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ensemble score

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@ -68,7 +68,7 @@ for metric in metrics:
**Ensemble learning for face recognition** - [`Demo`](https://youtu.be/EIBJJJ0ECXU)
A face recognition task can be handled by several models and similarity metrics. Herein, deepface offers a [special boosting and combination solution](https://sefiks.com/2020/06/03/mastering-face-recognition-with-ensemble-learning/) to improve the accuracy of a face recognition task. This provides a huge improvement on accuracy metrics but it runs much slower than single models.
A face recognition task can be handled by several models and similarity metrics. Herein, deepface offers a [special boosting and combination solution](https://sefiks.com/2020/06/03/mastering-face-recognition-with-ensemble-learning/) to improve the accuracy of a face recognition task. This provides a huge improvement on accuracy metrics. Human beings could have 97.53% score for face recognition tasks whereas this ensemble method passes the human level accuracy and gets 98.57% accuracy. On the other hand, this runs much slower than single models.
<p align="center"><img src="https://raw.githubusercontent.com/serengil/deepface/master/icon/stock-4.jpg" width="70%" height="70%"></p>