  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 ($\approx$ 2.5 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. Caffe is maintained and developed by the
Berkeley Vision and Learning Center (BVLC) with the help of an active community
of contributors on GitHub. It powers ongoing research projects, large-scale
industrial applications, and startup prototypes in vision, speech, and
multimedia.
