Add comprehensive dataset card for ZoomEye evaluation data

README.md

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+---
+task_categories:
+- image-text-to-text
+license: cc-by-nc-4.0
+tags:
+- multimodal
+- llm
+- vision-language
+- visual-reasoning
+- tree-search
+---
+
+# ZoomEye: Enhancing Multimodal LLMs with Human-Like Zooming Capabilities through Tree-Based Image Exploration
+
+This repository contains the evaluation data for **ZoomEye**, a method presented in the paper [ZoomEye: Enhancing Multimodal LLMs with Human-Like Zooming Capabilities through Tree-Based Image Exploration](https://huggingface.co/papers/2411.16044).
+
+ZoomEye proposes a training-free, model-agnostic tree search algorithm tailored for vision-level reasoning. It addresses the limitations of existing Multimodal Large Language Models (MLLMs) that operate on fixed visual inputs, especially when dealing with images containing numerous fine-grained elements. By treating an image as a hierarchical tree structure, ZoomEye enables MLLMs to simulate human-like zooming behavior, navigating from root to leaf nodes to gather detailed visual cues necessary for accurate decision-making.
+
+This dataset supports the evaluation of MLLMs on a series of high-resolution benchmarks, demonstrating consistent performance improvements for various models.
+
+*   **Paper:** [https://huggingface.co/papers/2411.16044](https://huggingface.co/papers/2411.16044)
+*   **Project Page:** [https://szhanz.github.io/zoomeye/](https://szhanz.github.io/zoomeye/)
+*   **Code:** [https://github.com/om-ai-lab/ZoomEye](https://github.com/om-ai-lab/ZoomEye)
+
+## Evaluation Data Preparation
+
+The core evaluation data (including V* Bench and HR-Bench) used in the ZoomEye paper has been packaged together.
+
+1.  **Download Data**: The evaluation data is provided [here](https://huggingface.co/datasets/omlab/zoom_eye_data). After downloading, please unzip it. The path to the unzipped data is referred to as **`<anno path>`**.
+
+2.  **[Optional] MME-RealWorld Benchmark**: If you wish to evaluate ZoomEye on the MME-RealWorld Benchmark, follow these steps:
+    *   Follow the instructions in [this repository](https://github.com/yfzhang114/MME-RealWorld) to download the images.
+    *   Extract the images to the `<anno path>/mme-realworld` directory.
+    *   Place the `annotation_mme-realworld.json` file from [this link](https://huggingface.co/datasets/omlab/zoom_eye_data) into `<anno path>/mme-realworld`.
+
+### Folder Tree
+The expected folder structure after preparation is as follows:
+
+```
+zoom_eye_data 
+  ├── hr-bench_4k                                  
+  │   └── annotation_hr-bench_4k.json
+  │   └── images/
+  │     └── some.jpg
+  │    ...
+  ├── hr-bench_8k
+  │   └── annotation_hr-bench_8k.json
+  │   └── images/
+  │     └── some.jpg
+  │    ...
+  ├── vstar
+  │   └── annotation_vstar.json
+  │   └── direct_attributes/
+  │     └── some.jpg
+  │    ...
+  │   └── relative_positions/
+  │     └── some.jpg
+  │    ...
+  ├── mme-realworld
+  │   └── annotation_mme-realworld.json
+  │   └── AutonomousDriving/
+  │   └── MME-HD-CN/
+  │   └── monitoring_images/
+  │   └── ocr_cc/
+  │   └── remote_sensing/
+ ...
+```
+
+## Sample Usage
+
+### 1. Run the Python Demo
+
+We provide a demo file of Zoom Eye accepting any input Image-Question pair. The zoomed views of Zoom Eye will be saved into the demo folder.
+
+```bash
+python ZoomEye/demo.py \
+    --model-path lmms-lab/llava-onevision-qwen2-7b-ov \
+    --input_image demo/demo.jpg \
+    --question "What is the color of the soda can?"
+```
+
+### 2. Run the Gradio Demo
+
+We also provide a Gradio Demo. Run the script and open `http://127.0.0.1:7860/` in your browser.
+
+```bash
+python gdemo_gradio.py
+```
+
+## Citation
+
+If you find this repository helpful to your research, please cite our paper:
+
+```bibtex
+@article{shen2024zoomeye,
+  title={ZoomEye: Enhancing Multimodal LLMs with Human-Like Zooming Capabilities through Tree-Based Image Exploration},
+  author={Shen, Haozhan and Zhao, Kangjia and Zhao, Tiancheng and Xu, Ruochen and Zhang, Zilun and Zhu, Mingwei and Yin, Jianwei},
+  journal={arXiv preprint arXiv:2411.16044},
+  year={2024}
+}
+```