Performing Object Detection for YOLOv9 with ONNX and ONNXRuntime

• Check the requirements.txt file.
• For ONNX, if you have a NVIDIA GPU, then install the onnxruntime-gpu, otherwise use the onnxruntime library.

git clone https://github.com/danielsyahputra/yolov9-onnx.git
cd yolov9-onnx
pip install -r requirements.txt

For Nvidia GPU computers: pip install onnxruntime-gpu

Otherwise: pip install onnxruntime

You can download the onnx model and class metadata file on the link below

https://drive.google.com/drive/folders/1QH5RCF5WOk53SfdzsHTFkXAdzMLbbQeO?usp=sharing

List the arguments available in main.py file.

• --source: Path to image or video file
• --weights: Path to yolov9 onnx file (ex: weights/yolov9-c.onnx)
• --classes: Path to yaml file that contains the list of class from model (ex: weights/metadata.yaml)
• --score-threshold: Score threshold for inference, range from 0 - 1
• --conf-threshold: Confidence threshold for inference, range from 0 - 1
• --iou-threshold: IOU threshold for inference, range from 0 - 1
• --image: Image inference mode
• --video: Video inference mode
• --show: Show result on pop-up window
• --device: Device use for inference, default = cpu.

Note: If you want to use cuda for inference, please make sure you are already install onnxruntime-gpu before running the script.

This code provides two modes of inference, image and video inference. Basically, you just add --image flag for image inference and --video flag for video inference when you are running the python script.

If you have your own custom model, don't forget to provide a yaml file that consists the list of class that your model want to predict. This is example of yaml content for defining your own classes:

names:
  0: person
  1: bicycle
  2: car
  3: motorcycle
  4: airplane
  .
  .
  .
  .
  n: object

python main.py --source assets/sample_image.jpeg --weights weights/yolov9-c.onnx --classes weights/metadata.yaml --image

python main.py --source assets/road.mp4 --weights weights/yolov9-c.onnx --classes weights/metadata.yaml --video

• YOLOv9 model: https://github.com/WongKinYiu/yolov9