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authorEvan Shelhamer <shelhamer@imaginarynumber.net>2014-01-22 02:37:51 (GMT)
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Created by Yangqing Jia, Department of EECS, University of California, Berkeley.
Maintained by the Berkeley Vision and Learning Center (BVLC).
+
+## Introduction
+
+Caffe aims to provide computer vision scientists with a **clean, modifiable
+implementation** of state-of-the-art deep learning algorithms. Network structure
+is easily specified in separate config files, with no mess of hard-coded
+parameters in the code. Python and Matlab wrappers are provided.
+
+At the same time, Caffe fits industry needs, with blazing fast C++/Cuda code for
+GPU computation. Caffe is currently the fastest GPU CNN implementation publicly
+available, and is able to process more than **20 million images per day** on a
+single Tesla K20 machine \*.
+
+Caffe also provides **seamless switching between CPU and GPU**, which allows one
+to train models with fast GPUs and then deploy them on non-GPU clusters with one
+line of code: `Caffe::set_mode(Caffe::CPU)`.
+
+Even in CPU mode, computing predictions on an image takes only 20 ms when images
+are processed in batch mode.
+
+* [Installation instructions](http://caffe.berkeleyvision.org/installation.html)
+* [Caffe presentation](https://docs.google.com/presentation/d/1lzyXMRQFlOYE2Jy0lCNaqltpcCIKuRzKJxQ7vCuPRc8/edit?usp=sharing) at the Berkeley Vision Group meeting
+
+\* When measured with the [SuperVision](http://www.image-net.org/challenges/LSVRC/2012/supervision.pdf) model that won the ImageNet Large Scale Visual Recognition Challenge 2012.
+
+## License
+
+Caffe is BSD 2-Clause licensed (refer to
+[LICENSE](http://caffe.berkeleyvision.org/license.html) for details).
+
+## Citing Caffe
+
+Please kindly cite Caffe in your publications if it helps your research:
+
+ @misc{Jia13caffe,
+ Author = {Yangqing Jia},
+ Title = { {Caffe}: An Open Source Convolutional Architecture for Fast Feature Embedding},
+ Year = {2013},
+ Howpublished = {\url{http://caffe.berkeleyvision.org/}
+ }