ONNX Runtime Web Example on CPU and GPU

This project demonstrates the performance differences between running ONNX Runtime inference sessions on CPU and GPU in a web environment. The example uses ONNX Runtime Web and OpenCV.js to highlight the efficiency and speed benefits of GPU acceleration.

Features

• ONNX Runtime Web Integration: Utilizes ONNX Runtime Web to run machine learning models directly in the browser.
• CPU and GPU Comparison: Provides a comparison of inference speeds between CPU and GPU.
• OpenCV.js: Uses OpenCV.js for image processing tasks.
• Performance Metrics: Displays elapsed time for inference to illustrate performance differences.

Getting Started

Prerequisites

• A modern web browser (e.g., Chrome, Firefox)
• Basic knowledge of JavaScript and HTML

Setup

1. Open the project directory in your preferred code editor.

2. Start a local server:

   • You can use Python's built-in HTTP server for this:

     python3 -m http.server

     This will start a server on port 8000 by default.

3. Open your web browser and navigate to http://localhost:8000.

4. Explore the project and compare the performance of ONNX Runtime inference on CPU and GPU.

Contributing

Contributions are welcome! Please fork the repository, make your changes, and submit a pull request.

License