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---

# lmms-lab_POPE-problematic


🔥🔥 Update: Seems like some other researchers have already figured out this: [https://arxiv.org/abs/2504.15707v1](https://arxiv.org/abs/2504.15707v1).
They also have published corrected annotations at: [https://github.com/YanNeu/RePOPE/tree/main/annotations](https://github.com/YanNeu/RePOPE/tree/main/annotations).
See https://huggingface.co/datasets/SushantGautam/RePOPE for the corrected version


This dataset contains **potentially incorrect annotations from the POPE dataset** that were automatically flagged during experimentation with vision-language models.

The goal of this dataset is **community review and verification**. The examples included here are **suspected to have incorrect answers**, but they are **not guaranteed to be wrong**. Contributors and researchers are encouraged to inspect them and determine whether the original annotation is indeed incorrect.


---

## Motivation

While working with the original dataset:

https://huggingface.co/datasets/lmms-lab/POPE

I noticed that some examples appear to have **incorrect ground-truth answers** when visually inspected.

To investigate this further, I ran a filtering pipeline to detect **potential annotation mismatches** between the image and the labeled answer. The filtered samples are collected in this dataset for **manual inspection and discussion**.

The intention is **not to replace the original dataset**, but to highlight examples that may require **re-evaluation or correction**.

---

## How the samples were filtered

The suspected problematic samples were identified using:

**Model:** Qwen2-VL  

**Method:**  
The model was prompted with the image and the POPE question. If the model's prediction strongly disagreed with the labeled answer, the sample was flagged as **potentially problematic**.

Important notes:

- This filtering was performed as part of a **separate experiment** on POPE.
- **Qwen2-VL is not treated as a ground truth verifier.**
- Some flagged samples may still be **correctly labeled**.

Future verification with **stronger models or human review** may help determine the true correctness.

---

## Intended Use

This dataset is meant for:

- Manual dataset auditing
- Community review
- Benchmark quality analysis
- Studying annotation errors in vision-language datasets

Possible workflows include:

- Human verification of each flagged sample
- Cross-model agreement analysis
- Dataset cleaning experiments
- Robustness evaluation of VLM hallucination benchmarks

---

## Dataset Structure

The dataset contains a subset of samples from the original POPE dataset that were flagged as suspicious.

Each example includes fields similar to the original dataset:

- `id`
- `question_id`
- `question`
- `answer`
- `image_source`
- `image`
- `category`

These correspond directly to entries in the original dataset.

---

## Important Disclaimer

⚠️ **These samples are only suspected to be problematic.**

They were filtered automatically and may include:

- genuine annotation errors
- model mistakes
- ambiguous images
- borderline cases

Human verification is required before any conclusions are drawn.