Caffe | A Fast Open Framework For Deep Learning

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Caffe

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Caffe is a deep learning framework made with expression, speed, and modularity in mind. It is developed by Berkeley AI Research (

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)/The Berkeley Vision and Learning Center (BVLC) and community contributors.

Check out the 

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 for all the details like

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   and the 
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and step-by-step examples.

Custom distributions

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   (Optimized for CPU and support for multi-node), in particular Intel® Xeon processors.
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   e.g. for AMD or Intel devices.
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Community

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Please join the 

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 or 

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 to ask questions and talk about methods and models. Framework development discussions and thorough bug reports are collected on 

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.

Happy brewing!

License and Citation

Caffe is released under the 

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. The BAIR/BVLC reference models are released for unrestricted use.

Please cite Caffe in your publications if it helps your research:

Quote

@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}
}

 

GitHub:

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