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[docs] moved example md’s to examples/**/md’s and added script to gather
them for publication
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‎.gitignore‎

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# Generated documentation
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docs/_site
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docs/gathered
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_site
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# Sublime Text settings

‎docs/README.md‎

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To generate stuff you can paste in an .md page from an IPython notebook, run
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# Caffe Documentation
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ipython nbconvert --to markdown <notebook_file>
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To generate the documentation, run `$CAFFE_ROOT/scripts/build_docs.sh`.
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To push your changes to the documentation to the gh-pages branch of your or the BVLC repo, run `$CAFFE_ROOT/scripts/deploy_docs.sh <repo_name>`.

‎docs/index.md‎

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Check out our web image classification [demo](http://demo.caffe.berkeleyvision.org)!
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## Why
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## Why use Caffe?
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**Clean architecture** enables rapid deployment.
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Networks are specified in simple config files, with no hard-coded parameters in the code.
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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.
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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.
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**Readable & modifiable implementation** fosters active development.
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In Caffe's first six months, it has been forked by over 300 developers on Github, and many have contributed significant changes.
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In Caffe's first six months, it has been forked by over 300 developers on Github, and many have pushed significant changes.
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**Speed** makes Caffe perfect for industry use.
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Caffe can process over **40M images per day** with a single NVIDIA K40 or Titan GPU\*.
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Consult performance [details](/performance_hardware.html).
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</p>
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## How
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* [Introductory slides](http://dl.caffe.berkeleyvision.org/caffe-presentation.pdf): slides about the Caffe architecture, *updated 03/14*.
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* [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.
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* [Installation instructions](/installation.html): tested on Ubuntu, Red Hat, OS X.
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* [Pre-trained models](/getting_pretrained_models.html): BVLC provides ready-to-use models for non-commercial use.
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* [Development](/development.html): Guidelines for development and contributing to Caffe.
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### Tutorials and Examples
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* [Image Classification \[notebook\]][imagenet_classification]: classify images with the pretrained ImageNet model by the Python interface.
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* [Detection \[notebook\]][detection]: run a pretrained model as a detector in Python.
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* [Visualizing Features and Filters \[notebook\]][visualizing_filters]: extracting features and visualizing trained filters with an example image, viewed layer-by-layer.
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* [LeNet / MNIST Demo](/mnist.html): end-to-end training and testing of LeNet on MNIST.
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* [CIFAR-10 Demo](/cifar10.html): training and testing on the CIFAR-10 data.
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* [Training ImageNet](/imagenet_training.html): recipe for end-to-end training of an ImageNet classifier.
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* [Feature extraction with C++](/feature_extraction.html): feature extraction using pre-trained model.
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[imagenet_classification]: http://nbviewer.ipython.org/github/BVLC/caffe/blob/master/examples/imagenet_classification.ipynb
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[detection]: http://nbviewer.ipython.org/github/BVLC/caffe/blob/master/examples/detection.ipynb
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[visualizing_filters]: http://nbviewer.ipython.org/github/BVLC/caffe/blob/master/examples/filter_visualization.ipynb
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## Documentation
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- [Introductory slides](http://dl.caffe.berkeleyvision.org/caffe-presentation.pdf)<br />
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Slides about the Caffe architecture, *updated 03/14*.
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- [ACM MM paper](http://ucb-icsi-vision-group.github.io/caffe-paper/caffe.pdf)<br />
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A 4-page report for the ACM Multimedia Open Source competition.
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- [Installation instructions](/installation.html)<br />
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Tested on Ubuntu, Red Hat, OS X.
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* [Pre-trained models](/getting_pretrained_models.html)<br />
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BVLC provides ready-to-use models for non-commercial use.
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* [Development](/development.html)<br />
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Guidelines for development and contributing to Caffe.
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### Examples
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{% for page in site.pages %}
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{% if page.category == 'example' %}
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- <div><a href="{{page.url}}">{{page.title}}</a><br />{{page.description}}</div>
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{% endif %}
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{% endfor %}
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### Notebook examples
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{% for page in site.pages %}
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{% if page.category == 'notebook' %}
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- <div><a href="http://nbviewer.ipython.org/github/BVLC/caffe/blob/master/{{page.original_path}}">{{page.title}}</a><br />{{page.description}}</div>
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{% endif %}
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{% endfor %}
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## Citing Caffe
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‎docs/mnist.md‎

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‎docs/mnist_prototxt.md‎

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‎docs/mnist_solver_prototxt.md‎

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docs/cifar10.md renamed to examples/cifar10/readme.md

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---
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title: CIFAR-10 tutorial
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category: example
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description: Train and test Caffe on CIFAR-10 data.
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include_in_docs: true
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layout: default
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title: Caffe
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---
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Alex's CIFAR-10 tutorial, Caffe style

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