Caffe: Convolutional architecture for fast feature embedding

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