Merge pull request #8 from sergeyk/gh-pages

small changes to front page

_layouts/default.html

@@ -17,7 +17,7 @@
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       <header>
         <h1 class="header"><a href="index.html">Caffe</a></h1>
-        <p class="header">Convolutional Architecture for Feature Embedding</p>
+        <p class="header">Convolutional Architecture for Fast Feature Embedding</p>
 
         <ul>
           <!--<li class="download"><a class="buttons" href="https://github.com/Yangqing/caffe/zipball/master">Download ZIP</a></li>
@@ -30,13 +30,12 @@ <h1 class="header"><a href="index.html">Caffe</a></h1>
       <section>
 
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       <footer>
-        <p><small>Hosted on <a href="http://pages.github.com">GitHub Pages</a> using the Dinky theme</small></p>
+        <p><small>Hosted on <a href="http://pages.github.com">GitHub Pages</a>.</small></p>
       </footer>
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     <!--[if !IE]><script>fixScale(document);</script><![endif]-->
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index.md

@@ -6,28 +6,32 @@ title: Caffe
 Welcome to Caffe
 ================
 
-Caffe is a framework for the recent convolutional neural networks algorithms, developed with speed in mind. It is written and maintained by [Yangqing Jia](http://www.eecs.berkeley.edu/~jiayq/) as a replacement of [decaf](http://decaf.berkeleyvision.org/), the python implementation of CNNs. Several [Berkeley vision group](http://ucbvlc.org/) members are actively contributing to the codebase.
+Caffe is a framework for convolutional neural network algorithms, developed with speed in mind.
+It is written and maintained by [Yangqing Jia](http://www.eecs.berkeley.edu/~jiayq/) as a replacement of [decaf](http://decaf.berkeleyvision.org/), Yangqing's first Python implementation of CNNs.
+Several [Berkeley vision group](http://ucbvlc.org/) members are actively contributing to the codebase.
 
 Caffe is currently released under [the UC Berkeley non-commercial license](license.html).
 
 Why Caffe?
 ----------
 
-Caffe aims to expand deep learning research by providing computer vision scientists easier access to state-of-the-art deep learning implementations. At the same time, caffe also aims for fast computation that fits industry needs, with codes in C++/Cuda providing maximum performance through efficient GPU computations. Being able to process more than **20 million images per day**\*, Caffe is currently the fastest GPU CNN implementation publicly available.
+Caffe aims to provide computer vision scientists with a **clean, modifiable implementation** of state-of-the-art deep learning algorithms.
+For example, network structure is easily specified in separate config files, with no mess of hard-coded parameters in the code.
 
-Caffe also provides **seamless switch between CPU and GPU**, which allows one to train models with fast GPUs, but to still have the flexibility of deploying models on cheaper, non-GPU clusters, with only one line of code necessary:
+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::set_mode(Caffe::CPU);
-```
+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 200 ms.
 
 Quick Links
 -----------
 
-* [Presentation](https://docs.google.com/presentation/d/1lzyXMRQFlOYE2Jy0lCNaqltpcCIKuRzKJxQ7vCuPRc8/edit?usp=sharing): Yangqing's presentation on Caffe at the Berkeley vision group meeting.
-* [Installation](installation.html): Instructions on installing Caffe, mainly with Ubuntu 12.04LTS.
-* [MNIST Demo](mnist.html): end-to-end training and testing on the MNIST data.
-* [Training ImageNet](imagenet.html): on how to train an ImageNet classifier.
+* [Presentation](https://docs.google.com/presentation/d/1lzyXMRQFlOYE2Jy0lCNaqltpcCIKuRzKJxQ7vCuPRc8/edit?usp=sharing): Presentation on Caffe at the UC Berkeley Vision Group meeting.
+* [Installation](installation.html): Instructions on installing Caffe (tested on Ubuntu 12.04, but works on Red Hat, OS X, etc.).
+* [MNIST Demo](mnist.html): example of end-to-end training and testing on the MNIST data.
+* [Training ImageNet](imagenet.html): tutorial on training an ImageNet classifier.
 * [Pretrained ImageNet](imagenet_pretrained.html): start running ImageNet classification in minutes.
 
 Citing Caffe
@@ -36,13 +40,17 @@ 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 Feature Embedding},
+       Title = { {Caffe}: An Open Source Convolutional Architecture for Fast Feature Embedding},
        Year  = {2013},
-       Howpublished = {\url{http://yangqing.github.io/caffe/}}
+       Howpublished = {\url{http://yangqing.github.io/caffe/}
+   }
+
+### Acknowledgements
 
-\* 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, and run on a single machine with Intel i5 processor and Tesla K20. Benchmark details coming soon.
+Yangqing would like to thank the NVidia Academic program for providing a K20 GPU.
+The Caffe Matlab wrapper is courtesy of [Dr. Ross Girshick](http://www.cs.berkeley.edu/~rbg/).
 
-\*\* Yangqing would like to thank the NVidia Academic program for providing a K20 GPU.
+---
 
-\*\*\* Matlab wrapper courtsy of [Dr Ross Girshick](http://www.cs.berkeley.edu/~rbg/).
+\*: 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.
+More benchmarks coming soon.

installation.md

@@ -6,7 +6,7 @@ title: Caffe
 Installation
 ================
 
-Here are some installation notes on various platforms. We have used Ubuntu 12.04 for development, so here describes the step-to-step guide on installing caffe with Ubuntu. You will be able to install Caffe on other platforms but you may need to tinker with paths in `Makefile.config` and maybe `Makefile` a little bit.
+We mostly used Ubuntu 12.04 for development, and here we describe the step-to-step guide on installing Caffe on Ubuntu. You will be able to install Caffe on other platforms, but you may need to minimally tinker with paths in `Makefile.config` and maybe the `Makefile` itself.
 
 Prerequisites
 -------------
@@ -42,4 +42,4 @@ Optionally, you can run `make distribute` to create a `build` directory that con
 
 To use Caffe with python, you will need to add `/path/to/caffe/python` or `/path/to/caffe/build/python` to your `PYTHONPATH`.
 
-Now that you have compiled Caffe, check out the [MNIST demo](mnist.html) and the pretrained [ImageNet example](imagenet.html). 
+Now that you have compiled Caffe, check out the [MNIST demo](mnist.html) and the pretrained [ImageNet example](imagenet.html).