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@ -65,7 +65,7 @@ df = DeepFace.find(img_path = "img1.jpg", db_path = "C:/workspace/my_db", model_
<p align="center"><img src="https://raw.githubusercontent.com/serengil/deepface/master/icon/deepface-wrapped-models.png" width="95%" height="95%"></p> <p align="center"><img src="https://raw.githubusercontent.com/serengil/deepface/master/icon/deepface-wrapped-models.png" width="95%" height="95%"></p>
FaceNet, VGG-Face, ArcFace and Dlib are [overperforming](https://youtu.be/i_MOwvhbLdI) ones based on experiments. You can find the scores of those models on both [Labeled Faces in the Wild](https://sefiks.com/2020/08/27/labeled-faces-in-the-wild-for-face-recognition/) and YouTube Faces in the Wild data sets declared by its creators. FaceNet, VGG-Face, ArcFace and Dlib are [overperforming](https://youtu.be/i_MOwvhbLdI) ones based on experiments. You can find out the scores of those models below on both [Labeled Faces in the Wild](https://sefiks.com/2020/08/27/labeled-faces-in-the-wild-for-face-recognition/) and YouTube Faces in the Wild data sets declared by its creators.
| Model | LFW Score | YTF Score | | Model | LFW Score | YTF Score |
| --- | --- | --- | | --- | --- | --- |