6%
  • CMake 2.8%
  • C 2.0%
  • Protocol Buffer 1.3%
  • Other 2.1%