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Caffe

Yangqing Jia; Evan Shelhamer; Jeff Donahue; Sergey Karayev; Jonathan Long; Ross Girshick; Sergio Guadarrama; Trevor Darrell · OpenAlex · 2014

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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.

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APA 7

Jia, Y, Shelhamer, E, Donahue, J, Karayev, S, Long, J, Girshick, R, Guadarrama, S, & Darrell, T. (2014). Caffe. https://doi.org/10.1145/2647868.2654889

MLA

Jia, Yangqing, et al. "Caffe." 2014. https://doi.org/10.1145/2647868.2654889.

Chicago

Jia, Yangqing, Evan Shelhamer, Jeff Donahue, Sergey Karayev, Jonathan Long, Ross Girshick, Sergio Guadarrama, and Trevor Darrell. 2014. "Caffe.". https://doi.org/10.1145/2647868.2654889.

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Jia, Y. et al. 2014, Caffe, OpenAlex, available at: https://doi.org/10.1145/2647868.2654889 [Accessed 7 Aug. 2026].

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Title
Caffe
Author / contributors
Yangqing Jia; Evan Shelhamer; Jeff Donahue; Sergey Karayev; Jonathan Long; Ross Girshick; Sergio Guadarrama; Trevor Darrell
Publisher
OpenAlex
Publication year
2014
Language
English

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