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---
license: mit
task_categories:
- text-generation
language:
- en
tags:
- code
pretty_name: OmniACT
---
<img src="intro.png" width="700" title="OmniACT">
Dataset for [OmniACT: A Dataset and Benchmark for Enabling Multimodal Generalist Autonomous Agents for Desktop and Web](https://arxiv.org/abs/2402.17553)
Splits:
| split_name | count |
|------------|-------|
| train | 6788 |
| test | 2020 |
| val | 991 |
Example datapoint:
```json
"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"):
```text
Task: Navigate to see the upcoming titles
Output Script:
pyautogui.moveTo(1881.5,1116.0)
```
- `image` - screen image where the action is performed
- `box` - 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. <i>They shouldn't be used while inferencing on the test set.</i>
<img src="screen_1.png" width="700" title="example screen image">
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}
}
``` |