Caffe is an open-source deep learning framework developed by Berkeley AI Research and community contributors, with an emphasis on expressive model definitions, speed, and modular design. Network architectures and optimization settings are described through configuration rather than being hard-coded into an application. It is primarily intended for researchers running vision, speech, or multimedia experiments and for engineering teams turning trained models into deployed systems.
The framework offers command-line utilities, Python and C++ interfaces, tutorials, reference documentation, notebooks, and a model repository. A configuration flag can switch work between CPU and GPU, while deployment targets can include computing clusters and mobile devices. Using Caffe requires installing the framework and its dependencies on a supported computer, preparing data, and defining a network configuration; it is a development framework rather than a hosted conversational service.