Update README.md

README.md

@@ -29,3 +29,162 @@ configs:
   - 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