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wasm-object-detection

★ 34 Open GitHub ↗

Real-time camera feed object detection with React.js, WASM, Rust, Candle ML, and YOLO

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# WASM + YOLO Real-time Object Detection

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](https://object-detection.martishin.com/)!

## Running Locally

### Prerequisites

- [Node.js & npm](https://nodejs.org/en/download/package-manager) - for the React client
- [Rust](https://www.rust-lang.org/tools/install) - for the WebAssembly module

### Steps

1. Clone the repository
   ```bash
   git clone https://github.com/martishin/react-rust-wasm-yolo-object-detection
   cd react-rust-wasm-yolo-object-detection
   ```
   
2. Install client dependencies
   ```bash
   cd client
   npm install
   ```

3. Running the React client  
   ```bash
   npm run dev
   ```

The client will be running at `http://localhost:5173/`
4. If you want to rebuild WASM module
   ```bash
   cd yolo_wasm
   make build
    ```
   Compiled module will be placed into `client/src/model`

## 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 Rust machine learning framework, used for reimplementing the YOLOv8 model
- **YOLOv8** - state-of-the-art object detection model. Only the pre-trained weights (in safetensors format) are used

wasm-object-detection

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