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---
license: apache-2.0
dataset_info:
features:
- name: screenId
dtype: int64
- name: bbox
sequence: float64
- name: captions
sequence: string
- name: file_name
dtype: string
- name: view_hierarchy
dtype: string
- name: file_name_semantic
dtype: string
- name: semantic_annotations
dtype: string
- name: app_package_name
dtype: string
- name: play_store_name
dtype: string
- name: category
dtype: string
- name: average_rating
dtype: float64
- name: number_of_ratings
dtype: string
- name: number_of_downloads
dtype: string
- name: file_name_icon
dtype: string
- name: image
dtype: image
- name: image_icon
dtype: image
- name: image_semantic
dtype: image
splits:
- name: train
num_bytes: 17838159558.65
num_examples: 71350
download_size: 2142847271
dataset_size: 17838159558.65
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
task_categories:
- image-to-text
language:
- en
tags:
- synthetic
- screens
pretty_name: RICO SCA
size_categories:
- 10K<n<100K
---
# Dataset Card for RICO SCA (SeeClick cache)
This is the [SeeClick](https://github.com/njucckevin/SeeClick/tree/main) cache of a syntehtically generated dataset following [RICO SCA's](https://github.com/google-research/google-research/tree/master/seq2act) generation procedure.
It consists of approximately 170k captions across 70k widgets and 18k screens.
## Dataset Details
### Dataset Description
This is a widget captioning (referring expression comprehension/generation) dataset.
- **Curated by:** Google Research, Nanjing University
- **Language(s) (NLP):** en
- **License:** apache-2.0
### Dataset Sources
- **Repository:** [RICO SCA Repo](https://github.com/google-research/google-research/tree/master/seq2act) / [SeeClick Repo](https://github.com/njucckevin/SeeClick/tree/main)
- **Paper [optional]:** [RICO SCA paper](https://arxiv.org/abs/2005.03776) / [SeeClick paper](https://arxiv.org/abs/2401.10935)
## Uses
### Direct Use
Training models that can be used to understand screens, explain mobile interaces, and act in an automated, digital context.
## Dataset Structure
- `screenId`: Unique RICO screen ID
- `image`: RICO screenshot
- `image_icon`: Google Play Store icon for the app
- `image_semantic`: Semantic RICO screenshot; details are abstracted away to main visual UI elements
- `file_name`: Image local filename
- `file_name_icon`: Icon image local filename
- `file_name_semantic`: Screenshot Image as a semantic annotated image local filename
- `captions`: A list of string captions
- `bbox`: The bounding box for the widget being captioned, relatively scaled with the image size so that coordinates are in [0, 1]
- `app_package_name`: Android package name
- `play_store_name`: Google Play Store name
- `category`: Type of category of the app
- `number_of_downloads`: Number of downloads of the app (as a coarse range string)
- `number_of_ratings`: Number of ratings of the app on the Google Play store (as of collection)
- `average_rating`: Average rating of the app on the Google Play Store (as of collection)
- `semantic_annotations`: Reduced view hierarchy, to the semantically-relevant portions of the full view hierarchy. It corresponds to what is visualized in `image_semantic` and has a lot of details about what's on screen. It is stored as a JSON object string.
## Dataset Creation
### Curation Rationale
- RICO rationale: Create a broad dataset that can be used for UI automation. An explicit goal was to develop automation software that can validate an app's design and assess whether it achieves its stated goal.
- SCA rationale: Primarily to benefit efforts to create more assitive technologies for visually-impaired users
### Source Data
- RICO: Mobile app screenshots, collected on Android devices.
- SCA: Generated from a trained model
## Citation
### RICO
**BibTeX:**
```misc
@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}
}
```
**APA:**
Deka, B., Huang, Z., Franzen, C., Hibschman, J., Afergan, D., Li, Y., ... & Kumar, R. (2017, October). Rico: A mobile app dataset for building data-driven design applications. In Proceedings of the 30th annual ACM symposium on user interface software and technology (pp. 845-854).
### RICO SCA
**BibTeX:**
```misc
@inproceedings{li2020mapping,
title={Mapping Natural Language Instructions to Mobile UI Action Sequences},
author={Li, Yang and He, Jiacong and Zhou, Xin and Zhang, Yuan and Baldridge, Jason},
booktitle={Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics},
pages={8198--8210},
year={2020}
}
```
**APA:**
Li, Y., He, J., Zhou, X., Zhang, Y., & Baldridge, J. (2020, July). Mapping Natural Language Instructions to Mobile UI Action Sequences. In Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics (pp. 8198-8210).
### SeeClick
**BibTeX:**
```misc
@misc{cheng2024seeclick,
title={SeeClick: Harnessing GUI Grounding for Advanced Visual GUI Agents},
author={Kanzhi Cheng and Qiushi Sun and Yougang Chu and Fangzhi Xu and Yantao Li and Jianbing Zhang and Zhiyong Wu},
year={2024},
eprint={2401.10935},
archivePrefix={arXiv},
primaryClass={cs.HC}
}
```
**APA:**
Cheng, K., Sun, Q., Chu, Y., Xu, F., Li, Y., Zhang, J., & Wu, Z. (2024). Seeclick: Harnessing gui grounding for advanced visual gui agents. arXiv preprint arXiv:2401.10935.
## Dataset Card Authors
Hunter Heidenreich, Roots Automation
## Dataset Card Contact
hunter "DOT" heidenreich "AT" rootsautomation "DOT" com |