diff --git a/README.md b/README.md
index 4211239..be961b9 100644
--- a/README.md
+++ b/README.md
@@ -67,14 +67,14 @@ df = DeepFace.find(img_path = "img1.jpg", db_path = "C:/workspace/my_db", model_
 
 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 |
+| Model | LFW Score | YTF Score |
 | ---   | --- | --- |
 | Facenet512 | 99.65% | - |
 | ArcFace | 99.41% | - |
 | Dlib | 99.38 % | - |
 | Facenet | 99.20% | - |
 | VGG-Face | 98.78% | 97.40% |
-| Human-beings | 97.53% | - |
+| *Human-beings* | 97.53% | - |
 | OpenFace | 93.80% | - |
 | DeepID | - | 97.05% |