Caffe

Caffe. Jia, Y., Shelhamer, E., Donahue, J., Karayev, S., Long, J., Girshick, R., Guadarrama, S., & Darrell, T. 2014.

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 mod-els efficiently on commodity architectures. Caffe fits indus-try and internet-scale media needs by CUDA GPU computa-tion, processing over 40 million images a day on a single K40 or Titan GPU (≈ 2.5 ms per image). By separating model representation from actual implementation, Caffe allows ex-perimentation and seamless switching among platforms for ease of development and deployment from prototyping ma-chines to cloud environments. Caffe is maintained and developed by the Berkeley Vi-sion and Learning Center (BVLC) with the help of an ac-tive community of contributors on GitHub. It powers on-going research projects, large-scale industrial applications, and startup prototypes in vision, speech, and multimedia.

@article{
 title = {Caffe},
 type = {article},
 year = {2014},
 keywords = {computation,computer vision,corresponding authors,machine learning,neural networks,open source,parallel},
 pages = {675-678},
 id = {60ce0358-7731-3095-9d00-487d4e39e50e},
 created = {2022-09-19T09:01:24.998Z},
 file_attached = {true},
 profile_id = {276016a7-2c9d-3507-8888-093db7c54774},
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 last_modified = {2022-09-19T09:02:20.444Z},
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 confirmed = {true},
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 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 mod-els efficiently on commodity architectures. Caffe fits indus-try and internet-scale media needs by CUDA GPU computa-tion, processing over 40 million images a day on a single K40 or Titan GPU (≈ 2.5 ms per image). By separating model representation from actual implementation, Caffe allows ex-perimentation and seamless switching among platforms for ease of development and deployment from prototyping ma-chines to cloud environments. Caffe is maintained and developed by the Berkeley Vi-sion and Learning Center (BVLC) with the help of an ac-tive community of contributors on GitHub. It powers on-going research projects, large-scale industrial applications, and startup prototypes in vision, speech, and multimedia.},
 bibtype = {article},
 author = {Jia, Yangqing and Shelhamer, Evan and Donahue, Jeff and Karayev, Sergey and Long, Jonathan and Girshick, Ross and Guadarrama, Sergio and Darrell, Trevor},
 doi = {10.1145/2647868.2654889}
}

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