Caffe

Caffe is a deep learning framework developed by Berkeley AI Research (BAIR) and community contributors. It is designed with expression, speed, and modularity in mind, enabling users to define models and optimization through configuration without hard-coding. Caffe supports seamless switching between CPU and GPU by setting a single flag, making it suitable for both research experimentation and industry deployment. Its extensible codebase has attracted over 1,000 developers who have contributed significant changes, keeping the framework aligned with state-of-the-art models and code. Caffe is known for its speed, capable of processing over 60 million images per day on a single NVIDIA K40 GPU, with inference times as low as 1 ms per image. The framework is widely used in academic research, startup prototypes, and large-scale industrial applications in vision, speech, and multimedia. Caffe is released under the BSD 2-Clause license and has a vibrant community accessible through the caffe-users group and GitHub.

Key Benefits

• Expressive architecture allows models and optimization to be defined by configuration without hard-coding
• Extensible codebase with active community contributions and over 1,000 forks
• High speed, processing over 60 million images per day on a single GPU
• Supports both CPU and GPU with a single flag switch