Caffe

Caffe is a deep learning framework developed by Berkeley AI Research (BAIR) and community contributors, with a focus on expression, speed, and modularity. Its architecture emphasizes flexibility, allowing models and optimization to be defined by configuration without hard-coding. Caffe supports seamless switching between CPU and GPU for training and deployment.

With an extensible codebase, it encourages active development and has attracted a large community of contributors. Caffe boasts impressive speed, capable of processing over 60M images per day with a single NVIDIA K40 GPU, making it suitable for both research experiments and industry deployment.