Top AI Repos — open-source AI, indexed and scored
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.
This is a tracking repo for all our AI projects. 🍕 🤖🍼
| Date | Stars |
|---|---|
| 2026-07-24 | 272 |
| 2026-07-25 | 272 |
| 2026-07-28 | 273 |
| 2026-07-30 | 273 |
| 2026-08-06 | 273 |
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growth rate 0.00%/day
# openfoodfacts-ai     [](https://codecov.io/gh/openfoodfacts/openfoodfacts-ai) [](https://github.com/psf/black) <picture> <source media="(prefers-color-scheme: dark)" srcset="https://static.openfoodfacts.org/images/logos/off-logo-horizontal-dark.png?refresh_github_cache=1"> <source media="(prefers-color-scheme: light)" srcset="https://static.openfoodfacts.org/images/logos/off-logo-horizontal-light.png?refresh_github_cache=1"> <img height="48" src="https://static.openfoodfacts.org/images/logos/off-logo-horizontal-light.svg"> </picture> ## ❗ Before you read on * This repository is to track and store all our experimental AI endeavours, models training, and wishlists. * The [Robotoff repo](https://github.com/openfoodfacts/robotoff) is the place to integrate them into production, and file more trivial issues. * Most trained Models and useful datasets are attached to [releases of this project](https://github.com/openfoodfacts/openfoodfacts-ai/releases) or [releases on robotoff-models](https://github.com/openfoodfacts/robotoff-models/releases). [A Google spreadsheet](https://docs.google.com/spreadsheets/d/1p2tvA5ySm0RJpTjUwT3fFrDJNJLXNlVTkxxU-izTIMA/edit#gid=0) also tracks active models. ## [What can I work on ?](https://github.com/openfoodfacts/openfoodfacts-ai/issues/76) ## 🔬 Projects Here are different experiments. ### Nutrition table * [Nutrition table detection and extraction (2018 GSoc work by Sagar)](./GSoC2018/table_detection) - integrated in Robotoff, used for the detection part by the Graphnet and TableNet models * [Nutrition Table Extraction (2020 by Sadok, Yichen and Ramzi)](./nutrition-table-extraction/data_exploration/README.md) - on Graphnet and TableNet * Basic nutrition extraction for text tables, already in the Robotoff API ### Category prediction * deployed * [Google.org fellowship (2021) - Category prediction based on ingredients and title](https://github.com/openfoodfacts/off-category-classification/) - deployed * not deployed: * [EM Lyon Category prediction (2020)](./ai-emlyon/README.md) - not yet evaluated and integrated * [Category from OCR prediction, Laure (Laurel16) (2021)](https://github.com/Laurel16/OpenFoodFactsCategorizer) - not yet evaluated and integrated - Categories maybe too general * on-going project @ https://github.com/openfoodfacts/off-category-classification/issues/2 ## Weekly meetings - We e-meet Tuesdays at 11:00 Paris Time (10:00 London Time, 15:30 IST, 02:00 AM PT) -  Video call link: https://meet.google.com/qvv-grzm-gzb - Join by phone: https://tel.meet/qvv-grzm-gzb?pin=9965177492770 - Add the Event to your Calendar by [adding the Open Food Facts community calendar to your calendar](https://wiki.openfoodfacts.org/Events) - [Weekly Agenda](https://drive.google.com/open?id=1RUfmWHjtFVaBcvQ17YfXu6FW6oRFWg-2lncljG0giKI): please add the Agenda items as early as you can. Make sure to check the Agenda items in advance of the meeting, so that we have the most informed discussions possible. - The meeting will handle Agenda items first, and if time permits, collaborative bug triage. - We strive to timebox the core of the meeting (decision making) to 30 minutes, with an optional free discussion/live debugging afterwards. - We take comprehensive notes in the Weekly Agenda of agenda item discussions and of dec
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Read on GitHubWould you bet a product on this? Bounded 0–100 and slow moving.
matched fp:c0b1677c00bad5f6, topic:deep-learning, topic:neural-network
matched fp:c0b1677c00bad5f6, topic:computer-vision
matched fp:c0b1677c00bad5f6, topic:nlp