Abstract

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. Caffe fits industry and internet-scale media needs by CUDA GPU computation, processing over 40 million images a day on a single K40 or Titan GPU (approx 2 ms per image). By separating model representation from actual implementation, Caffe allows experimentation and seamless switching among platforms for ease of development and deployment from prototyping machines to cloud environments.

Keywords

Computer scienceDeep learningPython (programming language)CUDAArtificial intelligenceTitan (rocket family)Convolutional neural networkSoftware deploymentCloud computingArtificial neural networkMATLABComputer architectureSoftware engineeringParallel computingOperating systemEngineering

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Publication Info

Year
2014
Type
article
Pages
675-678
Citations
11119
Access
Closed

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Yangqing Jia, Evan Shelhamer, Jeff Donahue et al. (2014). Caffe. , 675-678. https://doi.org/10.1145/2647868.2654889

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DOI
10.1145/2647868.2654889