YOLOv8_Pose.md

YOLOv8-Pose usage

NOTE: The yaml file is not required.

• Convert model
• Edit the config_infer_primary_yoloV8_pose file

Convert model

1. Download the YOLOv8 repo and install the requirements

git clone https://github.com/ultralytics/ultralytics.git
cd ultralytics
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_yoloV8_pose.py file from DeepStream-Yolo-Pose/utils directory to the ultralytics folder.

3. Download the model

Download the pt file from YOLOv8 releases (example for YOLOv8s-Pose)

wget https://github.com/ultralytics/assets/releases/download/v0.0.0/yolov8s-pose.pt

NOTE: You can use your custom model.

4. Convert model

Generate the ONNX model file (example for YOLOv8s-Pose)

python3 export_yoloV8_pose.py -w yolov8s-pose.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-Pose folder.

Edit the config_infer_primary_yoloV8_pose file

Edit the config_infer_primary_yoloV8_pose.txt file according to your model (example for YOLOv8s-Pose)

[property]
...
onnx-file=yolov8s-pose.onnx
...
parse-bbox-func-name=NvDsInferParseYoloPose
...