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Caffe (convolutional architecture for fast feature embedding) is a deep learning framework, originally developed at university of california, berkeley It is developed by the berkeley vision and learning center (bvlc) and community contributors. It is open source, under a bsd license
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Caffe uses a shared copyright model Efficiency is a major hallmark of caffe, and stands as a major design objective of caffe Each contributor holds copyright over their contributions to caffe
The project versioning records all such contribution and copyright details.
Check out our web image classification demo Expressive architecture encourages application and innovation Initially released in 2014 by yangqing jia during his ph.d Since its release, caffe has gained significant traction in both academia and industry.
Caffe is a deep learning framework made with expression, speed, and modularity in mind It is developed by berkeley ai research (bair)/the berkeley vision and learning center (bvlc) and community contributors. Caffe is an open source deep learning framework that’s focused on expression, speed and modularity It’s got an expressive architecture that encourages application and innovation, and extensible code that’s great for active development.
The bair/bvlc reference models are released for unrestricted use
Please cite caffe in your publications if it helps your research:
