Hysen Labscaffe
Caffe: a fast open framework for deep learning.
Caffe, the deep learning framework from Berkeley
Caffe is a deep learning framework built around expression, speed, and modularity, developed by Berkeley AI Research with community contributors.
The framework pitch
The README opens with Caffe as a deep learning framework made with expression, speed, and modularity in mind, developed by Berkeley AI Research, the Berkeley Vision and Learning Center, and community contributors. GitHub's one line calls it a fast open framework for deep learning, and the homepage sits at caffe.berkeleyvision.org. GitHub lists C++ as the primary language for the repository, so the README and the metadata describe the same framework from two angles.
Custom distributions
Two custom distributions are listed. Intel Caffe is optimized for CPU and multi node use, in particular on Intel Xeon processors, and OpenCL Caffe targets AMD or Intel devices. The choice lets people match the build to their hardware, which is the point of a custom distribution section in the first place.
Where the community talks
Users are pointed to the caffe-users group or the gitter chat for questions, while framework development discussion and thorough bug reports land on Issues. It is a small support surface, but a documented one, and the same section credits community contributors alongside the labs that started the project. The homepage at caffe.berkeleyvision.org rounds out the project's presence beyond GitHub.
License and citation
Caffe is released under the BSD 2 Clause license, and the reference models are released for unrestricted use. A citation entry for the paper, Caffe: Convolutional Architecture for Fast Feature Embedding, carries a 2014 arXiv preprint listing and names the authors, including Jia, Shelhamer, Donahue, Girshick, and Darrell. The citation is offered as the reference to use when the framework appears in a publication.
Community notes