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Caffe (convolutional architecture for fast feature embedding) is a deep learning framework, originally developed at university of california, berkeley A fast open framework for deep learning It is open source, under a bsd license
The bair/bvlc reference models are released for unrestricted use Let's review the progress culminating in our 1.0: Please cite caffe in your publications if it helps your research:
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Expressive architecture encourages application and innovation In this article, we will explore various applications and uses of caffe, delve into its architecture and components, and discuss its proficiency through integration and deployment with various tools and managers. Caffe was developed (and is currently maintained) by berkeley ai research and community contributors under the bsd license It is written in c++ and its first stable release date was april 18, 2017.
Caffe uses a shared copyright model Each contributor holds copyright over their contributions to caffe The project versioning records all such contribution and copyright details. This release marks the convergence of development into a stable, reference release of the framework and a shift into maintenance mode
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