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authorEvan Shelhamer <shelhamer@imaginarynumber.net>2015-02-17 05:41:24 (GMT)
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# Caffe
-Caffe is a deep learning framework developed with cleanliness, readability, and speed in mind.<br />
-Consult the [project website](http://caffe.berkeleyvision.org) for all documentation.
+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
-Please ask usage questions and how to model different tasks on the [caffe-users mailing list](https://groups.google.com/forum/#!forum/caffe-users).
+- [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.
+
+Please join the [caffe-users group](https://groups.google.com/forum/#!forum/caffe-users) 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}
+ }