From aee38e6e161030d5221b54f6a0ce33fcfb56518d Mon Sep 17 00:00:00 2001 From: Sefik Ilkin Serengil Date: Tue, 15 Dec 2020 10:37:21 +0300 Subject: [PATCH] human beings --- README.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/README.md b/README.md index c6fc3b0..6e42a72 100644 --- a/README.md +++ b/README.md @@ -60,7 +60,7 @@ for model in models: df = DeepFace.find(img_path = "img1.jpg", db_path = "C:/workspace/my_db", model_name = model) ``` -FaceNet, VGG-Face, ArcFace and Dlib [overperforms](https://youtu.be/i_MOwvhbLdI) than OpenFace, DeepFace and DeepID based on experiments. Supportively, VGG-Face got 98.78%; FaceNet got 99.65%; OpenFace got 92.92%; ArcFace got 99.40%; Dlib got 99.38% accuracy on [LFW data set](https://sefiks.com/2020/08/27/labeled-faces-in-the-wild-for-face-recognition/). +FaceNet, VGG-Face, ArcFace and Dlib [overperforms](https://youtu.be/i_MOwvhbLdI) than OpenFace, DeepFace and DeepID based on experiments. Supportively, VGG-Face got 98.78%; FaceNet got 99.65%; ArcFace got 99.40%; Dlib got 99.38% accuracy 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**