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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. Expressive architecture encourages application and innovation. Models and optimization are defined by configuration without hard-coding. Switch between CPU and GPU by setting a single flag to train on a GPU machine then deploy to commodity clusters or mobile devices. Extensible code 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.
Features
- Speed makes Caffe perfect for research experiments and industry deployment
- Caffe can process over 60M images per day with a single NVIDIA K40 GPU
- Caffe already powers academic research projects, startup prototypes, and even large-scale industrial applications in vision, speech, and multimedia
- Expressive architecture encourages application and innovation
- Extensible code fosters active development
- Instant recognition with a pre-trained model
Project Samples
