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YOLOv8 with onnxruntime-web

love react onnxruntime-web opencv.js


Object Detection application right in your browser. Serving YOLOv8 in browser using onnxruntime-web with wasm backend.

Setup

git clone https://github.com/Hyuto/yolov8-onnxruntime-web.git
cd yolov8-onnxruntime-web
yarn install # Install dependencies

Scripts

yarn start # Start dev server
yarn build # Build for productions

Models

Main Model

YOLOv8n model converted to onnx.

used model : yolov8n.onnx
size       : 13 Mb

NMS

ONNX model to perform NMS operator [CUSTOM].

nms-yolov8.onnx

Use another model

⚠️ Size Overload : used YOLOv8n model in this repo is the smallest with size of 13 MB, so other models is definitely bigger than this which can cause memory problems on browser.

Use another YOLOv8 model.

  1. Export YOLOv8 model to onnx format. Read more on the official documentation

    from ultralytics import YOLO
    
    # Load a model
    model = YOLO("yolov8n.pt")  # load an official model
    
    # Export the model
    model.export(format="onnx")
  2. Copy yolov8*.onnx to ./public/model

  3. Update modelName in App.jsx to new model name

    ...
    // configs
    const modelName = "yolov8*.onnx"; // change to new model name
    const modelInputShape = [1, 3, 640, 640];
    const topk = 100;
    const iouThreshold = 0.4;
    const scoreThreshold = 0.2;
    ...
  4. Done! 😊

Note: Custom Trained YOLOv8 Models

Please update src/utils/labels.json with your custom YOLOv8 classes.

Reference

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YOLOv8 right in your browser with onnxruntime-web

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  • JavaScript 74.3%
  • CSS 15.0%
  • HTML 10.7%