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ShyFoo/CountHallu-Dataset-SimObject

CountHalluSet — SimObject Rendered dataset from Counting Hallucinations in Diffusion Models (arXiv:2510.13080). Part of CountHalluSet, a suite with well-defined counting criteria used to measure counting hallucination — a diffusion model generating the wrong number of instances, even for patterns absent from its training data. What's inside 256×256 RGB rendered images of everyday objects, each labelled with the per-class instance count over three object classes.… See the full description on the dataset page: https://huggingface.co/datasets/ShyFoo/CountHallu-Dataset-SimObject.

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Dataset Card

CountHalluSet — SimObject

Rendered dataset from [Counting Hallucinations in Diffusion Models](https://arxiv.org/abs/2510.13080) (arXiv:2510.13080). Part of CountHalluSet, a suite with well-defined counting criteria used to measure counting hallucination — a diffusion model generating the wrong number of instances, even for patterns absent from its training data.

What's inside

256×256 RGB rendered images of everyday objects, each labelled with the per-class instance count over three object classes. As with ToyShape, a correct sample contains at most one instance per class; extra or missing instances are counting hallucinations.

SimObject/
├── images/       # 00000.png, 00001.png, ...
└── labels.csv    # filename, <class_1>, <class_2>, <class_3>

<!-- TODO: fill in the three object class names (the labels.csv column headers) and one line on how the images were rendered (renderer / asset source). -->

Usage

bash
huggingface-cli download ShyFoo/CountHallu-dataset-SimObject \
    --repo-type dataset --local-dir $DATASET_ROOT/SimObject

Load with the reference code (counthallu.datasets.SimObject). See the CountHallu repository for training and the full evaluation protocol.

Citation

bibtex
@article{fu2025counting,
  title={Counting Hallucinations in Diffusion Models},
  author={Fu, Shuai and Zhou, Jian and Chen, Qi and Jing, Huang and Nguyen, Huy Anh and Liu, Xiaohan and Zeng, Zhixiong and Ma, Lin and Zhang, Quanshi and Wu, Qi},
  journal={arXiv preprint arXiv:2510.13080},
  year={2025}
}