The COCO dataset occasionally contains annotation errors. A systematic way to report and correct these errors would enhance the dataset's accuracy and utility.
Feature Proposal:
Implement a crowdsourced error correction mechanism, allowing users to report and rectify annotation errors directly.
Key Points:
- User Reporting Interface: Simple UI for error reporting within the dataset platform.
- Error Verification: Process for validating reported errors, possibly automated or manual by maintainers.
- Community Engagement: Encourage user participation in dataset improvement.
- Enhanced Accuracy: Continuous user feedback can improve dataset reliability over time.
Example Errors:
According to my estimates, as of December 2023, around 4% of the images in the COCO dataset contain annotations with at least one misspelled word.




