YOLOv5_Face.md

YOLOv5-Face usage

NOTE: The yaml file is not required.

• Convert model
• Edit the config_infer_primary_yoloV5_face file

Convert model

1. Download the YOLOv5-Face repo and install the requirements

git clone https://github.com/deepcam-cn/yolov5-face.git
cd yolov5-face
pip3 install -r requirements.txt
python3 setup.py install
pip3 install onnx onnxsim onnxruntime

NOTE: It is recommended to use Python virtualenv.

2. Copy conversor

Copy the export_yoloV5_face.py file from DeepStream-Yolo-Face/utils directory to the yolov5-face folder.

3. Download the model

Download the pt file from YOLOv5-Face repo.

NOTE: You can use your custom model.

4. Convert model

Generate the ONNX model file (example for YOLOv5n-Face)

python3 export_yoloV5_face.py -w yolov5n-face.pt --dynamic

NOTE: To change the inference size (defaut: 640)

-s SIZE
--size SIZE
-s HEIGHT WIDTH
--size HEIGHT WIDTH

Example for 1280

-s 1280

or

-s 1280 1280

NOTE: To simplify the ONNX model (DeepStream >= 6.0)

--simplify

NOTE: To use dynamic batch-size (DeepStream >= 6.1)

--dynamic

NOTE: To use static batch-size (example for batch-size = 4)

--batch 4

5. Copy generated files

Copy the generated ONNX model file to the DeepStream-Yolo-Face folder.

Edit the config_infer_primary_yoloV5_face file

Edit the config_infer_primary_yoloV5_face.txt file according to your model (example for YOLOv5n-Face)

[property]
...
onnx-file=yolov5n-face.onnx
...

NOTE: The YOLOv5-Face resizes the input with center padding. To get better accuracy, use

[property]
...
maintain-aspect-ratio=1
symmetric-padding=1
...