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back-merging [docs] changes and web demo [example] addition; updating
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net_surgery example to new format

Conflicts:
	docs/getting_pretrained_models.md
	docs/index.md
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sergeyk committed Jul 12, 2014
2 parents e07a2c0 + dd292da commit 9c61462
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1 change: 1 addition & 0 deletions .gitignore
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# Generated documentation
docs/_site
docs/gathered
_site

# Sublime Text settings
1 change: 1 addition & 0 deletions data/ilsvrc12/get_ilsvrc_aux.sh
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# This script downloads the imagenet example auxiliary files including:
# - the ilsvrc12 image mean, binaryproto
# - synset ids and words
# - Python pickle-format data of ImageNet graph structure and relative infogain
# - the training splits with labels

DIR="$( cd "$(dirname "$0")" ; pwd -P )"
6 changes: 4 additions & 2 deletions docs/README.md
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To generate stuff you can paste in an .md page from an IPython notebook, run
# Caffe Documentation

ipython nbconvert --to markdown <notebook_file>
To generate the documentation, run `$CAFFE_ROOT/scripts/build_docs.sh`.

To push your changes to the documentation to the gh-pages branch of your or the BVLC repo, run `$CAFFE_ROOT/scripts/deploy_docs.sh <repo_name>`.
29 changes: 11 additions & 18 deletions docs/_layouts/default.html
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Caffe {% if page contains 'title' %}| {{ page.title }}{% endif %}
</title>

<link rel="stylesheet" href="stylesheets/reset.css">
<link rel="stylesheet" href="stylesheets/styles.css">
<link rel="stylesheet" href="stylesheets/pygment_trac.css">
<script src="javascripts/scale.fix.js"></script>
<link rel="stylesheet" href="/stylesheets/reset.css">
<link rel="stylesheet" href="/stylesheets/styles.css">
<link rel="stylesheet" href="/stylesheets/pygment_trac.css">

<meta name="viewport" content="width=device-width, initial-scale=1, user-scalable=no">
<!--[if lt IE 9]>
<script src="//html5shiv.googlecode.com/svn/trunk/html5.js"></script>
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</script>
<div class="wrapper">
<header>
<h1 class="header"><a href="index.html">Caffe</a></h1>
<!-- <p class="header">Convolutional Architecture for Fast Feature Embedding</p> -->

<h1 class="header"><a href="/">Caffe</a></h1>
<p class="header">
Deep learning framework developed by <a class="header name" href="http://daggerfs.com/">Yangqing Jia</a> / <a class="header name" href="http://bvlc.eecs.berkeley.edu/">BVLC</a>
</p>
<ul>
<!--<li class="download"><a class="buttons" href="https://github.com/BVLC/caffe/zipball/master">Download ZIP</a></li>
<li class="download"><a class="buttons" href="https://github.com/BVLC/caffe/tarball/master">Download TAR</a></li>-->
<li><a class="buttons github" href="https://github.com/BVLC/caffe">View On GitHub</a></li>
<li>
<a class="buttons github" href="https://github.com/BVLC/caffe">View On GitHub</a>
</li>
</ul>
<p class="header">Maintained by<br><a class="header name" href="http://bvlc.eecs.berkeley.edu/">BVLC</a></p>
<p class="header">Created by<br><a class="header name" href="http://daggerfs.com/">Yangqing Jia</a></p>

</header>
<section>

{{ content }}

</section>
<!-- <footer>
<p><small>Hosted on <a href="http://pages.github.com">GitHub Pages</a>.</small></p>
</footer>
-->
</div>
<!--[if !IE]><script>fixScale(document);</script><![endif]-->
</body>
</html>
11 changes: 8 additions & 3 deletions docs/development.md
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### Documentation

Tutorials and general documentation -- including this website -- are written in Markdown format in the `docs/` folder.
While the format is quite easy to read directly, you may prefer to view the whole thing as a website.
To do so, simply run `jekyll serve -s docs` and view the documentation website at `http://0.0.0.0:4000` (for [jekyll](http://jekyllrb.com/), you must have ruby and do `gem install jekyll`).
This website, written with [Jekyll](http://jekyllrb.com/), functions as the official Caffe documentation -- simply run `scripts/build_docs.sh` and view the website at `http://0.0.0.0:4000`.

We prefer tutorials and examples to be documented close to where they live, in `readme.md` files.
The `build_docs.sh` script gathers all `examples/**/readme.md` and `examples/*.ipynb` files, and makes a table of contents.
To be included in the docs, the readme files must be annotated with [YAML front-matter](http://jekyllrb.com/docs/frontmatter/), including the flag `include_in_docs: true`.
Similarly for IPython notebooks: simply include `"include_in_docs": true` in the `"metadata"` JSON field.

Other docs, such as installation guides, are written in the `docs` directory and manually linked to from the `index.md` page.

We strive to provide provide lots of usage examples, and to document all code in docstrings.
We absolutely appreciate any contribution to this effort!
5 changes: 4 additions & 1 deletion docs/getting_pretrained_models.md
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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.
*UPDATE* July 2014: we are actively working on a service for hosting user-uploaded model definition and trained weight files.
Soon, the community will be able to easily contribute different architectures!

### ImageNet

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**R-CNN (ILSVRC13)**: The pure Caffe instantiation of the [R-CNN](https://github.com/rbgirshick/rcnn) model for ILSVRC13 detection. Download the model (230.8MB) by running `examples/imagenet/get_caffe_rcnn_imagenet_model.sh` from the Caffe root directory. This model was made by transplanting the R-CNN SVM classifiers into a `fc-rcnn` classification layer, provided here as an off-the-shelf Caffe detector. Try the [detection example](http://nbviewer.ipython.org/github/BVLC/caffe/blob/master/examples/detection.ipynb) to see it in action. For the full details, refer to the R-CNN site. *N.B. For research purposes, make use of the official R-CNN package and not this example.*

### Auxiliary Data

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.
59 changes: 33 additions & 26 deletions docs/index.md
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It was created by [Yangqing Jia](http://daggerfs.com), and is in active development by the Berkeley Vision and Learning Center ([BVLC](http://bvlc.eecs.berkeley.edu)) and by community contributors.
Caffe is released under the [BSD 2-Clause license](https://github.com/BVLC/caffe/blob/master/LICENSE).

## Why
Check out our web image classification [demo](http://demo.caffe.berkeleyvision.org)!

## Why use Caffe?

**Clean architecture** enables rapid deployment.
Networks are specified in simple config files, with no hard-coded parameters in the code.
Switching between CPU and GPU code is as simple as setting a flag -- so models can be trained on a GPU machine, and then used on commodity clusters.
Switching between CPU and GPU is as simple as setting a flag -- so models can be trained on a GPU machine, and then used on commodity clusters.

**Readable & modifiable implementation** fosters active development.
In Caffe's first six months, it has been forked by over 300 developers on Github, and many have contributed significant changes.
In Caffe's first six months, it has been forked by over 300 developers on Github, and many have pushed significant changes.

**Speed** makes Caffe perfect for industry use.
Caffe can process over **40M images per day** with a single NVIDIA K40 or Titan GPU\*.
@@ -29,29 +31,34 @@ There is an active discussion and support community on [Github](https://github.c
Consult performance [details](/performance_hardware.html).
</p>

## How

* [Introductory slides](http://dl.caffe.berkeleyvision.org/caffe-presentation.pdf): slides about the Caffe architecture, *updated 03/14*.
* [ACM MM paper](http://ucb-icsi-vision-group.github.io/caffe-paper/caffe.pdf): a 4-page report for the ACM Multimedia Open Source competition.
* [Installation instructions](/installation.html): tested on Ubuntu, Red Hat, OS X.
* [Pre-trained models](/getting_pretrained_models.html): BVLC provides ready-to-use models for non-commercial use.
* [Development](/development.html): Guidelines for development and contributing to Caffe.

### Tutorials and Examples

* [Image Classification \[notebook\]][imagenet_classification]: classify images with the pretrained ImageNet model by the Python interface.
* [Detection \[notebook\]][detection]: run a pretrained model as a detector in Python.
* [Visualizing Features and Filters \[notebook\]][visualizing_filters]: extracting features and visualizing trained filters with an example image, viewed layer-by-layer.
* [Editing Model Parameters \[notebook\]][net_surgery]: how to do net surgery and manually change model parameters.
* [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): recipe for end-to-end training of an ImageNet classifier.
* [Feature extraction with C++](/feature_extraction.html): feature extraction using pre-trained model.

[imagenet_classification]: http://nbviewer.ipython.org/github/BVLC/caffe/blob/master/examples/imagenet_classification.ipynb
[detection]: http://nbviewer.ipython.org/github/BVLC/caffe/blob/master/examples/detection.ipynb
[visualizing_filters]: http://nbviewer.ipython.org/github/BVLC/caffe/blob/master/examples/filter_visualization.ipynb
[net_surgery]: http://nbviewer.ipython.org/github/BVLC/caffe/blob/master/examples/net_surgery.ipynb
## Documentation

- [Introductory slides](http://dl.caffe.berkeleyvision.org/caffe-presentation.pdf)<br />
Slides about the Caffe architecture, *updated 03/14*.
- [ACM MM paper](http://ucb-icsi-vision-group.github.io/caffe-paper/caffe.pdf)<br />
A 4-page report for the ACM Multimedia Open Source competition.
- [Installation instructions](/installation.html)<br />
Tested on Ubuntu, Red Hat, OS X.
* [Pre-trained models](/getting_pretrained_models.html)<br />
BVLC provides ready-to-use models for non-commercial use.
* [Development](/development.html)<br />
Guidelines for development and contributing to Caffe.

### Examples

{% for page in site.pages %}
{% if page.category == 'example' %}
- <div><a href="{{page.url}}">{{page.title}}</a><br />{{page.description}}</div>
{% endif %}
{% endfor %}

### Notebook examples

{% for page in site.pages %}
{% if page.category == 'notebook' %}
- <div><a href="http://nbviewer.ipython.org/github/BVLC/caffe/blob/master/{{page.original_path}}">{{page.title}}</a><br />{{page.description}}</div>
{% endif %}
{% endfor %}

## Citing Caffe

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