Caffe

Caffe is a deep learning framework developed by the Berkeley AI Research (BAIR) team, led by Yangqing Jia during his PhD at UC Berkeley. It is designed to facilitate deep learning model development with a focus on expression, speed, and modularity, allowing users to define models and optimizations through configuration files instead of hard coding them. This flexibility is particularly useful in adapting to different project requirements.
Caffe supports training on both CPU and GPU, making it suitable for a variety of hardware setups. This adaptability enhances its usability in multiple environments, ranging from personal computers to large-scale data centers.
The framework has gained significant popularity within the machine learning community, having been forked by over 1,000 developers in its first year of release. Caffe is particularly effective for image processing tasks and is extensively used in academic research as well as industrial applications, especially in fields involving vision, speech, and multimedia.
Caffe is open-source and is released under the BSD 2-Clause license, which encourages contributions and modifications from the community. It includes comprehensive documentation, tutorials, and community support resources to assist users in getting started and troubleshooting.
With its efficient architecture and community backing, Caffe remains a relevant choice for practitioners and researchers aiming to leverage deep learning technologies.