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

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

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

Main Model

YOLOv8n model converted to onnx.

used model : yolov8n.onnx
size       : 13 Mb

NMS

ONNX model to perform NMS operator [CUSTOM].

⚠️ 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.

• https://github.com/ultralytics/ultralytics