---
dataset_info:
  features:
  - name: id
    dtype: string
  - name: question_id
    dtype: string
  - name: question
    dtype: string
  - name: answer
    dtype: string
  - name: image_source
    dtype: string
  - name: image
    dtype: image
  - name: category
    dtype: string
  - name: pope_old_answer
    dtype: string
  splits:
  - name: test
    num_bytes: 417678467
    num_examples: 8185
  download_size: 382941163
  dataset_size: 417678467
configs:
- config_name: default
  data_files:
  - split: test
    path: data/test-*
---

# RePOPE (HuggingFace Version)

This dataset is a **Hugging Face formatted version of the RePOPE benchmark**, uploaded to make it easier to use in evaluation pipelines and multimodal research.

RePOPE is a **corrected relabeling of the POPE benchmark**, which is commonly used to evaluate **object hallucination in Vision-Language Models (VLMs)**. The dataset fixes annotation errors in the original POPE benchmark and removes ambiguous examples.

Original paper:
**RePOPE: Impact of Annotation Errors on the POPE Benchmark**
Yannic Neuhaus, Matthias Hein (2025)

---

# Dataset Overview

RePOPE evaluates whether a model **incorrectly claims that an object exists in an image**.

Example question format:

```
Question: Is there a car in the image?
Answer: Yes / No
```

The model must answer correctly based on the image.

Incorrect answers may indicate **object hallucination**, where the model claims objects exist that are not present.

---

# Dataset Statistics

* Total examples: **8185**
🔄 Changed 494 answers.
❌ Removed 815 ambigious questions in POPE.
* Image source: **COCO 2014 validation set**
* Categories:

  * **random**
  * **popular**
  * **adversarial**

These splits follow the structure of the original **POPE benchmark**.

---

# Dataset Features

| Feature           | Type   | Description                                         |
| ----------------- | ------ | --------------------------------------------------- |
| `id`              | string | Unique identifier for the sample                    |
| `question_id`     | string | Identifier linking to the question instance         |
| `question`        | string | yes/no question about objects in the image          |
| `answer`          | string | Correct label after RePOPE relabeling               |
| `pope_old_answer` | string | Original answer from the POPE benchmark             |
| `image_source`    | string | COCO image identifier                               |
| `image`           | image  | The corresponding COCO image                        |
| `category`        | string | Split category (`random`, `popular`, `adversarial`) |

---

# Example

```python
from datasets import load_dataset

dataset = load_dataset("SushantGautam/RePOPE")

print(dataset["test"][0])
```

Example output:

```
{
  'id': '...',
  'question_id': '...',
  'question': 'Is there a car in the image?',
  'answer': 'no',
  'pope_old_answer': 'yes',
  'image_source': 'COCO_val2014_000000310196',
  'category': 'adversarial',
  'image': <PIL.Image>
}
```

---

# Why RePOPE?

The original POPE dataset contains **annotation errors and ambiguous samples**.
These issues can significantly impact evaluation metrics such as **F1 score**.

Key findings from the RePOPE paper:

* Incorrect **"yes" labels**: **9.3%**
* Incorrect **"no" labels**: **1.7%**

This imbalance can distort hallucination evaluation results.

RePOPE fixes these issues by:

* correcting incorrect labels
* removing ambiguous samples
* preserving compatibility with POPE evaluation pipelines

---

# Intended Use

This dataset is intended for:

* Evaluating **vision-language models**
* Studying **object hallucination**
* Benchmarking multimodal systems
* Comparing hallucination mitigation techniques

Example models evaluated with POPE/RePOPE:

* LLaVA
* Qwen-VL
* BLIP
* GPT-4V

---

# Dataset Source

Original annotations from the official RePOPE repository:

[https://github.com/YanNeu/RePOPE](https://github.com/YanNeu/RePOPE)

Images come from:

**MS COCO 2014 validation set**

---

# Citation

If you use this dataset, please cite the original paper:

```
@article{neuhaus2025repope,
  title={RePOPE: Impact of Annotation Errors on the POPE Benchmark},
  author={Neuhaus, Yannic and Hein, Matthias},
  journal={arXiv preprint arXiv:2504.15707},
  year={2025}
}
```

---

# Acknowledgements

Thanks to the authors of:

* **POPE** – Evaluating object hallucination in large vision-language models
* **RePOPE** – Correcting annotation errors in the POPE benchmark