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[](https://anaconda.org/conda-forge/deepface)
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[](https://github.com/serengil/deepface/stargazers)
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[](https://github.com/serengil/deepface/blob/master/LICENSE)
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[](https://github.com/serengil/deepface/actions/workflows/tests.yml)
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[
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[](https://www.youtube.com/@sefiks?sub_confirmation=1)
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[](https://twitter.com/intent/user?screen_name=serengil)
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[](https://www.patreon.com/serengil?repo=deepface)
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[](https://github.com/sponsors/serengil)
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[](https://doi.org/10.1109/ICEET53442.2021.9659697)
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Deepface is a lightweight [face recognition](https://sefiks.com/2018/08/06/deep-face-recognition-with-keras/) and facial attribute analysis ([age](https://sefiks.com/2019/02/13/apparent-age-and-gender-prediction-in-keras/), [gender](https://sefiks.com/2019/02/13/apparent-age-and-gender-prediction-in-keras/), [emotion](https://sefiks.com/2018/01/01/facial-expression-recognition-with-keras/) and [race](https://sefiks.com/2019/11/11/race-and-ethnicity-prediction-in-keras/)) framework for python. It is a hybrid face recognition framework wrapping **state-of-the-art** models: [`VGG-Face`](https://sefiks.com/2018/08/06/deep-face-recognition-with-keras/), [`FaceNet`](https://sefiks.com/2018/09/03/face-recognition-with-facenet-in-keras/), [`OpenFace`](https://sefiks.com/2019/07/21/face-recognition-with-openface-in-keras/), [`DeepFace`](https://sefiks.com/2020/02/17/face-recognition-with-facebook-deepface-in-keras/), [`DeepID`](https://sefiks.com/2020/06/16/face-recognition-with-deepid-in-keras/), [`ArcFace`](https://sefiks.com/2020/12/14/deep-face-recognition-with-arcface-in-keras-and-python/), [`Dlib`](https://sefiks.com/2020/07/11/face-recognition-with-dlib-in-python/), `SFace` and `GhostFaceNet`.
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Experiments show that human beings have 97.53% accuracy on facial recognition tasks whereas those models already reached and passed that accuracy level.
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$ pip install deepface
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```
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$ conda install -c conda-forge deepface
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```
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$ git clone https://github.com/serengil/deepface.git
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$ cd deepface
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$ pip install -e .
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```
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from deepface import DeepFace
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```
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result = DeepFace.verify(img1_path = "img1.jpg", img2_path = "img2.jpg")
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```
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dfs = DeepFace.find(img_path = "img1.jpg", db_path = "C:/workspace/my_db")
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```
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embedding_objs = DeepFace.represent(img_path = "img.jpg")
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```
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embedding = embedding_objs[0]["embedding"]
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assert isinstance(embedding, list)
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assert model_name == "VGG-Face" and len(embedding) == 4096
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```
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models = [
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"VGG-Face",
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"Facenet",
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"Facenet512",
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"OpenFace",
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"DeepFace",
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"DeepID",
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"ArcFace",
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"Dlib",
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"SFace",
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"GhostFaceNet",
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]
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result = DeepFace.verify(img1_path = "img1.jpg",
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img2_path = "img2.jpg",
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model_name = models[0]
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)
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dfs = DeepFace.find(img_path = "img1.jpg",
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db_path = "C:/workspace/my_db",
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model_name = models[1]
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)
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#embeddings
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embedding_objs = DeepFace.represent(img_path = "img.jpg",
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model_name = models[2]
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)
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```
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| -------------- | ------------------ |
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| VGG-Face | 98.9% |
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| Facenet | 99.2% |
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| Facenet512 | 99.6% |
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| OpenFace | 92.9% |
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| DeepID | 97.4% |
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| Dlib | 99.3 % |
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| SFace | 99.5% |
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| ArcFace | 99.5% |
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| GhostFaceNet | 99.7% |
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| *Human-beings* | *97.5%* |
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metrics = ["cosine", "euclidean", "euclidean_l2"]
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result = DeepFace.verify(img1_path = "img1.jpg",
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img2_path = "img2.jpg",
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distance_metric = metrics[1]
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)
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dfs = DeepFace.find(img_path = "img1.jpg",
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db_path = "C:/workspace/my_db",
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distance_metric = metrics[2]
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)
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```
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objs = DeepFace.analyze(img_path = "img4.jpg",
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actions = ['age', 'gender', 'race', 'emotion']
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)
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```
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All deepface functions accept an optional detector backend input argument. You can switch among those detectors with this argument. OpenCV is the default detector.
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```python
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backends = [
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'opencv',
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'ssd',
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'dlib',
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'mtcnn',
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'fastmtcnn',
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'retinaface',
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'mediapipe',
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'yolov8',
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'yunet',
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'centerface',
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]
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obj = DeepFace.verify(img1_path = "img1.jpg",
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img2_path = "img2.jpg",
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detector_backend = backends[0]
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)
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dfs = DeepFace.find(img_path = "img.jpg",
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db_path = "my_db",
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detector_backend = backends[1]
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)
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embedding_objs = DeepFace.represent(img_path = "img.jpg",
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detector_backend = backends[2]
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)
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demographies = DeepFace.analyze(img_path = "img4.jpg",
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detector_backend = backends[3]
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)
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face_objs = DeepFace.extract_faces(img_path = "img.jpg",
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detector_backend = backends[4]
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)
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```
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<br><em>The Yellow Angels - Fenerbahce Women's Volleyball Team</em>
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</p>
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DeepFace.stream(db_path = "C:/User/Sefik/Desktop/database")
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```
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user
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├── database
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│ ├── Alice
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│ │ ├── Alice1.jpg
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│ │ ├── Alice2.jpg
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│ ├── Bob
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│ │ ├── Bob.jpg
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```
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cd scripts
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./service.sh
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```
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cd scripts
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./dockerize.sh
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```
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#face verification
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$ deepface verify -img1_path tests/dataset/img1.jpg -img2_path tests/dataset/img2.jpg
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$ deepface analyze -img_path tests/dataset/img1.jpg
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```
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## Contribution
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Pull requests are more than welcome! If you are planning to contribute a large patch, please create an issue first to get any upfront questions or design decisions out of the way first.
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Before creating a PR, you should run the unit tests and linting locally by running `make test && make lint` command. Once a PR sent, GitHub test workflow will be run automatically and unit test and linting jobs will be available in [GitHub actions](https://github.com/serengil/deepface/actions) before approval.
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## Support
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There are many ways to support a project - starring⭐️ the GitHub repo is just one 🙏
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You can also support this work on [Patreon](https://www.patreon.com/serengil?repo=deepface) or [GitHub Sponsors](https://github.com/sponsors/serengil).
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<a href="https://www.patreon.com/serengil?repo=deepface">
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<img src="https://raw.githubusercontent.com/serengil/deepface/master/icon/patreon.png" width="30%" height="30%">
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</a>
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## Citation
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Please cite deepface in your publications if it helps your research - see [`CITATIONS`](https://github.com/serengil/deepface/blob/master/CITATION.md) for more details. Here are its BibTex entries:
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If you use deepface in your research for facial recogntion purposes, please cite this publication.
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```BibTeX
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@inproceedings{serengil2020lightface,
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title = {LightFace: A Hybrid Deep Face Recognition Framework},
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author = {Serengil, Sefik Ilkin and Ozpinar, Alper},
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booktitle = {2020 Innovations in Intelligent Systems and Applications Conference (ASYU)},
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pages = {23-27},
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year = {2020},
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doi = {10.1109/ASYU50717.2020.9259802},
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url = {https://ieeexplore.ieee.org/document/9259802},
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organization = {IEEE}
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}
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```
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If you use deepface in your research for facial attribute analysis purposes such as age, gender, emotion or ethnicity prediction, please cite this publication.
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```BibTeX
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@inproceedings{serengil2021lightface,
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title = {HyperExtended LightFace: A Facial Attribute Analysis Framework},
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author = {Serengil, Sefik Ilkin and Ozpinar, Alper},
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booktitle = {2021 International Conference on Engineering and Emerging Technologies (ICEET)},
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pages = {1-4},
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year = {2021},
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doi = {10.1109/ICEET53442.2021.9659697},
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url = {https://ieeexplore.ieee.org/document/9659697},
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organization = {IEEE}
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}
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```
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Also, if you use deepface in your GitHub projects, please add `deepface` in the `requirements.txt`.
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## Licence
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DeepFace is licensed under the MIT License - see [`LICENSE`](https://github.com/serengil/deepface/blob/master/LICENSE) for more details.
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DeepFace wraps some external face recognition models: [VGG-Face](http://www.robots.ox.ac.uk/~vgg/software/vgg_face/), [Facenet](https://github.com/davidsandberg/facenet/blob/master/LICENSE.md), [OpenFace](https://github.com/iwantooxxoox/Keras-OpenFace/blob/master/LICENSE), [DeepFace](https://github.com/swghosh/DeepFace), [DeepID](https://github.com/Ruoyiran/DeepID/blob/master/LICENSE.md), [ArcFace](https://github.com/leondgarse/Keras_insightface/blob/master/LICENSE), [Dlib](https://github.com/davisking/dlib/blob/master/dlib/LICENSE.txt), [SFace](https://github.com/opencv/opencv_zoo/blob/master/models/face_recognition_sface/LICENSE) and [GhostFaceNet](https://github.com/HamadYA/GhostFaceNets/blob/main/LICENSE). Besides, age, gender and race / ethnicity models were trained on the backbone of VGG-Face with transfer learning. Similarly, DeepFace wraps many face detectors: [OpenCv](https://github.com/opencv/opencv/blob/4.x/LICENSE), [Ssd](https://github.com/opencv/opencv/blob/master/LICENSE), [Dlib](https://github.com/davisking/dlib/blob/master/LICENSE.txt), [MtCnn](https://github.com/ipazc/mtcnn/blob/master/LICENSE), [Fast MtCnn](https://github.com/timesler/facenet-pytorch/blob/master/LICENSE.md), [RetinaFace](https://github.com/serengil/retinaface/blob/master/LICENSE), [MediaPipe](https://github.com/google/mediapipe/blob/master/LICENSE), [YuNet](https://github.com/ShiqiYu/libfacedetection/blob/master/LICENSE), [Yolo](https://github.com/derronqi/yolov8-face/blob/main/LICENSE) and [CenterFace](https://github.com/Star-Clouds/CenterFace/blob/master/LICENSE). License types will be inherited when you intend to utilize those models. Please check the license types of those models for production purposes.
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DeepFace [logo](https://thenounproject.com/term/face-recognition/2965879/) is created by [Adrien Coquet](https://thenounproject.com/coquet_adrien/) and it is licensed under [Creative Commons: By Attribution 3.0 License](https://creativecommons.org/licenses/by/3.0/).
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---
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# For reference on model card metadata, see the spec: https://github.com/huggingface/hub-docs/blob/main/modelcard.md?plain=1
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# Doc / guide: https://huggingface.co/docs/hub/model-cards
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{}
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---
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# Model Card for Model ID
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<!-- Provide a quick summary of what the model is/does. -->
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This modelcard aims to be a base template for new models. It has been generated using [this raw template](https://github.com/huggingface/huggingface_hub/blob/main/src/huggingface_hub/templates/modelcard_template.md?plain=1).
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## Model Details
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### Model Description
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<!-- Provide a longer summary of what this model is. -->
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- **Developed by:** [More Information Needed]
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- **Funded by [optional]:** [More Information Needed]
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- **Shared by [optional]:** [More Information Needed]
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- **Model type:** [More Information Needed]
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- **Language(s) (NLP):** [More Information Needed]
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- **License:** [More Information Needed]
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- **Finetuned from model [optional]:** [More Information Needed]
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### Model Sources [optional]
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<!-- Provide the basic links for the model. -->
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- **Repository:** [More Information Needed]
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- **Paper [optional]:** [More Information Needed]
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- **Demo [optional]:** [More Information Needed]
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## Uses
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<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
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### Direct Use
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<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
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[More Information Needed]
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### Downstream Use [optional]
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<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
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[More Information Needed]
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### Out-of-Scope Use
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<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
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[More Information Needed]
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## Bias, Risks, and Limitations
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<!-- This section is meant to convey both technical and sociotechnical limitations. -->
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[More Information Needed]
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### Recommendations
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<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
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Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
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## How to Get Started with the Model
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Use the code below to get started with the model.
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[More Information Needed]
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## Training Details
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### Training Data
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<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
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[More Information Needed]
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### Training Procedure
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<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
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#### Preprocessing [optional]
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[More Information Needed]
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#### Training Hyperparameters
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- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
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#### Speeds, Sizes, Times [optional]
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<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
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[More Information Needed]
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## Evaluation
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<!-- This section describes the evaluation protocols and provides the results. -->
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### Testing Data, Factors & Metrics
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#### Testing Data
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<!-- This should link to a Dataset Card if possible. -->
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[More Information Needed]
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#### Factors
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<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
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[More Information Needed]
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#### Metrics
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<!-- These are the evaluation metrics being used, ideally with a description of why. -->
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[More Information Needed]
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### Results
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[More Information Needed]
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#### Summary
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## Model Examination [optional]
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<!-- Relevant interpretability work for the model goes here -->
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[More Information Needed]
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## Environmental Impact
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<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
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Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
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- **Hardware Type:** [More Information Needed]
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| 149 |
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- **Hours used:** [More Information Needed]
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| 150 |
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- **Cloud Provider:** [More Information Needed]
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| 151 |
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- **Compute Region:** [More Information Needed]
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| 152 |
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- **Carbon Emitted:** [More Information Needed]
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| 153 |
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## Technical Specifications [optional]
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| 155 |
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### Model Architecture and Objective
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| 157 |
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[More Information Needed]
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### Compute Infrastructure
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[More Information Needed]
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#### Hardware
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| 165 |
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[More Information Needed]
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#### Software
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[More Information Needed]
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## Citation [optional]
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<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
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**BibTeX:**
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[More Information Needed]
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**APA:**
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[More Information Needed]
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## Glossary [optional]
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| 185 |
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| 186 |
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<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
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| 187 |
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[More Information Needed]
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## More Information [optional]
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| 191 |
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[More Information Needed]
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## Model Card Authors [optional]
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| 195 |
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[More Information Needed]
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## Model Card Contact
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[More Information Needed]
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