---
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}
}
```