BVLC · shelhamer · Mar 18, 2014 · Mar 18, 2014 · Mar 18, 2014 · sguada
diff --git a/.gitignore b/.gitignore
@@ -55,4 +55,5 @@ data/*
 examples/*
 
 # Don't version the generated documentation
-docs/_site
+docs/_site
+_site
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@@ -0,0 +1,16 @@
+---
+layout: default
+---
+
+# Pre-trained models
+
+[BVLC](http://bvlc.eecs.berkeley.edu) aims to provide a variety of high quality pre-trained models.
+Note that unlike Caffe itself, these models are licensed for **academic research / non-commercial use only**.
+If you have any questions, please get in touch with us.
+
+This page will be updated as more models become available.
+
+### ImageNet
+Our reference implementation of the AlexNet model trained on ILSVRC-2010 can be downloaded (232.57MB) by running `models/get_caffe_reference_imagenet_model.sh` from the Caffe root directory.
+
+Additionally, you will probably eventually need some auxiliary data (mean image, synset list, etc.): run `data/ilsvrc12/get_ilsvrc_aux.sh` from the root directory to obtain it.
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diff --git a/docs/index.md b/docs/index.md
@@ -1,48 +1,47 @@
 ---
 layout: default
-title: Caffe
 ---
-
-Welcome to Caffe
-================
+# Welcome to Caffe
 
 Caffe is a framework for convolutional neural network algorithms, developed with speed in mind.
-It was created by [Yangqing Jia](http://www.eecs.berkeley.edu/~jiayq/) as a replacement of [decaf](http://decaf.berkeleyvision.org/), Yangqing's earlier Python implementation of CNNs.
-It is maintained by the [Berkeley Vision and Learning Center](http://bvlc.eecs.berkeley.edu) and several Berkeley vision group members are actively contributing to the codebase.
-
-Caffe is released under [the BSD 2-Clause license](license.html).
+It was created by [Yangqing Jia](http://daggerfs.com), and is in active development by the [Berkeley Vision and Learning Center](http://bvlc.eecs.berkeley.edu).
+Caffe is released under [the BSD 2-Clause license](https://github.com/BVLC/caffe/blob/master/LICENSE).
 
-Decaf, the big brother of Caffe, has a cool [demo](http://decaf.berkeleyvision.org). Caffe's own demo will come soon.
+<!-- BVLC hosts a quick [classification demo](http://demo.caffe.berkeleyvision.org/) using Caffe. -->
 
-Why Caffe?
-----------
+## Why Caffe?
 
-Caffe aims to provide computer vision scientists with a **clean, modifiable implementation** of state-of-the-art deep learning algorithms.
+Caffe aims to provide computer vision scientists and practicioners with a **clean and 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.
 
 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.
 
-Quick Links
------------
+## Documentation
+
+* [Introductory slides](/caffe-presentation.pdf): slides about the Caffe architecture, *updated 03/14*.
+* [Installation](/installation.html): Instructions on installing Caffe (works on Ubuntu, Red Hat, OS X).
+* [Pre-trained models](/getting_pretrained_models.html): BVLC provides some pre-trained models for academic / non-commercial use.
+* [Development](/development.html): Guidelines for development and contributing to Caffe.
+
+### Examples
+
+* [LeNet / MNIST Demo](/mnist.html): end-to-end training and testing of LeNet on MNIST.
+* [CIFAR-10 Demo](/cifar10.html): training and testing on the CIFAR-10 data.
+* [Training ImageNet](/imagenet_training.html): end-to-end training of an ImageNet classifier.
+* [Running Pretrained ImageNet \[notebook\]][pretrained_imagenet]: run classification with the pretrained ImageNet model using the Python interface.
+* [Running Detection \[notebook\]][imagenet_detection]: run a pretrained model as a detector.
+* [Visualizing Features and Filters \[notebook\]][visualizing_filters]: trained filters and an example image, viewed layer-by-layer.
 
-* [Presentation](caffe-presentation.pdf): The Caffe presentation, *updated 03/14*.
-* [Installation](installation.html): Instructions on installing Caffe (tested on Ubuntu 12.04, but works on Red Hat, OS X, etc.).
-* [Development](development.html): Guidelines for development and contributing to Caffe.
-* [LeNet / MNIST Demo](mnist.html): example of end-to-end training and testing of LeNet on MNIST.
-* [CIFAR-10 Demo](cifar10.html): example of training and testing on the CIFAR-10 data.
-* [Training ImageNet](imagenet_training.html): tutorial on end-to-end training of an ImageNet classifier.
-* [Running Pretrained ImageNet](imagenet_pretrained.html): simply runs in Python!
-* [Running Detection](imagenet_detection.html): run a pretrained model as a detector.
-* [Visualizing Features and Filters](lena_imagenet.html): Lena viewed layer-by-layer.
+[pretrained_imagenet]:  http://nbviewer.ipython.org/github/BVLC/caffe/blob/master/examples/imagenet_pretrained.ipynb
+[imagenet_detection]:   http://nbviewer.ipython.org/github/BVLC/caffe/blob/master/examples/selective_search_demo.ipynb
+[visualizing_filters]:  http://nbviewer.ipython.org/github/BVLC/caffe/blob/master/examples/filter_visualization.ipynb
 
+## Citing Caffe
 
-Citing Caffe
-------------
 Please kindly cite Caffe in your publications if it helps your research:
 
     @misc{Jia13caffe,
@@ -54,12 +53,11 @@ Please kindly cite Caffe in your publications if it helps your research:
 
 ### Acknowledgements
 
-Yangqing would like to thank the NVidia Academic program for providing a K20 GPU.
+Yangqing would like to thank the NVidia Academic program for providing K20 GPUs.
 The Caffe Matlab wrapper is courtesy of [Dr. Ross Girshick](http://www.cs.berkeley.edu/~rbg/).
 The detection module (`power_wrapper`) is courtesy of [Sergey Karayev](http://sergeykarayev.com/).
 Our thanks also go to [Jeff Donahue](http://jeffdonahue.com/) and [Oriol Vinyals](http://www1.icsi.berkeley.edu/~vinyals/) for various discussions along the journey.
 
 ---
 
 \*: 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.