README.md

---
license: other
task_categories:
  - visual-question-answering
  - image-to-text
language:
  - en
tags:
  - guinness-world-records
  - size-estimation
  - measurement
  - vqa
  - outdoor
  - abnormal-size
pretty_name: "Guinness World Records – Abnormal Size VQA"
size_categories:
  - n<1K
---

# Guinness World Records – Abnormal Size VQA

A curated visual question-answering dataset featuring objects of extraordinary or record-breaking dimensions, sourced from Guinness World Records. Each image is paired with one or more measurement questions (height, length, width, diameter) and ground-truth numeric answers.

---

## Dataset Preview

Below is a sample of entries from the dataset. Images are stored under `images/` and referenced by filename.

| # | Image | Object | Question | Answer |
|---|-------|--------|----------|--------|
| 1 | <img src="images/1.jpg" alt="rifle" style="width: 25vw; min-width: 200px; height: 150px; object-fit: cover;"> | rifle | What is the length of this rifle (in meters)? | 10.18 m |
| 2 | <img src="images/2.jpg" alt="telephone" style="width: 25vw; min-width: 200px; height: 150px; object-fit: cover;"> | telephone | How tall is this telephone (in meters)? | 2.47 m |
| 4 | <img src="images/3.jpg" alt="monster truck" style="width: 25vw; min-width: 200px; height: 150px; object-fit: cover;"> | monster truck | What is the length of this monster truck (in meters)? | 9.75 m |
| 5 | <img src="images/4.jpg" alt="hiking boot" style="width: 25vw; min-width: 200px; height: 150px; object-fit: cover;"> | hiking boot | How tall is this hiking boot (in meters)? | 4.20 m |
| 11 | <img src="images/8.jpg" alt="Burj Khalifa" style="width: 25vw; min-width: 200px; height: 150px; object-fit: cover;"> | man-made structure | How tall is this man-made structure (in meters)? | 828 m |
| 15 | <img src="images/11.jpg" alt="bicycle" style="width: 25vw; min-width: 200px; height: 150px; object-fit: cover;"> | bicycle | How long is this bicycle (in meters)? | 55.16 m |
| 18 | <img src="images/13.jpg" alt="Ferris Wheel" style="width: 25vw; min-width: 200px; height: 150px; object-fit: cover;"> | Axleless Ferris Wheel | How tall is this Axleless Ferris Wheel (in meters)? | 142.52 m |
| 38 | <img src="images/26.jpg" alt="motorcycle" style="width: 25vw; min-width: 200px; height: 150px; object-fit: cover;"> | motorcycle | How long is this motorcycle (in meters)? | 26.29 m |

---

## Dataset Summary

This dataset is designed to benchmark the **physical size and dimension estimation** capabilities of vision-language models (VLMs). It focuses on objects that hold or are associated with Guinness World Records for their abnormal dimensions — covering outdoor scenes, indoor scenes, drone aerial views, and tabletop/close-up shots.

| Split | Images | QA Pairs |
|-------|--------|----------|
| all   | 33     | 50       |

### Scene Label Distribution

| Scene Label | # QA Pairs |
|-------------|-----------|
| outdoor     | 35        |
| indoor      | 9         |
| drone       | 4         |
| tabletop    | 2         |

### Measurement Types

All questions ask for a single numeric measurement in **meters** or **centimeters**:
- Height (`How tall is …`)
- Length (`How long is …` / `What is the length of …`)
- Width (`How wide is …` / `What is the width of …`)
- Diameter (`What is the diameter of …`)

---

## File Structure

```
.
├── images/
│   ├── 1.jpg          # world's longest rifle
│   ├── 2.jpg          # world's tallest telephone
│   ├── 3.jpg          # world's longest monster truck
│   └── ...            # 30 more images (1–33)
├── annotation.csv     # flat CSV with Num., Image_Name, Question, Answer, Object, Scene
└── annotation.jsonl   # structured JSONL with full metadata per QA pair
```

### `annotation.csv` columns

| Column | Description |
|--------|-------------|
| `Num.` | QA pair index (1–50) |
| `Image_Name` | Filename of the corresponding image |
| `Question` | Natural-language measurement question |
| `Answer` | Ground-truth numeric answer |
| `Object` | The object being measured |
| `indoor/outdoor/tiny/tabletop/drone` | Scene type label |

### `annotation.jsonl` fields

| Field | Description |
|-------|-------------|
| `Question Number` | QA pair index |
| `Question` | Natural-language measurement question |
| `Answer` | Ground-truth numeric value |
| `Scene Source` | Internal collection identifier |
| `Frames Path` | Image filename (without extension) |
| `Input Type` | `Image+text` for all entries |
| `Input Point / Mask / BBox` | Optional spatial prompt (null for this subset) |
| `Question Type` | `regression` (numeric answer) |
| `TaskType` | `HEIGHT` for all entries |
| `Object` | The record-holding object |
| `Scene Label` | `outdoor` / `indoor` / `drone` / `tabletop` |

---

## Usage

```python
from datasets import load_dataset

ds = load_dataset("SpaceVista/Guinness_World_Records")
```

Or load annotations manually:

```python
import json
from pathlib import Path
from PIL import Image

data = [json.loads(l) for l in open("annotation.jsonl")]

for entry in data:
    img_path = Path("images") / f"{entry['Frames Path']}.jpg"
    image = Image.open(img_path)
    print(entry["Question"], "→", entry["Answer"])
```

---

## Data Provenance and Licensing

> ⚠️ **Important Notice Regarding Data Licensing**

This dataset contains images collected from multiple sources:

- **A subset of the images were obtained directly through the official Guinness World Records website ([guinnessworldrecords.com](https://www.guinnessworldrecords.com)).** These images are identified by their association with official record entries and were retrieved via publicly accessible web pages.

- **The remaining images were gathered from various third-party web sources** (news articles, social media, photo repositories, etc.) during data collection. **We suspect that these images may carry potential licensing restrictions** or unresolved copyright claims. We were unable to verify the original license terms for each of these images at collection time.

**We strongly advise users of this dataset to:**
1. Treat all images as potentially copyrighted unless verified otherwise.
2. Use this dataset **for non-commercial research and evaluation purposes only**.
3. Not redistribute individual images outside the context of this dataset.
4. Independently verify licensing before any commercial or production use.

The annotations (questions and numeric answers) were produced by the dataset authors and are released for research use.

---

## Citation

If you use this dataset in your research, please cite it as:

```bibtex
@article{sun2025spacevista,
  title={SpaceVista: All-Scale Visual Spatial Reasoning from mm to km}, 
  author={Sun, Peiwen and Lang, Shiqiang and Wu, Dongming and Ding, Yi and Feng, Kaituo and Liu, Huadai and Ye, Zhen and Liu, Rui and Liu, Yun-Hui and Wang, Jianan and Yue, Xiangyu},
  journal={arXiv preprint arXiv:2510.09606},
  year={2025}
}
```