Datasets:
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license: apache-2.0
pretty_name: CountHalluSet — SimObject
task_categories:
- unconditional-image-generation
- image-classification
size_categories:
- 10K<n<100K
tags:
- diffusion
- counting
- hallucination
- rendered
---
# 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](<https://github.com/ShyFoo/CountHallu-Diff>) 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}
}
```
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