Caffe: A Deep Learning Framework for Fast Feature Embedding
Caffe is a deep learning framework designed with expression, speed, and modularity at its core. Developed by Berkeley AI Research (BAIR) and a community of contributors, it was initiated by Yangqing Jia during his PhD at UC Berkeley. The framework is open-source and is released under the BSD 2-Clause license.
- Expressive Architecture: Caffe's architecture promotes innovation and application. It allows models and optimization to be defined by configuration, eliminating the need for hard-coding.
- Switch Between CPU and GPU: Users can easily transition between CPU and GPU by toggling a single flag. This facilitates training on a GPU machine and then deploying to various platforms, from commodity clusters to mobile devices.
- Extensibility: The code is designed to be extensible, encouraging active development. In its first year alone, Caffe was forked by over 1,000 developers, with many significant changes contributed back to the main framework.
- Speed: Caffe is renowned for its speed, making it ideal for both research experiments and industrial deployment. With a single NVIDIA K40 GPU, it can process over 60M images per day. This translates to 1 ms/image for inference and 4 ms/image for learning.
Ideal Use Case:
Caffe is perfect for academic research projects, startup prototypes, and large-scale industrial applications in fields such as vision, speech, and multimedia. Its speed and flexibility make it a go-to choice for those looking to leverage deep learning in their projects.
Why use Caffe:
- Community Support: Caffe has a robust community of brewers on the caffe-users group and Github, ensuring continuous support and development.
- Documentation: From DIY deep learning tutorials to detailed API documentation, Caffe offers extensive resources to help users get started and make the most of the framework.
- Model Zoo: BAIR suggests a standard distribution format for Caffe models and provides trained models for users to leverage.
Caffe is an open-source deep learning framework developed by BAIR, emphasizing expression, speed, and modularity. With features like an expressive architecture, extensibility, and unmatched speed, it caters to a wide range of applications, from academic research to industrial deployment.