UIX-Qwen2 / README.md
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---
license: odc-by
datasets:
- neulab/MultiUI
language:
- en
base_model:
- Qwen/Qwen2-7B-Instruct
tags:
- GUI
- Agent
- Web
- OCR
- Doc
- VQA
---
#### Model for the paper: [Harnessing Webpage Uis For Text Rich Visual Understanding](https://arxiv.org/abs/2410.13824)
🌐 [Homepage](https://neulab.github.io/MultiUI/) | 🐍 [GitHub](https://github.com/neulab/multiui) | 📖 [arXiv](https://arxiv.org/abs/2410.13824)
## Introduction
We introduce **MultiUI**, a dataset containing 7.3 million samples from 1 million websites, covering diverse multi- modal tasks and UI layouts. Models trained on **MultiUI** not only excel in web UI tasks—achieving up to a 48% improvement on VisualWebBench and a 19.1% boost in action accuracy on a web agent dataset Mind2Web—but also generalize surprisingly well to non-web UI tasks and even to non-UI domains, such as document understanding, OCR, and chart interpretation.
<video controls autoplay src="https://cdn-uploads.huggingface.co/production/uploads/65403d8781a8731a1c09a584/vk7yT4Y7ydBOHM6BojmlI.mp4"></video>
## Training & Evaluation
The model training is based on the **[LLaVA-NeXT](https://github.com/LLaVA-VL/LLaVA-NeXT)**.
For deployment, refer to **SGLang deployment** section in LLaVA-NeXT repo.
For benchmark evaluation, the awesome **lmms-eval** package is used. Check our repo **[MultiUI](https://github.com/neulab/multiui)** to evaluate on benchmarks mentioned in the paper.
## Model Performance
![image/png](https://cdn-uploads.huggingface.co/production/uploads/65403d8781a8731a1c09a584/h1L7J4rLlq6EOtbiXZjZW.png)
![image/png](https://cdn-uploads.huggingface.co/production/uploads/65403d8781a8731a1c09a584/NOVQ8WjgJoRm0bzN9zxFx.png)
![image/png](https://cdn-uploads.huggingface.co/production/uploads/65403d8781a8731a1c09a584/O6GhR1UXOSi7o3yjXvK4e.png)
## Contact
* Junpeng Liu: [email protected]
* Xiang Yue: [email protected]
## Citation
If you find this work helpful, please cite out paper:
````
@misc{liu2024harnessingwebpageuistextrich,
title={Harnessing Webpage UIs for Text-Rich Visual Understanding},
author={Junpeng Liu and Tianyue Ou and Yifan Song and Yuxiao Qu and Wai Lam and Chenyan Xiong and Wenhu Chen and Graham Neubig and Xiang Yue},
year={2024},
eprint={2410.13824},
archivePrefix={arXiv},
primaryClass={cs.CV},
url={https://arxiv.org/abs/2410.13824},
}
````