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| # Caffe | ||
| +Forked by paeng (khpaeng@kaist.ac.kr) | ||
| Caffe is a deep learning framework made with expression, speed, and modularity in mind. | ||
| It is developed by the Berkeley Vision and Learning Center ([BVLC](http://bvlc.eecs.berkeley.edu)) and community contributors. | ||
| -Check out the [project site](http://caffe.berkeleyvision.org) for all the details like | ||
| - | ||
| -- [DIY Deep Learning for Vision with Caffe](https://docs.google.com/presentation/d/1UeKXVgRvvxg9OUdh_UiC5G71UMscNPlvArsWER41PsU/edit#slide=id.p) | ||
| -- [Tutorial Documentation](http://caffe.berkeleyvision.org/tutorial/) | ||
| -- [BVLC reference models](http://caffe.berkeleyvision.org/model_zoo.html) and the [community model zoo](https://github.com/BVLC/caffe/wiki/Model-Zoo) | ||
| -- [Installation instructions](http://caffe.berkeleyvision.org/installation.html) | ||
| - | ||
| -and step-by-step examples. | ||
| - | ||
| -[](https://gitter.im/BVLC/caffe?utm_source=badge&utm_medium=badge&utm_campaign=pr-badge&utm_content=badge) | ||
| - | ||
| -Please join the [caffe-users group](https://groups.google.com/forum/#!forum/caffe-users) or [gitter chat](https://gitter.im/BVLC/caffe) to ask questions and talk about methods and models. | ||
| -Framework development discussions and thorough bug reports are collected on [Issues](https://github.com/BVLC/caffe/issues). | ||
| - | ||
| -Happy brewing! | ||
| - | ||
| -## License and Citation | ||
| - | ||
| -Caffe is released under the [BSD 2-Clause license](https://github.com/BVLC/caffe/blob/master/LICENSE). | ||
| -The BVLC reference models are released for unrestricted use. | ||
| - | ||
| -Please cite Caffe in your publications if it helps your research: | ||
| - | ||
| - @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} | ||
| - } |