README.md

---
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}
}
```