Caffe
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Introduction
Caffe is a deep learning framework focused on expression, speed, and modularity, developed by Berkeley AI Research.
Listed on
July 22, 2026
What is 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, allowing models and optimization to be defined by configuration without hard-coding. Caffe supports CPU and GPU switching with a single flag, making it suitable for both research and industry deployment.
How to use Caffe?
- To use Caffe, follow the installation instructions for your operating system (Ubuntu, Red Hat, OS X). Then, refer to the tutorial documentation and examples to define and train models. You can use the command line tools, Python interface, or C++ API to build and deploy models. The Model Zoo provides pre-trained models for quick start.
Core features of Caffe
- Expressive architecture: models and optimization defined by configuration
- Speed: processes over 60M images per day with a single NVIDIA K40 GPU
- Extensible code: active development with over 1,000 forks in the first year
- CPU/GPU switching with a single flag
- Community support: caffe-users group and GitHub
- Model Zoo with standard distribution format and trained models
- Comprehensive documentation and tutorials
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