Combat Effectiveness Detection using YOLOv8 and Tensorflow.js

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

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

Scripts

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

Model

YOLOv8n model converted to tensorflow.js.

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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

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   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

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   ... // 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

heartbeat-js + FaceAPI heart pulse rate monitoring

This project combines the heartbeat-js and FaceAPI
By detecting the human face using FaceAPI, we can estimate the heart pulse rate using rPPG method implemented by hearbeat-js project.

Demo

Check the demo here~

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Quick Start

Create a new post

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$ hexo new "My New Post"

More info: Writing

Run server

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$ hexo server

More info: Server

Generate static files

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$ hexo generate

More info: Generating

Deploy to remote sites

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$ hexo deploy

More info: Deployment