Caffe Deep Neural Network Framework

A Caffe Deep Neural Network Framework is a neural network framework developed by Berkeley's Vision and Learning Center.

• • …
• Counter-Example(s):
  • Torch Framework.
  • TensorFlow Framework.
• See: Theano.

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References

• http://caffe.berkeleyvision.org/

2015

• http://caffe.berkeleyvision.org/tutorial/
  • QUOTE: In one sip, Caffe is brewed for
    • Expression: models and optimizations are defined as plaintext schemas instead of code.
    • Speed: for research and industry alike speed is crucial for state-of-the-art models and massive data.
    • Modularity: new tasks and settings require flexibility and extension.
    • Openness: scientific and applied progress call for common code, reference models, and reproducibility.
    • Community: academic research, startup prototypes, and industrial applications all share strength by joint discussion and development in a BSD-2 project.

2014

• (Jia et al., 2014) ⇒ Yangqing Jia, Evan Shelhamer, Jeff Donahue, Sergey Karayev, Jonathan Long, Ross Girshick, Sergio Guadarrama, and Trevor Darrell. (2014). “Caffe: Convolutional Architecture for Fast Feature Embedding.” In: Proceedings of the ACM International Conference on Multimedia. ISBN:978-1-4503-3063-3 doi:10.1145/2647868.2654889
  • QUOTE: Caffe provides multimedia scientists and practitioners with a clean and modifiable framework for state-of-the-art deep learning algorithms and a collection of reference models. The framework is a BSD-licensed C++ library with Python and MATLAB bindings for training and deploying general-purpose convolutional neural networks and other deep models efficiently on commodity architectures.

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