---
license: apache-2.0
dataset_info:
  features:
  - name: image
    dtype: image
  - name: image_name
    dtype: string
  splits:
  - name: small
    num_bytes: 694935
    num_examples: 50
  - name: large
    num_bytes: 4651568.0
    num_examples: 300
  download_size: 10233742
  dataset_size: 5346503.0
configs:
- config_name: default
  data_files:
  - split: large
    path: data/large-*
  - split: small
    path: data/small-*
task_categories:
- image-text-to-text
---

# RotBench

Data for _RotBench: Evaluating Multimodal Large Language Models on Identifying Image Rotation_.

[**arXiv**](https://arxiv.org/abs/2508.13968) | [**GitHub**](https://github.com/tianyiniu/RotBench)

### Dataset Summary

**RotBench** is a benchmark for evaluating whether multimodal large language models (MLLMs) can identify image orientation. It contains 350 manually filtered images. The dataset includes two subsets:  

- **Large**: 300 images  
- **Small**: 50 images 

All images were drawn from the [Spatial-MM](https://github.com/FatemehShiri/Spatial-MM) dataset and passed a two-stage human verification process to ensure rotations are distinguishable.  

### Dataset Download

```python
from datasets import load_dataset

dataset = load_dataset("tianyin/RotBench")
data = dataset['large'] # or dataset['small']

for i, sample in enumerate(data):
    image = sample['image']  # PIL Image object
    image_name = sample['image_name'] 
```

# Citation
If you find our data useful in your research, please cite the following paper:
```bibtex
@misc{niu2025rotbenchevaluatingmultimodallarge,
      title={RotBench: Evaluating Multimodal Large Language Models on Identifying Image Rotation}, 
      author={Tianyi Niu and Jaemin Cho and Elias Stengel-Eskin and Mohit Bansal},
      year={2025},
      eprint={2508.13968},
      archivePrefix={arXiv},
      primaryClass={cs.CV},
      url={https://arxiv.org/abs/2508.13968}, 
}
```