OmniParser: Screen Parsing tool for Pure Vision Based GUI Agent

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📢 [Project Page] [V2 Blog Post] [Models V2] [Models V1.5] [HuggingFace Space 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

• [2025/3] We support local logging of trajecotry so that you can use OmniParser+OmniTool to build training data pipeline for your favorate agent in your domain. [Documentation WIP]
• [2025/3] We are gradually adding multi agents orchstration and improving user interface in OmniTool for better experience.
• [2025/2] We release OmniParser V2 checkpoints. Watch Video
• [2025/2] We introduce OmniTool: Control a Windows 11 VM with OmniParser + your vision model of choice. OmniTool supports out of the box the following large language models - OpenAI (4o/o1/o3-mini), DeepSeek (R1), Qwen (2.5VL) or Anthropic Computer Use. Watch Video
• [2025/1] V2 is coming. We achieve new state of the art results 39.5% on the new grounding benchmark Screen Spot Pro with OmniParser v2 (will be released soon)! Read more details here.
• [2024/11] We release an updated version, OmniParser V1.5 which features 1) more fine grained/small icon detection, 2) prediction of whether each screen element is interactable or not. Examples in the demo.ipynb.
• [2024/10] OmniParser was 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

First clone the repo, and then install environment:

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

Ensure you have the V2 weights downloaded in weights folder (ensure caption weights folder is called icon_caption_florence). If not download them with:

   # download the model checkpoints to local directory OmniParser/weights/
   for f in icon_detect/{train_args.yaml,model.pt,model.yaml} icon_caption/{config.json,generation_config.json,model.safetensors}; do huggingface-cli download microsoft/OmniParser-v2.0 "$f" --local-dir weights; done
   mv weights/icon_caption weights/icon_caption_florence

<!-- ## [deprecated] 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. For v1: convert the safetensor to .pt file. ```python python weights/convert_safetensor_to_pt.py For v1.5: download 'model_v1_5.pt' from https://huggingface.co/microsoft/OmniParser/tree/main/icon_detect_v1_5, make a new dir: weights/icon_detect_v1_5, and put it inside the folder. No weight conversion is needed. ``` -->

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}, 
}