ref: https://github.com/serengil/deepface

# model store 
model checkpoint 放在了 `C:\Users\User\.deepface\weights`

# installation
```
$ pip install deepface
``` 

# Usage: 
```
(deepface_v2) PS path/to/workspace> python
Python 3.7.16 (default, Jan 17 2023, 16:06:28) [MSC v.1916 64 bit (AMD64)] :: Anaconda, Inc. on win32
Type "help", "copyright", "credits" or "license" for more information.
>>> from deepface import DeepFace
```

# Model introduce
```
Deepface is a hybrid face recognition package. It currently wraps many state-of-the-art face recognition models: 
model_name	  Declared LFW Score
VGG-Face	    98.9%
Facenet	        99.2%
Facenet512	    99.6%  drop 
OpenFace	    92.9%
DeepID	        97.4%
Dlib	        99.3%
SFace	        99.5%
ArcFace	        99.5% 阈值调整为1.00  ** 
GhostFaceNet	99.7%   **
Human-beings	97.5%

The default configuration uses VGG-Face model.
```

```
backends = {
    "opencv": OpenCv.OpenCvClient,
    "mtcnn": MtCnn.MtCnnClient,
    "ssd": Ssd.SsdClient,
    "dlib": Dlib.DlibClient,
    "retinaface": RetinaFace.RetinaFaceClient,
    "mediapipe": MediaPipe.MediaPipeClient,
    "yolov8": Yolo.YoloClient,
    "yunet": YuNet.YuNetClient,
    "fastmtcnn": FastMtCnn.FastMtCnnClient,
}
```

```
distance_metric = ['euclidean_l2','cosine','euclidean']
author: Euclidean L2 form seems to be more stable than cosine and regular Euclidean distance based on experiments.
```