Caffe

Caffe is a powerful deep learning framework developed by Berkeley AI Research (BAIR) with a focus on expression, speed, and modularity. It allows developers and researchers to define complex models and optimizations through configuration files rather than hard-coding, promoting rapid prototyping and innovation. A key feature is its seamless ability to switch between CPU and GPU processing with a single flag, enabling users to train models on powerful GPU machines and deploy them on a wide range of devices, from commodity clusters to mobile phones. Caffe is renowned for its performance, capable of processing over 60 million images per day on a single NVIDIA K40 GPU, making it one of the fastest convnet implementations available for both research and industrial-scale deployment. Its extensible codebase has fostered a vibrant open-source community, leading to continuous improvements and state-of-the-art models. Use cases span academic research, startup prototypes, and large-scale applications in image classification, object detection (R-CNN), feature extraction, and more. The framework also includes a 'Model Zoo' with pre-trained models to accelerate development.