---
license: apache-2.0
pretty_name: CountHalluSet — ToyShape
task_categories:
  - unconditional-image-generation
  - image-classification
size_categories:
  - 10K<n<100K
tags:
  - diffusion
  - counting
  - hallucination
  - synthetic
---

# CountHalluSet — ToyShape

Synthetic 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

128×128 RGB images of **non-overlapping white shapes on a black background**. Each
image holds 1–3 shapes drawn from `{triangle, square, pentagon}`, **at most one
instance per type**. This defines the counting criterion: a correct sample has
each present shape exactly once; two of any shape, or an empty image, is a
hallucination.

```
ToyShape/
├── images/       # 00000.png, 00001.png, ...
└── labels.csv    # filename, triangle, square, pentagon   (each count ∈ {0, 1})
```

Default release: 30,000 samples.

## Usage

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

Load with the reference code (`counthallu.datasets.ToyShape`) or regenerate from
scratch — the generator is deterministic given a seed:

```bash
python -m counthallu.datasets.toyshape --data_root $DATASET_ROOT --num_samples 30000
```

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}
}
```