Caffe: Convolutional Architecture for Fast Feature Embedding

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Caffe provides multimedia scientists and practitioners with a clean and modiable 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 eciently on commodity architectures. Cae ts 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, Cae allows ex- perimentation and seamless switching among platforms for ease of development and deployment from prototyping ma- chines to cloud environments. Cae 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.

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