[Caffe: Convolutional Architecture for Fast Feature Extraction](http://caffe.berkeleyvision.org)

Created by [Yangqing Jia](http://daggerfs.com), UC Berkeley EECS department.
In active development by the Berkeley Vision and Learning Center ([BVLC](http://bvlc.eecs.berkeley.edu/)).

## 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.

* [Caffe introductory presentation](https://www.dropbox.com/s/10fx16yp5etb8dv/caffe-presentation.pdf)
* [Installation instructions](http://caffe.berkeleyvision.org/installation.html)

\* 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.

## License

Caffe is BSD 2-Clause licensed (refer to the
[LICENSE](http://caffe.berkeleyvision.org/license.html) for details).

The pretrained models published by the BVLC, such as the
[Caffe reference ImageNet model](https://www.dropbox.com/s/n3jups0gr7uj0dv/caffe_reference_imagenet_model)
are licensed for academic research / non-commercial use only. However, Caffe is
a full toolkit for model training, so start brewing your own Caffe model today!

## 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{http://caffe.berkeleyvision.org/}}
    }

## Documentation

Tutorials and general documentation 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` (to get [jekyll](http://jekyllrb.com/), you must have ruby and do `gem install jekyll`).

We strive to provide provide lots of usage examples, and to document all code in docstrings.
We'd appreciate your contribution to this effort!

## Development

Caffe is developed with active participation of the community by the [Berkeley Vision and Learning Center](http://bvlc.eecs.berkeley.edu/).
We welcome all contributions!

### The release cycle

- The `dev` branch is for new development, including community contributions. We aim to keep it in a functional state, but large changes may occur and things may get broken every now and then. Use this if you want the "bleeding edge".
- The `master` branch is handled by BVLC, which will integrate changes from `dev` on a roughly monthly schedule, giving it a release tag. Use this if you want more stability.

### Setting priorities

- Make GitHub Issues for bugs, features you'd like to see, questions, etc.
- Development work is guided by [milestones](https://github.com/BVLC/caffe/issues?milestone=1), which are sets of issues selected for concurrent release (integration from `dev` to `master`).
- Please note that since the core developers are largely researchers, we may work on a feature in isolation from the open-source community for some time before releasing it, so as to claim honest academic contribution. We do release it as soon as a reasonable technical report may be written about the work, and we still aim to inform the community of ongoing development through Issues.

### Contibuting

- Do new development in [feature branches](https://www.atlassian.com/git/workflows#!workflow-feature-branch) with descriptive names.
- Bring your work up-to-date by [rebasing](http://git-scm.com/book/en/Git-Branching-Rebasing) onto the latest `dev`. (Polish your changes by [interactive rebase](https://help.github.com/articles/interactive-rebase), if you'd like.)
- [Pull request](https://help.github.com/articles/using-pull-requests) your contribution to BVLC/caffe's `dev` branch for discussion and review.
  * PRs should live fast, die young, and leave a beautiful merge. Pull request sooner than later so that discussion can guide development.
  * Code must be accompanied by documentation and tests at all times.
  * Only fast-forward merges will be accepted.

See our [development guidelines](http://caffe.berkeleyvision.org/development.html) for further details–the more closely these are followed, the sooner your work will be merged.

#### [Shelhamer's](https://github.com/shelhamer) “life of a branch in four acts”

Make the `feature` branch off of the latest `bvlc/dev`
```
git checkout dev
git pull upstream dev
git checkout -b feature
# do your work, make commits
```

Prepare to merge by rebasing your branch on the latest `bvlc/dev`
```
# make sure dev is fresh
git checkout dev
git pull upstream dev
# rebase your branch on the tip of dev
git checkout feature
git rebase dev
```

Push your branch to pull request it into `dev`
```
git push origin feature
# ...make pull request to dev...
```

Now make a pull request! You can do this from the command line (`git pull-request -b dev`) if you install [hub](https://github.com/github/hub).

The pull request of `feature` into `dev` will be a clean merge. Applause.
