diff --git a/README.md b/README.md
index 7549650..5146304 100644
--- a/README.md
+++ b/README.md
@@ -58,6 +58,8 @@ result = DeepFace.verify("img1.jpg", "img2.jpg", model_name = models[1])
 df = DeepFace.find(img_path = "img1.jpg", db_path = "C:/workspace/my_db", model_name = models[1])
 ```
 
+<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 got 99.65%; ArcFace got 99.40%; Dlib got 99.38%; VGG-Face got 98.78%; 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%.
 
 **Similarity**