metadata
license: mit
task_categories:
- text-generation
language:
- en
tags:
- code
pretty_name: OmniACT
Dataset for OmniACT: A Dataset and Benchmark for Enabling Multimodal Generalist Autonomous Agents for Desktop and Web
Splits:
| split_name | count |
|---|---|
| train | 6788 |
| test | 2020 |
| val | 991 |
Example datapoint:
"2849": {
"task": "data/tasks/desktop/ibooks/task_1.30.txt",
"image": "data/data/desktop/ibooks/screen_1.png",
"box": "data/metadata/desktop/boxes/ibooks/screen_1.json"
},
where:
task- contains natural language description ("Task") along with the corresponding PyAutoGUI code ("Output Script"):
Task: Navigate to see the upcoming titles
Output Script:
pyautogui.moveTo(1881.5,1116.0)
image- screen image where the action is performedbox- This is the metadata used during evaluation. The json format file contains labels for the interactable elements on the screen and their corresponding bounding boxes. They shouldn't be used while inferencing on the test set.
To cite OmniACT, please use:
@misc{kapoor2024omniact,
title={OmniACT: A Dataset and Benchmark for Enabling Multimodal Generalist Autonomous Agents for Desktop and Web},
author={Raghav Kapoor and Yash Parag Butala and Melisa Russak and Jing Yu Koh and Kiran Kamble and Waseem Alshikh and Ruslan Salakhutdinov},
year={2024},
eprint={2402.17553},
archivePrefix={arXiv},
primaryClass={cs.AI}
}