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@ -21,7 +21,7 @@ Deepface is a lightweight [face recognition](https://sefiks.com/2018/08/06/deep-
Experiments show that human beings have 97.53% accuracy on facial recognition tasks whereas those models already reached and passed that accuracy level. Experiments show that human beings have 97.53% accuracy on facial recognition tasks whereas those models already reached and passed that accuracy level.
## Installation ## Installation [![PyPI](https://img.shields.io/pypi/v/deepface.svg)](https://pypi.org/project/deepface/)
The easiest way to install deepface is to download it from [`PyPI`](https://pypi.org/project/deepface/). It's going to install the library itself and its prerequisites as well. The library is mainly powered by TensorFlow and Keras. The easiest way to install deepface is to download it from [`PyPI`](https://pypi.org/project/deepface/). It's going to install the library itself and its prerequisites as well. The library is mainly powered by TensorFlow and Keras.
@ -212,7 +212,7 @@ Tech stack is vast to store vector embeddings. To determine the right tool, you
## Contribution ## Contribution
Pull requests are welcome! You should run the unit tests locally by running [`test/unit_tests.py`](https://github.com/serengil/deepface/blob/master/tests/unit_tests.py). Once a PR sent, GitHub test workflow will be run automatically and unit test results will be available in GitHub actions before approval. Pull requests are welcome! You should run the unit tests locally by running [`test/unit_tests.py`](https://github.com/serengil/deepface/blob/master/tests/unit_tests.py). Once a PR sent, GitHub test workflow will be run automatically and unit test results will be available in [GitHub actions](https://github.com/serengil/deepface/actions) before approval.
## Support ## Support