diff --git a/README.md b/README.md
index 897e23e..b857409 100644
--- a/README.md
+++ b/README.md
@@ -72,7 +72,7 @@ result = DeepFace.verify(img1_path = "img1.jpg", img2_path = "img2.jpg", model_n
 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>
+<p align="center"><img src="https://raw.githubusercontent.com/serengil/deepface/master/icon/model-portfolio-v8.jpg" 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 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.
 
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