README.md

metadata

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:

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

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.