513ad57244 | ||
---|---|---|
batch-represent | ||
demos | ||
docker | ||
docs | ||
evaluation | ||
images | ||
models | ||
openface | ||
training | ||
util | ||
.gitignore | ||
.gitmodules | ||
.travis.yml | ||
CHANGELOG.md | ||
LICENSE | ||
README.md | ||
cloc.sh | ||
mkdocs.yml |
README.md
OpenFace
[ ![Build Status] travis-image ] travis [ ![Release] release-image ] releases [ license-image ] license [ doi-image ] doi [ gitter-image ] gitter [ ] docs
This is a Python and Torch implementation of the CVPR 2015 paper FaceNet: A Unified Embedding for Face Recognition and Clustering by Florian Schroff, Dmitry Kalenichenko, and James Philbin at Google using publicly available libraries and datasets. Torch allows the network to be executed on a CPU or with CUDA.
Crafted by Brandon Amos in the Elijah research group at Carnegie Mellon University.
Overview
Real-Time Demo
The documentation is available here.
Please join the cmu-openface group or the gitter chat for discussions and installation issues.
Development discussions and bugs reports are on the issue tracker.
Citations
Please cite this repository if you use this in academic works.
@misc{amos2015openface,
author = {Amos, Brandon and Harkes, Jan and Pillai, Padmanabhan and Elgazzar, Khalid and Satyanarayanan, Mahadev},
title = {OpenFace 0.1.1: Face recognition with Google's FaceNet deep neural network},
month = oct,
year = 2015,
doi = {10.5281/zenodo.32148},
url = {http://dx.doi.org/10.5281/zenodo.32148}
}
Acknowledgements
- The fantastic Torch ecosystem and community.
- Alfredo Canziani's implementation of FaceNet's loss function in torch-TripletEmbedding
- Nicholas Léonard for quickly merging my pull requests to nicholas-leonard/dpnn modifying the inception layer.
- Francisco Massa and Andrej Karpathy for quickly releasing nn.Normalize after I expressed interest in using it.
- Soumith Chintala for help with the fbcunn example code.
- NVIDIA's academic hardware grant program for providing the Tesla K40 used to train the model.
- Davis King's dlib library for face detection and alignment.
- Zhuo Chen, Kiryong Ha, Wenlu Hu, Rahul Sukthankar, and Junjue Wang for insightful discussions.
Licensing
The source code and trained models nn4.v1.t7
and
celeb-classifier.nn4.v1.t7
are copyright
Carnegie Mellon University and licensed under the
Apache 2.0 License.
Portions from the following third party sources have
been modified and are included in this repository.
These portions are noted in the source files and are
copyright their respective authors with
the licenses listed.
Project | Modified | License |
---|---|---|
Atcold/torch-TripletEmbedding | No | MIT |
facebook/fbnn | Yes | BSD |