Dataset Card for Rico

Dataset Summary

Rico is a mobile app UI dataset for building data-driven design applications. The original dataset mines Android apps at runtime and exposes visual, textual, structural, and interactive design properties from more than 9.3k apps across 27 categories and more than 66k unique UI screens. This packaging provides metadata, screenshots, view hierarchies, and semantic annotations as separate configs.

Supported Tasks and Leaderboards

The dataset supports mobile UI understanding, screen hierarchy modeling, semantic element detection, and screenshot-conditioned interface analysis. No public leaderboard is bundled with this Hugging Face dataset.

Languages

Metadata and UI text are primarily English (en), though app screenshots may contain other languages.

Dataset Structure

Data Fields

• default: app and trace metadata.
• ui-screenshots-and-hierarchies-with-semantic-annotations: semantic hierarchy fields including ancestors, klass, bounds, clickable, children, and screenshot.
• ui-screenshots-and-view-hierarchies: screenshot and Android view hierarchy metadata.

Data Splits

Config	Split	Rows
default	metadata	66,261
semantic annotations	train	56,322
semantic annotations	validation	3,314
semantic annotations	test	6,625
view hierarchies	train	56,322
view hierarchies	validation	3,314
view hierarchies	test	6,625

Dataset Creation

Rico was collected from mobile app interaction traces and UI screenshots to support data-driven UI research.

Considerations for Using the Data

The dataset contains mobile app screenshots and UI metadata. It may include app-specific text, brands, and interface content from the collection period.

Additional Information

Licensing Information

The dataset license is not specified in the local loader metadata. Users should verify the upstream terms before redistribution or commercial use.

Citation Information

@inproceedings{deka2017rico,
  title={Rico: A mobile app dataset for building data-driven design applications},
  author={Deka, Biplab and Huang, Zifeng and Franzen, Chad and Hibschman, Joshua and Afergan, Daniel and Li, Yang and Nichols, Jeffrey and Kumar, Ranjitha},
  booktitle={Proceedings of the 30th Annual ACM Symposium on User Interface Software and Technology},
  pages={845--854},
  year={2017}
}