Caffe | Deep Learning Framework

Discover Caffe, the speedy & versatile deep learning framework by BAIR & community! Switch effortlessly between CPU & GPU, perfect for research & deployment. Used in academia, startups, & industry. Learn more at https://caffe.berkeleyvision.org/ 🚀🧠 #AI #DeepLearning" #caffedeeplearning #BAIR

Caffe is a deep learning framework developed by BAIR and the community.
Yangqing Jia created Caffe during his PhD at UC Berkeley.
Caffe is known for its expressive architecture, speed, and modularity.
It can switch between CPU and GPU by setting a single flag.
Caffe has been forked by over 1,000 developers in its first year.
The framework tracks state-of-the-art developments in both code and models.
Caffe is optimized for research experiments and industry deployment.
It can process over 60M images per day with a single NVIDIA K40 GPU.
Caffe is used in academic research, startup prototypes, and industrial applications.
Documentation includes tutorials, installation instructions, model zoo, API documentation, benchmarking, and examples.
Caffe provides guidelines for development and contribution.
Caffe has a community where users can discuss methods and models.
The BAIR team acknowledges NVIDIA, A9, and Amazon Web Services for their support.
Open-source contributors play a significant role in Caffe's development.
Yangqing Jia extends personal thanks to the NVIDIA Academic program, Oriol Vinyals, and Trevor Darrell.