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OmniParser: Screen Parsing tool for Pure Vision Based GUI Agent

📢 [Project Page] [Blog Post] [Models] [Huggingface demo]

OmniParser is a comprehensive method for parsing user interface screenshots into structured and easy-to-understand elements, which significantly enhances the ability of GPT-4V to generate actions that can be accurately grounded in the corresponding regions of the interface.

News

[2024/10] OmniParser is the #1 trending model on huggingface model hub (starting 10/29/2024).
[2024/10] Feel free to checkout our demo on huggingface space! (stay tuned for OmniParser + Claude Computer Use)
[2024/10] Both Interactive Region Detection Model and Icon functional description model are released! Hugginface models
[2024/09] OmniParser achieves the best performance on Windows Agent Arena!

Install

Install environment:

conda create -n "omni" python==3.12
conda activate omni
pip install -r requirements.txt

Then download the model ckpts files in: https://huggingface.co/microsoft/OmniParser, and put them under weights/, default folder structure is: weights/icon_detect, weights/icon_caption_florence, weights/icon_caption_blip2.

Finally, convert the safetensor to .pt file.

python weights/convert_safetensor_to_pt.py

Examples:

We put together a few simple examples in the demo.ipynb.

Gradio Demo

To run gradio demo, simply run:

python gradio_demo.py

Model Weights License

For the model checkpoints on huggingface model hub, please note that icon_detect model is under AGPL license since it is a license inherited from the original yolo model. And icon_caption_blip2 & icon_caption_florence is under MIT license. Please refer to the LICENSE file in the folder of each model: https://huggingface.co/microsoft/OmniParser.

📚 Citation

Our technical report can be found here. If you find our work useful, please consider citing our work:

@misc{lu2024omniparserpurevisionbased,
      title={OmniParser for Pure Vision Based GUI Agent}, 
      author={Yadong Lu and Jianwei Yang and Yelong Shen and Ahmed Awadallah},
      year={2024},
      eprint={2408.00203},
      archivePrefix={arXiv},
      primaryClass={cs.CV},
      url={https://arxiv.org/abs/2408.00203}, 
}