Datasets:
Tasks:
Visual Question Answering
Formats:
imagefolder
Sub-tasks:
multiple-choice-qa
Languages:
English
Size:
1K - 10K
License:
File size: 1,828 Bytes
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pretty_name: WTB-Bench
language:
- en
license: apache-2.0
task_categories:
- visual-question-answering
task_ids:
- multiple-choice-qa
tags:
- benchmark
- visual-question-answering
- wind-turbine
- inspection
- defect-detection
- computer-vision
size_categories:
- 1K<n<10K
---
# WTB-Bench
WTB-Bench is a visual question answering benchmark for wind turbine inspection. Each example pairs a turbine image with a multiple-choice question, answer key, options, and task type.
## Dataset Structure
This repository uses the Hugging Face ImageFolder layout:
```text
test/
metadata.jsonl
images/
<sha256>.jpg
```
`metadata.jsonl` contains one JSON object per question. The `file_name` field points to the relative image path.
## Fields
- `image`: The wind turbine inspection image loaded by Hugging Face Datasets from `file_name`.
- `question`: Multiple-choice question.
- `answer`: Correct option key, such as `A`, `B`, `C`, or `D`.
- `options`: Mapping from option key to option text.
- `task_type`: Question category, such as `existence`, `counting`, `classification`, `localization`, or `analysis`.
## Splits
- `test`: 1,200 QA entries over 288 unique images.
## Usage
```python
from datasets import load_dataset
dataset = load_dataset("withstaticTai/WTB-Bench")
example = dataset["test"][0]
```
## Intended Use
WTB-Bench is intended for evaluating visual question answering systems on wind turbine inspection scenarios, including defect existence, counting, classification, localization, and inspection implication analysis.
## Limitations
The dataset is provided as a benchmark test split. It should not be treated as a complete training corpus for wind turbine defect recognition. Model outputs should be interpreted with domain expertise before any operational inspection or maintenance decision.
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