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authorEvan Shelhamer <>2014-01-22 02:37:51 (GMT)
committerEvan Shelhamer <>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](
+* [Caffe presentation]( at the Berkeley Vision Group meeting
+\* When measured with the [SuperVision]( model that won the ImageNet Large Scale Visual Recognition Challenge 2012.
+## License
+Caffe is BSD 2-Clause licensed (refer to
+[LICENSE]( 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{}
+ }