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Description
This project demonstrates real-time object detection entirely in the browser using YOLOv8 and WebAssembly (WASM). The front-end is built with React.js and integrates with the webcam to capture and process video frames, while the object detection is handled by a Rust module compiled into WebAssembly.
The advantage of this setup is that all object detection happens locally in the browser, without the need for a server backend, resulting in better performance and user privacy.
Check out the live version here!
Source code can be found on GitHub.
Technologies Used
- React (Client) - for capturing video frames and displaying object detection results
- Rust (WASM) - for real-time object detection using WASM and YOLOv8 model
- Candle ML - minimalist machine learning framework in Rust, used for reimplementing the YOLOv8 model
- YOLOv8 - state-of-the-art object detection model. Only the pre-trained weights (in safetensors format) are used