ONNX Runtime - Object detection with YOLO

This example makes use of the ONNX Runtime to run object detection using a YOLO model. It differs from the YOLO example in that the later uses the Ultralytics SDK (Pytorch).

The YOLO ONNX model was obtained by simply exporting the YOLOv8n model to the ONNX format.

The pre-processing and post-processing is implemented using OpenCV and NumPy.

Requirements

Pipeless: Check the installation guide.
Python OpenCV and NumPy packages. Install them by running:

pip install opencv-python numpy

Run the example

Create an empty Pipeless project

pipeless init my-project --template empty # Using the empty template we avoid the interactive shell
cd my-project

Feel free to change my-project by any name you want.

Download the stage folder

wget -O - https://github.com/pipeless-ai/pipeless/archive/main.tar.gz | tar -xz --strip=2 "pipeless-main/examples/onnx-yolo"

(Optional) If you have CUDA or TensorRT installed you can enable them at `process.json`

process.json

{
    "runtime": "onnx",
    "model_uri": "https://pipeless-public.s3.eu-west-3.amazonaws.com/yolov8n.onnx",
    "inference_params": {
        "execution_provider": "tensorrt"
    }
}

Start Pipeless

The following command leaves Pipeless running on the current terminal

pipeless start --stages-dir .

Provide a stream

Open a new terminal and run:

pipeless add stream --input-uri "v4l2" --output-uri "screen" --frame-path "onnx-yolo"

This command assumes you have a webcam available, if you don't just change the input URI.

ONXX Runtime - Candy filter Play a piano with your eyes by looking to the notes