--- title: OmniParser emoji: 💻 colorFrom: gray colorTo: gray sdk: gradio pinned: true models: - microsoft/OmniParser --- # OmniParser: Screen Parsing tool for Pure Vision Based GUI Agent

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[![arXiv](https://img.shields.io/badge/Paper-green)](https://arxiv.org/abs/2408.00203) [![License](https://img.shields.io/badge/License-MIT-yellow.svg)](https://opensource.org/licenses/MIT) 📢 [[Project Page](https://microsoft.github.io/OmniParser/)] [[Blog Post](https://www.microsoft.com/en-us/research/articles/omniparser-for-pure-vision-based-gui-agent/)] [[Models](https://huggingface.co/microsoft/OmniParser)] **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] Both Interactive Region Detection Model and Icon functional description model are released! [Hugginface models](https://huggingface.co/microsoft/OmniParser) - [2024/09] OmniParser achieves the best performance on [Windows Agent Arena](https://microsoft.github.io/WindowsAgentArena/)! ## Install Install environment: ```python 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 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 python gradio_demo.py ``` ## 📚 Citation Our technical report can be found [here](https://arxiv.org/abs/2408.00203). 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}, } ```