diff --git a/README.md b/README.md
index 513d612..f963a51 100644
--- a/README.md
+++ b/README.md
@@ -223,7 +223,7 @@ face = DeepFace.detectFace(img_path = "img.jpg",
 
 Face recognition models are actually CNN models and they expect standard sized inputs. So, resizing is required before representation. To avoid deformation, deepface adds black padding pixels according to the target size argument after detection and alignment.
 
-<p align="center"><img src="https://raw.githubusercontent.com/serengil/deepface/master/icon/deepface-detectors-v2.jpg" width="90%" height="90%"></p>
+<p align="center"><img src="https://raw.githubusercontent.com/serengil/deepface/master/icon/deepface-detectors-v3.jpg" width="90%" height="90%"></p>
 
 [RetinaFace](https://sefiks.com/2021/04/27/deep-face-detection-with-retinaface-in-python/) and [MTCNN](https://sefiks.com/2020/09/09/deep-face-detection-with-mtcnn-in-python/) seem to overperform in detection and alignment stages but they are much slower. If the speed of your pipeline is more important, then you should use opencv or ssd. On the other hand, if you consider the accuracy, then you should use retinaface or mtcnn.