Caffe

Caffe is a deep learning framework designed for speed, modularity, and expressiveness. It allows users to define models and optimization configurations without hard-coding, making it easy to switch between CPU and GPU for training and deployment. With Caffe, users can process over 60M images per day using a single GPU, making it one of the fastest convnet implementations available. Caffe is extensively used in academic research projects, startup prototypes, and large-scale industrial applications in areas such as vision, speech, and multimedia. It has an active development community with over 1,000 developers contributing to its code and models. Caffe offers extensive documentation, including tutorials, installation instructions, API documentation, and benchmarking comparisons. It also provides notebook and command line examples for various tasks, such as image classification, fine-tuning, feature extraction, and network embedding. Researchers are encouraged to cite Caffe in their publications, and users can join the caffe-users group for support and discussions. The development of Caffe is supported by donations and grants from organizations like NVIDIA, A9, Amazon Web Services, and guidance from researchers at Berkeley AI Research.