Caffe
Caffe is a robust deep learning framework designed with speed, expression, and modularity at its core. Developed by Berkeley AI Research and led by Yangqing Jia, it streamlines model configuration and supports seamless switching between CPU and GPU. With the capability to process over 60 million images daily, Caffe is ideal for both research and industrial applications.
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Top Caffe Features
- Deep learning framework by BAIR
- Created during PhD at UC Berkeley
- BSD 2-Clause license
- Expressive architecture for innovation
- Configuration-based model optimization
- Easy CPU to GPU switching
- High-speed image processing
- Over 60M images/day capability
- Fastest convnet implementations
- Extensible code for contributions
- Active open-source community
- Forked by over 1
- 000 developers
- Supports research and industry applications
- Comprehensive user support group
- Extensive documentation and resources
- Web image classification demo
- Contributions tracked by Google Scholar
- Collaborations with major tech companies
- Regular updates and improvements.
