README.md

---
license: apache-2.0
task_categories:
- visual-question-answering
language:
- en
tags:
- vision
- question-answering
- multimodal
size_categories:
- 1K<n<10K
---

# RealXBench

RealXBench is a comprehensive visual question answering benchmark dataset. The full dataset contains 300 high-quality image-question-answer triplets. Due to internal regulations, only a subset of 194 samples is released in this open-source version.

## Dataset Structure

Each example contains:
- **query**: The question about the image (in English)
- **answer**: The ground truth answer(s), with multiple answers separated by "or"
- **perception**: Difficulty level for perception task (1 if required, 0 otherwise)
- **search**: Difficulty level for search task (1 if required, 0 otherwise)
- **reason**: Difficulty level for reasoning task (1 if required, 0 otherwise)
- **image**: The corresponding image file

## Usage

```python
from datasets import load_dataset

dataset = load_dataset("glowol/RealXBench")
```

## Citation

If you use this dataset, please cite:

```bibtex
@article{deepEyesV2,
  title={DeepEyesV2: Toward Agentic Multimodal Model},
  author={Jack Hong and Chenxiao Zhao and ChengLin Zhu and Weiheng Lu and Guohai Xu and Xing Yu},
  journal={arXiv preprint arXiv:2511.05271},
  year={2025},
  url={https://arxiv.org/abs/2511.05271}
}
```