From 73723277776e69e034fa3804832cce0c5d6a959f Mon Sep 17 00:00:00 2001 From: Sefik Ilkin Serengil Date: Mon, 10 Feb 2020 09:21:13 +0300 Subject: [PATCH 1/3] download count --- README.md | 2 ++ 1 file changed, 2 insertions(+) diff --git a/README.md b/README.md index 4c0357a..2d43f1b 100644 --- a/README.md +++ b/README.md @@ -1,5 +1,7 @@ # deepface +[![Downloads](https://pepy.tech/badge/deepface)](https://pepy.tech/project/deepface) + **deepface** is a lightweight python based face recognition framework. You can verify faces with just a few lines of codes. ```python From cc5fb80495f4062ab1319de79c73b424240cd5d5 Mon Sep 17 00:00:00 2001 From: Sefik Ilkin Serengil Date: Mon, 10 Feb 2020 09:43:02 +0300 Subject: [PATCH 2/3] github > pypi --- README.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/README.md b/README.md index 2d43f1b..0491d69 100644 --- a/README.md +++ b/README.md @@ -65,7 +65,7 @@ The easiest way to install deepface is to download it from [PyPI](https://pypi.o pip install deepface ``` -Alternatively, you can directly download the source code from this repository. GitHub repo might be newer than the PyPI version. +Alternatively, you can directly download the source code from this repository. **GitHub repo might be newer than the PyPI version**. ``` git clone https://github.com/serengil/deepface.git From c0fe56945447e4f325a7c7a745e4b2cdf9452079 Mon Sep 17 00:00:00 2001 From: Sefik Ilkin Serengil Date: Mon, 10 Feb 2020 09:43:33 +0300 Subject: [PATCH 3/3] sub headings --- README.md | 6 +++--- 1 file changed, 3 insertions(+), 3 deletions(-) diff --git a/README.md b/README.md index 0491d69..7a9d57b 100644 --- a/README.md +++ b/README.md @@ -9,7 +9,7 @@ from deepface import DeepFace result = DeepFace.verify("img1.jpg", "img2.jpg") ``` -# Face recognition models +## Face recognition models Face recognition can be handled by different models. Currently, [`VGG-Face`](https://sefiks.com/2018/08/06/deep-face-recognition-with-keras/) , [`Facenet`](https://sefiks.com/2018/09/03/face-recognition-with-facenet-in-keras/) and [`OpenFace`](https://sefiks.com/2019/07/21/face-recognition-with-openface-in-keras/) models are supported in deepface. The default configuration verifies faces with **VGG-Face** model. You can set the base model while verification as illustared below. Accuracy and speed show difference based on the performing model. @@ -20,7 +20,7 @@ facenet_result = DeepFace.verify("img1.jpg", "img2.jpg", model_name = "Facenet") openface_result = DeepFace.verify("img1.jpg", "img2.jpg", model_name = "OpenFace") ``` -# Similarity +## Similarity These models actually find the vector embeddings of faces. Decision of verification is based on the distance between vectors. Distance could be found by different metrics such as [`Cosine Similarity`](https://sefiks.com/2018/08/13/cosine-similarity-in-machine-learning/), Euclidean Distance and L2 form. The default configuration finds the **cosine similarity**. You can alternatively set the similarity metric while verification as demostratred below. @@ -32,7 +32,7 @@ result = DeepFace.verify("img1.jpg", "img2.jpg", model_name = "VGG-Face", distan 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. -# Verification +## Verification Verification function returns a tuple including boolean verification result, distance between two faces and max threshold to identify.