Caffe is a deep learning framework made with expression, speed, and modularity in mind. It is developed by Berkeley AI Research (BAIR) and by community contributors.
Deep Learning Software
• Expressive architecture
• Extensible code
Small (<50 employees), Medium (50 to 1000 employees), Enterprise (>1001 employees)
Caffe is a deep learning framework made with expression, speed, and modularity in mind. It is developed by Berkeley AI Research (BAIR) and by community contributors. Yangqing Jia created the project during his PhD at UC Berkeley. Caffe is released under the BSD 2-Clause license. The BAIR/BVLC reference models are released for unrestricted use. Caffe is installed and run on Ubuntu 16.04–12.04, OS X 10.11–10.8, and through Docker and AWS. Caffe requires the CUDA nvcc compiler to compile its GPU code and CUDA driver for GPU operation. Caffe promotes expressive architecture which encourages application and innovation. Models and optimization are defined by configuration without hard-coding. Users can switch between CPU and GPU by setting a single flag to train on a GPU machine then deploy to commodity clusters or mobile devices. Caffe provides extensible code that fosters active development. In Caffe’s first year, it has been forked by over 1,000 developers and had many significant changes contributed back. Thanks to these contributors the framework tracks the state-of-the-art in both code and models. In addition, speed makes Caffe perfect for research experiments and industry deployment. Caffe can process over 60M images per day with a single NVIDIA K40 GPU*. That’s 1 ms/image for inference and 4 ms/image for learning and more recent library versions and hardware are faster still. Caffe is among the fastest convnet implementations available. Caffe has a Community. Caffe already powers academic research projects, startup prototypes, and even large-scale industrial applications in vision, speech, and multimedia. Join Caffe’s community of brewers on the caffe-users group and Github.