Object Segmentation application right in your browser. Serving YOLOv5 segmentation in browser using onnxruntime-web with wasm backend.

git clone https://github.com/Hyuto/yolov5-seg-onnxruntime-web.git
cd yolov5-seg-onnxruntime-web
yarn install # Install dependencies

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

Main Model

YOLOv5n-seg model converted to onnx.

used model : yolov5n-seg.onnx
size       : 8 Mb

NMS

ONNX model to perform NMS operator [CUSTOM].

nms-yolov5.onnx

Mask

ONNX model to produce mask for every object detected [CUSTOM].

mask-yolov5-seg.onnx

⚠️ Size Overload : used YOLOv5 segmentation model in this repo is the smallest with size of 8 MB, so other models is definitely bigger than this which can cause memory problems on browser.

Use another YOLOv5 model.

1. Clone yolov5 repository

   git clone https://github.com/ultralytics/yolov5.git && cd yolov5

   Install requirements.txt first

   pip install -r requirements.txt

2. Export model to onnx format

   python export.py --weights yolov5*-seg.pt --include onnx --img 640

3. Copy yolov5*-seg.onnx to ./public/model

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

   ...
   // configs
   const modelName = "yolov5*-seg.onnx"; // change to new model name
   const modelInputShape = [1, 3, 640, 640];
   const topk = 100;
   const iouThreshold = 0.4;
   const confThreshold = 0.2;
   const classThreshold = 0.2;
   ...

5. Done! 😊

Note : Custom Trained Model

For custom trained YOLOv5 Segmentation model please update labels.json

• https://github.com/ultralytics/yolov5
• https://github.com/UNeedCryDear/yolov5-seg-opencv-dnn-cpp