From 0226ef49d9950109d5394cc332f7130cbf3a46aa Mon Sep 17 00:00:00 2001
From: Sefik Ilkin Serengil <serengil@gmail.com>
Date: Fri, 4 Dec 2020 18:14:27 +0300
Subject: [PATCH] detectors moved below

---
 README.md | 44 ++++++++++++++++++++++----------------------
 1 file changed, 22 insertions(+), 22 deletions(-)

diff --git a/README.md b/README.md
index 28fb1c4..9c74411 100644
--- a/README.md
+++ b/README.md
@@ -115,28 +115,6 @@ user
 │   │   ├── Bob.jpg
 ```
 
-**Face Detectors** - [`Demo`](https://youtu.be/GZ2p2hj2H5k)
-
-Face detection and alignment are early stages of a modern face recognition pipeline. [OpenCV haar cascade](https://sefiks.com/2020/02/23/face-alignment-for-face-recognition-in-python-within-opencv/), [SSD](https://sefiks.com/2020/08/25/deep-face-detection-with-opencv-in-python/), [Dlib](https://sefiks.com/2020/07/11/face-recognition-with-dlib-in-python/) and [MTCNN](https://sefiks.com/2020/09/09/deep-face-detection-with-mtcnn-in-python/) methods are wrapped in deepface as a detector. You can optionally pass a custom detector to functions in deepface interface. MTCNN is the default detector if you won't pass any detector.
-
-```python
-backends = ['opencv', 'ssd', 'dlib', 'mtcnn']
-for backend in backends:
-   #face detection and alignment
-   detected_face = DeepFace.detectFace("img.jpg", detector_backend = backend)
-   
-   #face verification
-   obj = DeepFace.verify("img1.jpg", "img2.jpg", detector_backend = backend)
-   
-   #face recognition
-   df = DeepFace.find(img_path = "img.jpg", db_path = "my_db", detector_backend = backend)
-   
-   #facial analysis
-   demography = DeepFace.analyze("img4.jpg", detector_backend = backend)
-```
-
-[MTCNN](https://sefiks.com/2020/09/09/deep-face-detection-with-mtcnn-in-python/) seems to overperform in detection and alignment stages but it is slower than [SSD](https://sefiks.com/2020/08/25/deep-face-detection-with-opencv-in-python/).
-
 **Ensemble learning for face recognition** - [`Demo`](https://youtu.be/EIBJJJ0ECXU)
 
 A face recognition task can be handled by several models and similarity metrics. Herein, deepface offers a [special boosting and combination solution](https://sefiks.com/2020/06/03/mastering-face-recognition-with-ensemble-learning/) to improve the accuracy of a face recognition task. This provides a huge improvement on accuracy metrics. Human beings could have 97.53% score for face recognition tasks whereas this ensemble method passes the human level accuracy and gets 98.57% accuracy. On the other hand, this runs much slower than single models.
@@ -160,6 +138,28 @@ python api.py
 
 The both face recognition and facial attribute analysis are covered in the API. You are expected to call these functions as http post methods. Service endpoints will be `http://127.0.0.1:5000/verify` for face recognition and `http://127.0.0.1:5000/analyze` for facial attribute analysis. You should pass input images as base64 encoded string in this case. [Here](https://github.com/serengil/deepface/tree/master/api), you can find a postman project.
 
+**Face Detectors** - [`Demo`](https://youtu.be/GZ2p2hj2H5k)
+
+Face detection and alignment are early stages of a modern face recognition pipeline. [OpenCV haar cascade](https://sefiks.com/2020/02/23/face-alignment-for-face-recognition-in-python-within-opencv/), [SSD](https://sefiks.com/2020/08/25/deep-face-detection-with-opencv-in-python/), [Dlib](https://sefiks.com/2020/07/11/face-recognition-with-dlib-in-python/) and [MTCNN](https://sefiks.com/2020/09/09/deep-face-detection-with-mtcnn-in-python/) methods are wrapped in deepface as a detector. You can optionally pass a custom detector to functions in deepface interface. MTCNN is the default detector if you won't pass any detector.
+
+```python
+backends = ['opencv', 'ssd', 'dlib', 'mtcnn']
+for backend in backends:
+   #face detection and alignment
+   detected_face = DeepFace.detectFace("img.jpg", detector_backend = backend)
+   
+   #face verification
+   obj = DeepFace.verify("img1.jpg", "img2.jpg", detector_backend = backend)
+   
+   #face recognition
+   df = DeepFace.find(img_path = "img.jpg", db_path = "my_db", detector_backend = backend)
+   
+   #facial analysis
+   demography = DeepFace.analyze("img4.jpg", detector_backend = backend)
+```
+
+[MTCNN](https://sefiks.com/2020/09/09/deep-face-detection-with-mtcnn-in-python/) seems to overperform in detection and alignment stages but it is slower than [SSD](https://sefiks.com/2020/08/25/deep-face-detection-with-opencv-in-python/).
+
 **Passing pre-built face recognition models**
 
 You can build models once and pass to deepface functions as well. This speeds you up if you are going to call deepface several times.