diff --git a/README.md b/README.md index 8bbed3c..b7c6811 100644 --- a/README.md +++ b/README.md @@ -51,9 +51,7 @@ openface_result = DeepFace.verify("img1.jpg", "img2.jpg", model_name = "OpenFace deepface_result = DeepFace.verify("img1.jpg", "img2.jpg", model_name = "DeepFace") ``` -VGG-Face has the highest accuracy score but it is not convenient for real time studies because of its complex structure. FaceNet is a complex model as well. On the other hand, OpenFace has a close accuracy score but it performs the fastest. That's why, OpenFace is much more convenient for real time studies. - -The complexity of each face recognition model is different. Mean ± std. dev. of 7 runs on CPU for each model in my experiments is illustrated in the following table. +The complexity and response time of each face recognition model is different so do accuracy scores. Mean ± std. dev. of 7 runs on CPU for each model in my experiments is illustrated in the following table. | Model | VGG-Face | OpenFace | Google FaceNet | Facebook DeepFace | | --- | --- | --- | --- | --- |