Datasets:

withstaticTai
/

WTB-Bench

question stringlengths 20 122	answer stringclasses 4 values	options dict	task_type stringclasses 5 values
Is there any defect in this wind turbine?	B	{ "A": "Yes.", "B": "No.", "C": null, "D": null }	existence
How many defects are on the wind turbine structure?	D	{ "A": "1.", "B": "2.", "C": "3.", "D": "0." }	counting
What type of defect is on the wind turbine structure?	B	{ "A": "oil.", "B": "no defect.", "C": "lightning_strike.", "D": "peeling." }	classification
What is the inspection implication here?	B	{ "A": "Immediate repair is required.", "B": "No visible defect-related risk is indicated.", "C": "Lightning damage must be mapped.", "D": "Oil leakage needs confirmation." }	analysis
Is there any defect in this wind turbine?	B	{ "A": "Yes.", "B": "No.", "C": null, "D": null }	existence
How many defects are on the wind turbine structure?	D	{ "A": "1.", "B": "2.", "C": "3.", "D": "0." }	counting
What type of defect is on the wind turbine structure?	B	{ "A": "oil.", "B": "no defect.", "C": "lightning_strike.", "D": "peeling." }	classification
What is the inspection implication here?	A	{ "A": "No visible defect-related risk.", "B": "Immediate structural repair is needed.", "C": "Lightning damage is confirmed.", "D": "Blade erosion is severe." }	analysis
Is there any defect in this wind turbine?	B	{ "A": "Yes.", "B": "No.", "C": null, "D": null }	existence
How many defects are on the wind turbine structure?	D	{ "A": "1.", "B": "2.", "C": "4.", "D": "0." }	counting
Why is the visible surface cue benign?	A	{ "A": "It appears to be normal shading.", "B": "It is clear lightning damage.", "C": "It shows severe erosion.", "D": "It indicates delamination." }	analysis
Is there any defect in this wind turbine?	A	{ "A": "Yes.", "B": "No.", "C": null, "D": null }	existence
How many defects are on the wind turbine structure?	A	{ "A": "1.", "B": "2.", "C": "3.", "D": "0." }	counting
What type of defect is on the wind turbine structure?	C	{ "A": "oil.", "B": "painting.", "C": "lightning_strike.", "D": "peeling." }	classification
What most likely caused this mark?	D	{ "A": "Routine surface shading.", "B": "Harmless blade seam.", "C": "Paint overspray residue.", "D": "Lightning strike impact." }	analysis
Is there any defect in this wind turbine?	A	{ "A": "Yes.", "B": "No.", "C": null, "D": null }	existence
What type of defect is on the wind turbine structure?	D	{ "A": "oil.", "B": "painting.", "C": "lightning_strike.", "D": "peeling." }	classification
What most likely caused this visible cue?	C	{ "A": "Normal shadow from the blade.", "B": "Freshly applied protective paint.", "C": "Coating loss from weathering or erosion.", "D": "Oil leaking from the hub." }	analysis
Is there any defect in this wind turbine?	B	{ "A": "Yes.", "B": "No.", "C": null, "D": null }	existence
How many defects are on the wind turbine structure?	D	{ "A": "1.", "B": "2.", "C": "4.", "D": "0." }	counting
What type of defect is on the wind turbine structure?	B	{ "A": "oil.", "B": "no defect.", "C": "lightning_strike.", "D": "peeling." }	classification
Why is the dark tone not a defect cue?	B	{ "A": "It indicates oil leakage.", "B": "It is normal lighting shadow.", "C": "It shows peeling paint.", "D": "It marks impact damage." }	analysis
Is there any defect in this wind turbine?	B	{ "A": "Yes.", "B": "No.", "C": null, "D": null }	existence
How many defects are on the wind turbine structure?	D	{ "A": "1.", "B": "2.", "C": "4.", "D": "0." }	counting
What type of defect is on the wind turbine structure?	B	{ "A": "oil.", "B": "no defect.", "C": "lightning_strike.", "D": "peeling." }	classification
What is the likely inspection implication?	D	{ "A": "Immediate structural repair is needed.", "B": "Lightning damage is spreading.", "C": "Oil leakage must be traced.", "D": "No visible defect-related risk." }	analysis
Is there any defect in this wind turbine?	B	{ "A": "Yes.", "B": "No.", "C": null, "D": null }	existence
How many defects are on the wind turbine structure?	D	{ "A": "1.", "B": "2-3.", "C": "4-5.", "D": "0." }	counting
What type of defect is on the wind turbine structure?	C	{ "A": "oil.", "B": "lightning_strike.", "C": "no defect.", "D": "peeling." }	classification
What is the inspection implication here?	C	{ "A": "Immediate structural repair is needed.", "B": "Lightning damage is likely spreading.", "C": "No visible defect-related risk is indicated.", "D": "Oil leakage must be traced." }	analysis
Is there any defect in this wind turbine?	B	{ "A": "Yes.", "B": "No.", "C": null, "D": null }	existence
How many defects are on the wind turbine structure?	D	{ "A": "1.", "B": "2-3.", "C": "4-5.", "D": "0." }	counting
What type of defect is on the wind turbine structure?	C	{ "A": "oil.", "B": "lightning_strike.", "C": "no defect.", "D": "peeling." }	classification
What does the blade surface appear to show?	A	{ "A": "No visible defect marks.", "B": "A large peeling patch.", "C": "A lightning burn scar.", "D": "Oil leaking down the blade." }	analysis
Is there any defect in this wind turbine?	B	{ "A": "Yes.", "B": "No.", "C": null, "D": null }	existence
How many defects are on the wind turbine structure?	D	{ "A": "1.", "B": "2-3.", "C": "4-5.", "D": "0." }	counting
What type of defect is on the wind turbine structure?	C	{ "A": "oil.", "B": "lightning_strike.", "C": "no defect.", "D": "peeling." }	classification
Why should the visible cue not be treated as damage?	B	{ "A": "It shows impact cracking.", "B": "It appears to be normal shading.", "C": "It is exposed fiberglass.", "D": "It indicates surface peeling." }	analysis
Is there any defect in this wind turbine?	B	{ "A": "Yes.", "B": "No.", "C": null, "D": null }	existence
How many defects are on the wind turbine structure?	D	{ "A": "1.", "B": "2-3.", "C": "4-5.", "D": "0." }	counting
What type of defect is on the wind turbine structure?	C	{ "A": "oil.", "B": "lightning_strike.", "C": "no defect.", "D": "peeling." }	classification
What is the inspection implication here?	D	{ "A": "Immediate structural repair is needed.", "B": "Blade shutdown is required.", "C": "Lightning damage must be confirmed.", "D": "No visible defect-related risk." }	analysis
Is there any defect in this wind turbine?	B	{ "A": "Yes.", "B": "No.", "C": null, "D": null }	existence
How many defects are on the wind turbine structure?	D	{ "A": "1.", "B": "2-3.", "C": "4-5.", "D": "0." }	counting
What type of defect is on the wind turbine structure?	C	{ "A": "oil.", "B": "lightning_strike.", "C": "no defect.", "D": "peeling." }	classification
Why should the visible shading be considered benign?	D	{ "A": "It shows coating peeling.", "B": "It indicates oil leakage.", "C": "It marks impact damage.", "D": "It matches normal lighting." }	analysis
Is there any defect in this wind turbine?	B	{ "A": "Yes.", "B": "No.", "C": null, "D": null }	existence
How many defects are on the wind turbine structure?	D	{ "A": "1.", "B": "2-3.", "C": "4-5.", "D": "0." }	counting
What type of defect is on the wind turbine structure?	C	{ "A": "oil.", "B": "crack.", "C": "no defect.", "D": "peeling." }	classification
What is the inspection implication here?	B	{ "A": "Immediate structural repair is needed.", "B": "No visible defect-related risk.", "C": "Oil leakage must be confirmed.", "D": "Crack growth should be measured." }	analysis
Is there any defect in this wind turbine?	B	{ "A": "Yes.", "B": "No.", "C": null, "D": null }	existence
How many defects are on the wind turbine structure?	D	{ "A": "1.", "B": "2-3.", "C": "4-5.", "D": "0." }	counting
What type of defect is on the wind turbine structure?	C	{ "A": "oil.", "B": "crack.", "C": "no defect.", "D": "peeling." }	classification
Why should the visible cues be considered benign?	B	{ "A": "They show active cracking.", "B": "They appear as normal surface shading.", "C": "They indicate oil contamination.", "D": "They reveal peeled material." }	analysis
Is there any defect in this wind turbine?	B	{ "A": "Yes.", "B": "No.", "C": null, "D": null }	existence
How many defects are on the wind turbine structure?	D	{ "A": "1.", "B": "2-3.", "C": "4-5.", "D": "0." }	counting
What type of defect is on the wind turbine structure?	C	{ "A": "oil.", "B": "crack.", "C": "no defect.", "D": "peeling." }	classification
What is the inspection implication here?	D	{ "A": "Immediate structural repair is needed.", "B": "Oil leakage is spreading.", "C": "Crack growth should be measured.", "D": "No visible defect-related risk." }	analysis
Is there any defect in this wind turbine?	B	{ "A": "Yes.", "B": "No.", "C": null, "D": null }	existence
How many defects are on the wind turbine structure?	D	{ "A": "1.", "B": "2-3.", "C": "4-5.", "D": "0." }	counting
What type of defect is on the wind turbine structure?	C	{ "A": "oil.", "B": "crack.", "C": "no defect.", "D": "peeling." }	classification
Why should the dark areas not be treated as defects?	D	{ "A": "They show open cracking.", "B": "They indicate coating loss.", "C": "They are active oil stains.", "D": "They appear to be normal shadows." }	analysis
Is there any defect in this wind turbine?	A	{ "A": "Yes.", "B": "No.", "C": null, "D": null }	existence
How many defects are on the wind turbine structure?	A	{ "A": "1.", "B": "2-3.", "C": "4-5.", "D": "0." }	counting
Where is the defect located in the image?	C	{ "A": "Bottom.", "B": "Top.", "C": "Center.", "D": null }	localization
What is the most likely cause of the visible cue?	C	{ "A": "Normal shadow from the turbine body.", "B": "Paint reflection from the sky.", "C": "Corrosion staining around a fitting or fastener.", "D": "A missing blade tip." }	analysis
Is there any defect in this wind turbine?	A	{ "A": "Yes.", "B": "No.", "C": null, "D": null }	existence
What type of defect is on the wind turbine structure?	D	{ "A": "hole.", "B": "crack.", "C": "no defect.", "D": "peeling." }	classification
What most likely caused this cue?	B	{ "A": "Normal blade shadowing.", "B": "Coating adhesion failure.", "C": "A factory seam line.", "D": "Grass reflected on the blade." }	analysis
Is there any defect in this wind turbine?	A	{ "A": "Yes.", "B": "No.", "C": null, "D": null }	existence
What type of defect is on the wind turbine structure?	D	{ "A": "hole.", "B": "crack.", "C": "no defect.", "D": "erosion." }	classification
What is a likely inspection concern?	D	{ "A": "It improves airflow efficiency.", "B": "It confirms no maintenance is needed.", "C": "It only changes the logo color.", "D": "It may expose material to weathering." }	analysis
Is there any defect in this wind turbine?	B	{ "A": "Yes.", "B": "No.", "C": null, "D": null }	existence
How many defects are on the wind turbine structure?	D	{ "A": "1.", "B": "2-3.", "C": "4-5.", "D": "0." }	counting
What type of defect is on the wind turbine structure?	C	{ "A": "oil.", "B": "crack.", "C": "no defect.", "D": "peeling." }	classification
Why should the visible shading be treated as benign?	D	{ "A": "It indicates a deep crack.", "B": "It shows coating peeling.", "C": "It confirms oil contamination.", "D": "It appears to be normal lighting." }	analysis
Is there any defect in this wind turbine?	B	{ "A": "Yes.", "B": "No.", "C": null, "D": null }	existence
How many defects are on the wind turbine structure?	D	{ "A": "1.", "B": "2-3.", "C": "4-5.", "D": "0." }	counting
What type of defect is on the wind turbine structure?	C	{ "A": "oil.", "B": "crack.", "C": "no defect.", "D": "peeling." }	classification
Why should the visible shading not be treated as damage?	C	{ "A": "It is exposed internal laminate.", "B": "It is a leaking fluid trail.", "C": "It appears to be normal lighting.", "D": "It shows paint peeling." }	analysis
Is there any defect in this wind turbine?	B	{ "A": "Yes.", "B": "No.", "C": null, "D": null }	existence
How many defects are on the wind turbine structure?	D	{ "A": "1.", "B": "2-3.", "C": "4-5.", "D": "0." }	counting
What type of defect is on the wind turbine structure?	C	{ "A": "oil.", "B": "crack.", "C": "no defect.", "D": "peeling." }	classification
What is the inspection implication here?	A	{ "A": "No visible defect-related risk.", "B": "Immediate structural repair needed.", "C": "Oil leakage is likely spreading.", "D": "Crack growth requires shutdown." }	analysis
Is there any defect in this wind turbine?	A	{ "A": "Yes.", "B": "No.", "C": null, "D": null }	existence
What type of defect is on the wind turbine structure?	C	{ "A": "hole.", "B": "crack.", "C": "ice.", "D": "peeling." }	classification
Where exactly is this defect located on the wind turbine structure?	C	{ "A": "Near the leading edge in the tip region", "B": "Near the trailing edge in the mid-span region", "C": "Close to the blade root attachment point", "D": "Centered at the midpoint of the blade span" }	localization
What is a likely risk of this defect?	D	{ "A": "It improves aerodynamic efficiency.", "B": "It only changes the blade color.", "C": "It confirms the blade is undamaged.", "D": "It can disrupt airflow and add load." }	analysis
Can you identify any defects on this blade surface?	A	{ "A": "Yes.", "B": "No.", "C": null, "D": null }	existence
What is the type of the defect?	A	{ "A": "oil.", "B": "paint.", "C": "chip.", "D": "scratch." }	classification
Where is the main defect distributed in the given image?	A	{ "A": "center left.", "B": "bottom right.", "C": "top right.", "D": "center right." }	localization
What most likely caused this cue?	D	{ "A": "Blade tip impact damage.", "B": "Fresh white paint overspray.", "C": "Normal shadow from lighting only.", "D": "Oil or fluid residue on the surface." }	analysis
Is there any defect in this wind turbine?	A	{ "A": "Yes.", "B": "No.", "C": null, "D": null }	existence
How many defects are there?	A	{ "A": "1.", "B": "2-3.", "C": "4-5.", "D": "More than 5." }	counting
What is the type of the defect?	C	{ "A": "lightning_strike.", "B": "oil.", "C": "crack.", "D": "deformity." }	classification
Where is the defect located in the image?	C	{ "A": "Bottom.", "B": "Top.", "C": "Center.", "D": null }	localization
Why treat this mark as a defect?	C	{ "A": "It is uniform shading.", "B": "It follows a normal seam.", "C": "It is an irregular surface break.", "D": "It is only background dirt." }	analysis
Is there any defect in this wind turbine?	A	{ "A": "Yes.", "B": "No.", "C": null, "D": null }	existence
How many defects are there?	A	{ "A": "1.", "B": "2-3.", "C": "4-5.", "D": "More than 5." }	counting
What is the type of the defect?	D	{ "A": "painting.", "B": "crack.", "C": "lightning_strike.", "D": "erosion." }	classification

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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.

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