openfoodfacts-ai
This is a tracking repo for all our AI projects. 🍕 🤖🍼
Stars: 220
The openfoodfacts-ai repository is dedicated to tracking and storing experimental AI endeavors, models training, and wishlists related to nutrition table detection, category prediction, logos and labels detection, spellcheck, and other AI projects for Open Food Facts. It serves as a hub for integrating AI models into production and collaborating on AI-related issues. The repository also hosts trained models and datasets for public use and experimentation.
README:
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This repository is to track and store all our experimental AI endeavours, models training, and wishlists.
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The Robotoff repo is the place to integrate them into production, and file more trivial issues.
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Most trained Models and useful datasets are attached to releases of this project or releases on robotoff-models.
A Google spreadsheet also tracks active models.
Here are different experiments.
- Nutrition table detection and extraction (2018 GSoc work by Sagar) - integrated in Robotoff, used for the detection part by the Graphnet and TableNet models
- Nutrition Table Extraction (2020 by Sadok, Yichen and Ramzi) - on Graphnet and TableNet
- Basic nutrition extraction for text tables, already in the Robotoff API
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deployed
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not deployed:
- EM Lyon Category prediction (2020) - not yet evaluated and integrated
- Category from OCR prediction, Laure (Laurel16) (2021) - not yet evaluated and integrated - Categories maybe too general
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on-going project @ https://github.com/openfoodfacts/off-category-classification/issues/2
- We e-meet Mondays at 17:00 Paris Time (16:00 London Time, 21:30 IST, 08:00 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
- Weekly Agenda: 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 decisions taken.
- Labels and Logo detection (Data 4 Good, by Raphael, Charlotte and Antoine - code is duplicated and integrated in Robotoff
- logo-ann (related to logos and labels) - classification using approximate KNN search - deployed in robotoff-ann
- Updating the pre-weighted model to recent publications offers a nice no-effort boost
- Spellcheck (by Wauplin) - code is duplicated and integrated in Robotoff
- ocr-cleaning (please add a description)
- object-detection (related to logos and labels)
You can fork this repository and start your own experiments or use a distinct repository. Please use a AGPL or more permissive but compatible license.
Do not hesitate to join us on #robotoff channel (or #computervision for work relating on images). We will be happy to help you get data, insights and other useful tips.
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Get the data to start playing with food (see also datasets in this project releases)
- You can see many great analysis of Open Food Facts data in notebooks on Kaggle
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