DataForGood-2022: Difference between revisions

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=== Summary ===
=== Summary ===
[https://dataforgood.fr/ DataForGood] is an association that mobilize tech to help citizen projects.
[https://dataforgood.fr/ DataForGood] is a French "association" that mobilizes tech to help citizen projects. We are part of [https://dataforgood.fr/projects/tags/saison-10 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.
Main focus this year is on using machine learning to automatically categorize products so as to enable computing Nutri-Score and Éco-Score.
== Get in touch ==
{{Box
| 1    =  Slack channel
| 2    =  [https://openfoodfacts.slack.com/messages/CMT7MQ3S7/ #data4good-fr]
}}


=== Description ===
=== Description ===
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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, nutritional information, eventually images or any feature
* 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 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 its confidence to ask the user whenever needed.


'''Impact:''' augment massively the number of Eco-Score and Nutri-Score we are able to provide on the platform.
'''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 way, for example to compare nutrition data with other products of same category (rank among category).
Categorization is also important in many ways, for example to compare nutrition data with other products of same category (rank within a category).


'''Timeline''':
'''Timeline''':


* 2022-03-12 launch at Data for good launch event
* 2022-03-12: Data 4 Good kickoff event
* 2022-03-16 project kickoff (first working session)
* 2022-03-16: Actual project kickoff (first working session)
* 2022-06-12 theoretical end of the project
* 2022-06-12: Planned end of the project (Data 4 Good Demo Day)


=== Resources / Contributing ===
=== 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
'''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.


You should also join data-for-good slack. To be invited, use https://dataforgood.fr/join/
'''Main information :''' [https://docs.google.com/document/d/13LnTBoBXFWyGGKlGKgmo-utkmiMkapzkVlg39uLKsH4/edit Minutes of the meetings (google docs)]


'''Main repos'''
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


* [https://github.com/openfoodfacts/robotoff/ robotoff]: Our program that handles predictions / insights: good to read : https://openfoodfacts.github.io/robotoff/introduction/architecture/
'''Main GitHub repositories'''
* [https://github.com/openfoodfacts/robotoff-ann/ 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
* '''Project where development happens''' : https://github.com/openfoodfacts/off-category-classification
* [https://github.com/openfoodfacts/robotoff/ robotoff]: Robotoff, our program that handles predictions / insights
  * 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)  
* 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/ openfoodfacts API documentation]
* [https://openfoodfacts.github.io/api-documentation/ Open Food Facts API documentation]


=== Archives ===
=== Archives ===


* Data4Good launch presentation: '''FIXME'''
* [https://docs.google.com/presentation/d/1fMN4di6AN1vz4sC3HYsA1PTya0mDGJYZ9htQYyqkLZQ/edit Data4Good launch presentation (Google Docs)]
[[Category:Project]]
 
[[Category:Previous Project]]
[[Category:Robotoff]]
[[Category:Robotoff]]
[[Category:DataForGood]]

Latest revision as of 14:55, 9 August 2024

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.

Get in touch

Slack channel


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 

 * good to read : https://openfoodfacts.github.io/robotoff/introduction/architecture/

Archives

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