Difference between revisions of "DataForGood-2022"
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The weekly meeting for this project is Wednesdays at 20.00 (French time). We use Google Meet. | The weekly meeting for this project is Wednesdays at 20.00 (French time). We use Google Meet. | ||
| − | [https://docs.google.com/document/d/13LnTBoBXFWyGGKlGKgmo-utkmiMkapzkVlg39uLKsH4/edit Minutes of the meetings (google docs)] | + | '''Main information :''' [https://docs.google.com/document/d/13LnTBoBXFWyGGKlGKgmo-utkmiMkapzkVlg39uLKsH4/edit Minutes of the meetings (google docs)] |
You should also join the Open Food Facts and Data 4 Good slacks. To be invited, use https://dataforgood.fr/join/ and https://slack.openfoodfacts.org | You should also join the Open Food Facts and Data 4 Good slacks. To be invited, use https://dataforgood.fr/join/ and https://slack.openfoodfacts.org | ||
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'''Main GitHub repositories''' | '''Main GitHub repositories''' | ||
| + | * '''Project where development happens''' : https://github.com/openfoodfacts/off-category-classification | ||
* [https://github.com/openfoodfacts/robotoff/ robotoff]: Robotoff, our program that handles predictions / insights | * [https://github.com/openfoodfacts/robotoff/ robotoff]: Robotoff, our program that handles predictions / insights | ||
* good to read : https://openfoodfacts.github.io/robotoff/introduction/architecture/ | * good to read : https://openfoodfacts.github.io/robotoff/introduction/architecture/ | ||
* [https://github.com/openfoodfacts/robotoff-ann/ robotoff-ann] is a complement to robotoff that focuses on logo detection and embedding (we use nearest neighbors to classify logos) | * [https://github.com/openfoodfacts/robotoff-ann/ robotoff-ann] is a complement to robotoff that focuses on logo detection and embedding (we use nearest neighbors to classify logos) | ||
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* All trained models are published as "releases" on https://github.com/openfoodfacts/openfoodfacts-ai | * All trained models are published as "releases" on https://github.com/openfoodfacts/openfoodfacts-ai | ||
* [https://openfoodfacts.github.io/api-documentation/ Open Food Facts API documentation] | * [https://openfoodfacts.github.io/api-documentation/ Open Food Facts API documentation] | ||
Revision as of 13:27, 16 May 2022
Summary
DataForGood is a French "association" that mobilizes tech to help citizen projects. We are part of season #10, this is our 4th season.
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 uses 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 its confidence to ask the user whenever needed.
Impact: massively increase the number of Eco-Scores and Nutri-Scores we are able to provide on the Open Food Facts platform.
Categorization is also important in many ways, for example to compare nutrition data with other products of same category (rank within a category).
Timeline:
- 2022-03-12: Data 4 Good kickoff event
- 2022-03-16: Actual project kickoff (first working session)
- 2022-06-12: Planned end of the project (Data 4 Good Demo Day)
Resources / Contributing
There is a spreadsheet to have our coordinates see on slack channel.
The weekly meeting for this project is Wednesdays at 20.00 (French time). We use Google Meet.
Main information : Minutes of the meetings (google docs)
You should also join the Open Food Facts and Data 4 Good slacks. To be invited, use https://dataforgood.fr/join/ and https://slack.openfoodfacts.org
Main GitHub repositories
- Project where development happens : https://github.com/openfoodfacts/off-category-classification
- robotoff: 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 focuses on logo detection and embedding (we use nearest neighbors to classify logos)
- All trained models are published as "releases" on https://github.com/openfoodfacts/openfoodfacts-ai
- Open Food Facts API documentation