diff --git a/README.md b/README.md
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--- a/README.md
+++ b/README.md
@@ -65,7 +65,18 @@ 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>
 
-FaceNet, VGG-Face, ArcFace and Dlib [overperforms](https://youtu.be/i_MOwvhbLdI) than OpenFace, DeepFace and DeepID based on experiments. Supportively, FaceNet /w 512d got 99.65%; FaceNet /w 128d got 99.2%; ArcFace got 99.41%; Dlib got 99.38%; VGG-Face got 98.78%; DeepID got 97.05; OpenFace got 93.80% accuracy scores on [LFW data set](https://sefiks.com/2020/08/27/labeled-faces-in-the-wild-for-face-recognition/) whereas human beings could have just 97.53%.
+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.
+
+| Model | LFW Score | YFW Score |
+| ---   | --- | --- |
+| Facenet512 | 99.65% | - |
+| ArcFace | 99.41% | - |
+| Dlib | 99.38 % | - |
+| Facenet | 99.20% | - |
+| VGG-Face | 98.78% | 97.40% |
+| Human-beings | 97.53% | - |
+| OpenFace | 93.80% | - |
+| DeepID | - | 97.05% |
 
 **Similarity**