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Top AI Repos tracks AI repositories on GitHub and answers two different questions about each one: is it moving right now, and would you bet a product on it.
Top AI Repos tracks AI repositories on GitHub and answers two different questions about each one: is it moving right now, and would you bet a product on it.
Recycle your plastic better with Artificial Intelligence ♻️
| Date | Stars |
|---|---|
| 2026-07-24 | 282 |
| 2026-07-25 | 282 |
| 2026-07-28 | 282 |
| 2026-07-30 | 282 |
| 2026-08-06 | 282 |
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# EcoSnap
https://user-images.githubusercontent.com/7581348/208559445-a449cef6-0ae1-4c08-b9a5-c591062c3a3e.mp4
Recycle your plastic better with Artificial Intelligence ♻️
EcoSnap tells you how and where to recycle your items from a simple picture, with advice tailored to your location. We built this product in a week for [Ben's Bites AI Hackathon](https://alyssax.substack.com/p/we-built-an-ai-recycling-app-in-a).
👉 [Try it now - it's free with no sign in needed](https://ecosnap.vercel.app)
[](https://vercel.com/new/clone?repository-url=https://github.com/alyssaxuu/ecosnap)
> You can support this project (and many others) through [GitHub Sponsors](https://github.com/sponsors/alyssaxuu)! ❤️
Made by [Alyssa X](https://twitter.com/alyssaxuu) & [Leo](https://www.linkedin.com/in/leonorfurtado/). Read more about how we built this [here](https://alyssax.substack.com/p/we-built-an-ai-recycling-app-in-a).
<a href="https://www.producthunt.com/posts/ecosnap?utm_source=badge-featured&utm_medium=badge&utm_souce=badge-ecosnap" target="_blank"><img src="https://api.producthunt.com/widgets/embed-image/v1/featured.svg?post_id=374164&theme=neutral" alt="EcoSnap - Recycle your plastic better with Artificial Intelligence | Product Hunt" style="width: 250px; height: 54px;" width="250" height="54" /></a>
## Table of contents
- [Features](#features)
- [Installation](#installation)
- [The AI model](#the-ai-model)
- [Dataset](#data)
- [Training your own model](#training)
- [Prediction](#prediction)
- [The feedback loop](#feedback)
- [Credit](#credit)
- [Libraries used](#libraries-used)
## Features
📸 Snap or upload a picture of a plastic code<br>
📱 Install the PWA on your phone for easy access<br>
🔍 Search for specific item to know how to dispose of it<br>
♻️ Learn how to recycle effectively using AI<br>
🥤 Keep track of how many plastic items you've recycled<br>
🌍 Change your location for specific advice<br>
✨...and much more to come - all for free & no sign in needed!
## Installation
You can deploy to Vercel directly by [clicking here](https://vercel.com/new/clone?repository-url=https://github.com/alyssaxuu/ecosnap).
**Important:** Make sure to update the environment variable for [NEXT_PUBLIC_MODEL_URL](https://github.com/alyssaxuu/ecosnap/blob/a9c7e7e1ec19f106db69abd6d66be558bd21445a/.env#L16) in the .env file, and set it to an absolute URL where you host the [model.json](https://github.com/alyssaxuu/ecosnap/tree/main/ml/models/efficient_net/10/predict) (make sure to include the other shard bin files alongside the JSON).
## The AI Model
### Data
The model was trained on image examples of the 7 different resin codes, the data for this can be found in `ml/seven_plastics`. It is a combination of the following [Kaggle Dataset](https://www.kaggle.com/datasets/piaoya/plastic-recycling-codes) and images collected by the authors and contributors.
### Training
The final model was trained using [TensorFlow's EfficientNet](https://www.tensorflow.org/api_docs/python/tf/keras/applications/efficientnet_v2/EfficientNetV2B0) implementation, the model weights were frozen for transfer learning, so the model could learn the resin codes faster! The model was trained in `Python` on a GPU-powered machine, for faster training! You can find the training script in `ml/train.py` and try it for yourself, there you will see that different meta architectures and parameters were experimented with before arriving at the final model.
### Prediction
To predict the plastic resin code, the model had to be integrated with the front end app for real-time results, to do this we had to convert the model in a way that was compatible with [TensorFlow.js](https://www.tensorflow.org/js). We used [Web Workers](https://github.com/alyssaxuu/ecosnap/blob/main/components/Worker.js) to prevent the main thread from being block while running the prediction in the client.
The app passes Excerpt of 5,734 characters
Read on GitHubWould you bet a product on this? Bounded 0–100 and slow moving.
matched fp:efbd40aa1154375e, topic:tensorflow