From acf9e1044be2c6609bf0243500e6a986cb8d063a Mon Sep 17 00:00:00 2001
From: Sefik Ilkin Serengil <serengil@gmail.com>
Date: Tue, 13 Jul 2021 15:33:28 +0300
Subject: [PATCH] deepid score

---
 README.md | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/README.md b/README.md
index 49ce645..534b097 100644
--- a/README.md
+++ b/README.md
@@ -58,7 +58,7 @@ 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%; 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%.
+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%; ArcFace got 99.40%; 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%.
 
 **Similarity**