Combat Effectiveness(CE) Detection

Combat Effectiveness Detection using YOLOv8 and Tensorflow.js

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Combat Effectveness Detection application right in your browser. Serving YOLOv8 in browser using tensorflow.js
with webgl backend.

DEMO

Check it!

• https://dengbuqi.github.io/Combat-Effectiveness-Detection_yolov8-tfjs/

Setup

git clone https://github.com/dengbuqi/Combat-Effectiveness-Detection_yolov8-tfjs
cd Combat-Effectiveness-Detection_yolov8-tfjs
yarn install #Install dependencies

Scripts

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

Model

YOLOv8n model converted to tensorflow.js.

used model : yolov8n
size       : 13 Mb

Use another model

Use another YOLOv8 model.

1. Export YOLOv8 model to tfjs 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="tfjs")

2. Copy yolov8*_web_model to ./public

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

   ...
   // model configs
   const modelName = "yolov8*"; // change to new model name
   ...

4. Done! 😊

Reference

• https://github.com/ultralytics/ultralytics
• https://github.com/Hyuto/yolov8-onnxruntime-web
• https://github.com/Hyuto/yolov8-tfjs