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RotBench

Data for RotBench: Evaluating Multimodal Large Language Models on Identifying Image Rotation.

arXiv | GitHub

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 dataset and passed a two-stage human verification process to ensure rotations are distinguishable.

Dataset Download

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:

@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}, 
}
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