AI Deep Learning Framework
What makes Caffe a good choice for deep learning projects?
Caffe offers an expressive architecture, speed, and modularity. Models and optimization are defined by configuration rather than hard-coding, enabling rapid experimentation and easy switching between CPU and GPU with a single flag. It scales from single machines to clusters and even mobile devices. Caffe can process over 60 million images per day on a single NVIDIA K40 GPU, delivering fast inference (about 1 ms per image) and learning (about 4 ms per image) on newer hardware. It is released under the BSD 2-Clause license.
Where can I find the Caffe source code and under what license is it released?
Caffe is hosted on GitHub (View On GitHub). It is released under the BSD 2-Clause license.
How can I participate in the Caffe community?
Join the caffe-users group to ask questions and discuss methods and models. For framework development discussions and bug reports, use the Issues page. You can also follow the GitHub project pulse to stay updated on recent activity and contributions. If you’re new, read the developing & contributing guide for how to get involved.
What learning and usage resources are available for Caffe?
- Tutorials: DIY Deep Learning for Vision with Caffe and Caffe in a Day
- Tutorial documentation: practical guide and framework reference
- API documentation: auto-generated from code comments
- Notebook examples: Image Classification and Feature Visualization, LeNet MNIST Tutorial, CIFAR-10 Tutorial, Fine-tuning for Style Recognition, and more
- Official examples: ImageNet tutorial, CaffeNet C++ classification example, and web demo
- Model Zoo: trained models and a standard distribution format for Caffe models
- Benchmarking: comparisons of inference and learning across networks and GPUs
How should I cite Caffe in publications?
Please cite the following paper:
@article{jia2014caffe,
Author = {Jia, Yangqing and Shelhamer, Evan and Donahue, Jeff and Karayev, Sergey and Long, Jonathan and Girshick, Ross and Guadarrama, Sergio and Darrell, Trevor},
Journal = {arXiv preprint arXiv:1408.5093},
Title = {Caffe: Convolutional Architecture for Fast Feature Embedding},
Year = {2014}
}
If you publish work where Caffe helped your research, you’re encouraged to cite the framework for tracking by Google Scholar.
What is the Model Zoo?
BAIR provides a standard distribution format for Caffe models and offers trained models through the Model Zoo.
Who created and maintains Caffe?
Caffe was created by Yangqing Jia and developed by Berkeley AI Research (BAIR) and community contributors. Lead Developer: Evan Shelhamer. The project is released under the BSD 2-Clause license, with ongoing contributions from BAIR and the broader community.
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