Overview
The NextJS ONNX Runtime Web Template serves as an outstanding foundation for developers looking to leverage ONNX Runtime for machine learning inferencing directly in the browser. This template, built on Next.js, integrates seamlessly with PyTorch, enabling the processing of images using popular models such as SqueezeNet, ResNet, and MobileNet. It offers a robust platform for testing and deploying applications with diverse machine-learning capabilities.
Equipped with essential tools such as webpack and typescript, this template streamlines the development process, making it easy to get started with real-time inferencing. Its user-friendly approach caters not only to seasoned developers but also provides an engaging environment for newcomers to experiment with machine learning in web applications.
Features
- Pre-configured Environment: Built with webpack, onnxruntime-web, React, and TypeScript, this template allows for quick setup and easy local development.
- Real-time Image Processing: Supports image input to run inference using popular models, providing immediate results in the browser.
- API Route Integration: Easily accessible API routes enable seamless integration of machine learning functionalities within React applications.
- Automatic Hot Reloading: The development environment automatically updates the browser when pages are edited, enhancing the testing and debugging experience.
- Deployment Ready: Simplified deployment instructions for Azure Static Web Apps ensure a smooth transition from local development to production.
- Contributor-Friendly: Welcomes contributions and improvements, promoting community engagement and innovation in machine learning applications.
- Interactive Demos: Includes a portal for users to experience ONNX Runtime Web in action with various examples to understand its capabilities quickly.
