Caffe is a deep learning framework developed by Berkeley AI Research (BAIR), built with expression, speed, and modularity as explicit design goals. It handles neural network design, training, and deployment for computer vision and related tasks – image classification, model fine-tuning, and feature extraction – and has processed over 60 million images per day on GPU hardware in production use.
Predating the current AI boom by nearly a decade and still being actively maintained is the notable part – many frameworks from Caffe’s era (2013-2015) have since gone fully dormant as TensorFlow and PyTorch captured most new development, but Caffe continues evolving, including ongoing work toward ONNX compatibility to interoperate with newer tools rather than existing in isolation. For performance-sensitive, speed-and-modularity-focused use cases specifically, that continued relevance is a real technical argument, not just legacy inertia.
This is an open-source library maintained under the BSD 2-Clause license, not a hosted product – access it via GitHub and integrate it into your own ML infrastructure. Over 1,000 developers have contributed to the project historically. Free and open-source with no signup required.
