DataForGood-2022: Difference between revisions
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=== Summary === | === Summary === | ||
[https://dataforgood.fr/ DataForGood] is an association that mobilize tech to help | [https://dataforgood.fr/ DataForGood] is an association that mobilize tech to help citizen projects. | ||
Main focus this year is on using machine learning to automatically categorize products so as to enable computing Nutri-Score and Éco-Score. | Main focus this year is on using machine learning to automatically categorize products so as to enable computing Nutri-Score and Éco-Score. | ||
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'''Expected outcomes''': Have a new machine learning model, ready to deploy in production: | '''Expected outcomes''': Have a new machine learning model, ready to deploy in production: | ||
* That use more features to predict category: OCR data, | * That use more features to predict category: OCR data, nutritional information, eventually images or any feature | ||
* Precision should be very high to be able to apply category automatically but the model should use it's confidence to ask for user when needed | * Precision should be very high to be able to apply category automatically but the model should use it's confidence to ask for user when needed | ||
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* 2022-03-12 launch at Data for good launch event | * 2022-03-12 launch at Data for good launch event | ||
* 2022-03-16 project | * 2022-03-16 project kickoff (first working session) | ||
* 2022-06-12 theoretical end of the project | * 2022-06-12 theoretical end of the project | ||
=== Resources / Contributing === | === Resources / Contributing === | ||
The weekly meeting for this project is every | The weekly meeting for this project is every Wednesday 20.00. We have a meeting room at https://meet.jit.si/DFG-OPENFOODFACTS | ||
You should also join data-for-good slack. To be invited, use https://dataforgood.fr/join/ | You should also join data-for-good slack. To be invited, use https://dataforgood.fr/join/ | ||
Revision as of 15:02, 15 March 2022
Summary
DataForGood is an association that mobilize tech to help citizen projects.
Main focus this year is on using machine learning to automatically categorize products so as to enable computing Nutri-Score and Éco-Score.
Description
Status: started
Expected outcomes: Have a new machine learning model, ready to deploy in production:
- That use more features to predict category: OCR data, nutritional information, eventually images or any feature
- Precision should be very high to be able to apply category automatically but the model should use it's confidence to ask for user when needed
Impact: augment massively the number of Eco-Score and Nutri-Score we are able to provide on the platform.
Categorization is also important in many way, for example to compare nutrition data with other products of same category (rank among category).
Timeline:
- 2022-03-12 launch at Data for good launch event
- 2022-03-16 project kickoff (first working session)
- 2022-06-12 theoretical end of the project
Resources / Contributing
The weekly meeting for this project is every Wednesday 20.00. We have a meeting room at https://meet.jit.si/DFG-OPENFOODFACTS
You should also join data-for-good slack. To be invited, use https://dataforgood.fr/join/
Main repos
- robotoff: Our program that handles predictions / insights: good to read : https://openfoodfacts.github.io/robotoff/introduction/architecture/
- robotoff-ann is a complement to robotoff that focus on logo detection and embedding (we use nearest neighbors to classify logos)
- last training of category model (using only title and ingredients): https://github.com/kulizhsy/off-category-classification
- All trained models are published as "releases" on https://github.com/openfoodfacts/openfoodfacts-ai
- openfoodfacts API documentation
Archives
- Data4Good launch presentation: FIXME