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README.md

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+---
+license: mit
+task_categories:
+  - visual-question-answering
+language:
+  - en
+tags:
+  - hallucination-detection
+  - object-hallucination
+  - pope
+  - coco
+  - benchmark
+size_categories:
+  - 1K<n<10K
+---
+
+# RePOPE: Revisiting Partial Object Hallucination Evaluation
+
+RePOPE is a re-annotated version of the POPE (Polling-based Object Probing Evaluation) benchmark with corrected ground-truth labels. It evaluates object hallucination in multimodal large language models (MLLMs) by asking yes/no questions about object existence in MSCOCO images.
+
+## Dataset Details
+
+- **Original Paper:** [RePOPE: Revisiting Partial Object Hallucination Evaluation](https://arxiv.org/abs/2405.14571)
+- **Original Repository:** [https://github.com/YanNeu/RePOPE](https://github.com/YanNeu/RePOPE)
+- **Images:** MSCOCO 2014 (subset of 500 images)
+
+## Dataset Structure
+
+Each row contains:
+
+- `image`: The MSCOCO image (struct with `bytes` and `path`)
+- `image_id`: COCO image identifier (e.g., `000000310196`)
+- `question`: A yes/no question about object presence (e.g., "Is there a snowboard in the image?")
+- `answer`: Ground truth label (`yes` or `no`)
+- `category`: Sampling strategy used to select the queried object (`random`, `popular`, or `adversarial`)
+
+### Splits
+
+This dataset contains all three POPE sampling categories in a single split:
+
+| Category      | Count |
+|---------------|-------|
+| random        | 2,774 |
+| popular       | 2,727 |
+| adversarial   | 2,684 |
+| **Total**     | **8,185** |
+
+### Label Distribution
+
+| Answer | Count |
+|--------|-------|
+| yes    | 3,539 |
+| no     | 4,646 |
+
+## How to Use
+
+```python
+from datasets import load_dataset
+
+ds = load_dataset("MM-Hallu/RePOPE")
+```
+
+## Citation
+
+```bibtex
+@misc{neuhaus2024repope,
+      title={RePOPE: Revisiting Partial Object Hallucination Evaluation},
+      author={Yannik Neuschwander and Selen Yu and Jordy Van Landeghem and Jan Van Loock and Lilian Ngweta and Rukiye Savran Kizildag and Desmond Elliott and Matthew B. Blaschko},
+      year={2024},
+      eprint={2405.14571},
+      archivePrefix={arXiv},
+      primaryClass={cs.CV}
+}
+```