This is a submission for the Hacktoberfest Open-Source AI Challenge Week 1: Touch Grass
What I Built
GrassCheck is a self-contained, minimal and privacy-first web application designed to help people disconnect from their desks and step into nature.
The app gives you a simple, real-world task, such as finding an interestingly shaped leaf, taking a photo of a tree canopy or capturing a patch of green grass. Once you step away from your screen, complete the task and take a photo, GrassCheck runs an open-source vision AI model directly inside your browser to analyse and verify that you completed the challenge.
Code
Download the .html file from GitHub.
How I Built It
GrassCheck is built around Transformers.js by Hugging Face, an open-source library that allows state-of-the-art Machine Learning models to run natively in JavaScript using the ONNX Runtime and WebAssembly.
It uses MobileViT (Xenova/mobilevit-small), an efficient, open-weight vision transformer model optimised for non-desktop devices.
All inference happens completely locally in the browser's runtime.
Built with simple, lightweight HTML, JS and Tailwind CSS.
Why Does Open Innovation Matter?
This project is made possible through the use of open-weight models.
Because the open-weight model runs entirely inside WebAssembly in the browser, photos are never uploaded to any server or third-party cloud.
Using an open-weight model loaded client-side means hosting costs are non-existent. It also makes the application infinitely scalable without the need for server infrastructure.
Also, nature walks and hikes often take people into places with little to no internet connection. Once loaded into the browser cache, open-weight models can perform image classification deep in the forest or on a remote trail with no connectivity at all.
Top comments (0)