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Wind Turbines (by Kyle Graupe) Dataset

302021965
Tagenergy-and-utilities, drones
Taskobject detection
Release YearMade in 2023
LicenseCC0 1.0
Download658 MB

Introduction #

Released 2023-04-22 Β·Kyle Graupe

The author of the Object Detection Dataset - Wind Turbines have curated a collection of images featuring wind turbines amidst dynamic and varying backgrounds. The dataset was specifically designed to cater to drone photographers. Considering that several commercial drones, including those from DJI, come equipped with Software Development Kits (SDKs) that support programming in languages like Python, these high-quality camera-equipped drones can be effectively paired with their respective SDKs to undertake remarkable computer vision projects.

Features:

  • 50% probability of horizontal flip
  • Random Gaussian blur between 0 and 3 pixels
  • Random exposure adjustment of between -25 and +25 percent
  • Pre-Split: 87% Train, 9% Validation, 4% Test
  • New: Bounding Box: Noise: Up to 5% of pixels
ExpandExpand
Dataset LinkHomepage

Summary #

Object Detection Dataset - Wind Turbines is a dataset for an object detection task. Possible applications of the dataset could be in the energy industry.

The dataset consists of 3020 images with 18289 labeled objects belonging to 2 different classes including turbine and cable tower.

Images in the Wind Turbines (by Kyle Graupe) dataset have bounding box annotations. There are 18 (1% of the total) unlabeled images (i.e. without annotations). There are 3 splits in the dataset: train (2643 images), valid (247 images), and test (130 images). The dataset was released in 2023.

Dataset Poster

Explore #

Wind Turbines (by Kyle Graupe) dataset has 3020 images. Click on one of the examples below or open "Explore" tool anytime you need to view dataset images with annotations. This tool has extended visualization capabilities like zoom, translation, objects table, custom filters and more. Hover the mouse over the images to hide or show annotations.

OpenSample annotation mask from Wind Turbines (by Kyle Graupe)Sample image from Wind Turbines (by Kyle Graupe)
OpenSample annotation mask from Wind Turbines (by Kyle Graupe)Sample image from Wind Turbines (by Kyle Graupe)
OpenSample annotation mask from Wind Turbines (by Kyle Graupe)Sample image from Wind Turbines (by Kyle Graupe)
OpenSample annotation mask from Wind Turbines (by Kyle Graupe)Sample image from Wind Turbines (by Kyle Graupe)
OpenSample annotation mask from Wind Turbines (by Kyle Graupe)Sample image from Wind Turbines (by Kyle Graupe)
OpenSample annotation mask from Wind Turbines (by Kyle Graupe)Sample image from Wind Turbines (by Kyle Graupe)
OpenSample annotation mask from Wind Turbines (by Kyle Graupe)Sample image from Wind Turbines (by Kyle Graupe)
OpenSample annotation mask from Wind Turbines (by Kyle Graupe)Sample image from Wind Turbines (by Kyle Graupe)
OpenSample annotation mask from Wind Turbines (by Kyle Graupe)Sample image from Wind Turbines (by Kyle Graupe)
OpenSample annotation mask from Wind Turbines (by Kyle Graupe)Sample image from Wind Turbines (by Kyle Graupe)
OpenSample annotation mask from Wind Turbines (by Kyle Graupe)Sample image from Wind Turbines (by Kyle Graupe)
OpenSample annotation mask from Wind Turbines (by Kyle Graupe)Sample image from Wind Turbines (by Kyle Graupe)
OpenSample annotation mask from Wind Turbines (by Kyle Graupe)Sample image from Wind Turbines (by Kyle Graupe)
OpenSample annotation mask from Wind Turbines (by Kyle Graupe)Sample image from Wind Turbines (by Kyle Graupe)
OpenSample annotation mask from Wind Turbines (by Kyle Graupe)Sample image from Wind Turbines (by Kyle Graupe)
OpenSample annotation mask from Wind Turbines (by Kyle Graupe)Sample image from Wind Turbines (by Kyle Graupe)
OpenSample annotation mask from Wind Turbines (by Kyle Graupe)Sample image from Wind Turbines (by Kyle Graupe)
OpenSample annotation mask from Wind Turbines (by Kyle Graupe)Sample image from Wind Turbines (by Kyle Graupe)
OpenSample annotation mask from Wind Turbines (by Kyle Graupe)Sample image from Wind Turbines (by Kyle Graupe)
OpenSample annotation mask from Wind Turbines (by Kyle Graupe)Sample image from Wind Turbines (by Kyle Graupe)
OpenSample annotation mask from Wind Turbines (by Kyle Graupe)Sample image from Wind Turbines (by Kyle Graupe)
OpenSample annotation mask from Wind Turbines (by Kyle Graupe)Sample image from Wind Turbines (by Kyle Graupe)
OpenSample annotation mask from Wind Turbines (by Kyle Graupe)Sample image from Wind Turbines (by Kyle Graupe)
OpenSample annotation mask from Wind Turbines (by Kyle Graupe)Sample image from Wind Turbines (by Kyle Graupe)
OpenSample annotation mask from Wind Turbines (by Kyle Graupe)Sample image from Wind Turbines (by Kyle Graupe)
OpenSample annotation mask from Wind Turbines (by Kyle Graupe)Sample image from Wind Turbines (by Kyle Graupe)
OpenSample annotation mask from Wind Turbines (by Kyle Graupe)Sample image from Wind Turbines (by Kyle Graupe)
OpenSample annotation mask from Wind Turbines (by Kyle Graupe)Sample image from Wind Turbines (by Kyle Graupe)
OpenSample annotation mask from Wind Turbines (by Kyle Graupe)Sample image from Wind Turbines (by Kyle Graupe)
OpenSample annotation mask from Wind Turbines (by Kyle Graupe)Sample image from Wind Turbines (by Kyle Graupe)
πŸ‘€
Have a look at 3020 images
View images along with annotations and tags, search and filter by various parameters

Class balance #

There are 2 annotation classes in the dataset. Find the general statistics and balances for every class in the table below. Click any row to preview images that have labels of the selected class. Sort by column to find the most rare or prevalent classes.

Search
Rows 1-2 of 2
Class
γ…€
Images
γ…€
Objects
γ…€
Count on image
average
Area on image
average
turbineβž”
rectangle
2869
17798
6.2
24.16%
cable towerβž”
rectangle
133
491
3.69
27.28%

Co-occurrence matrix #

Co-occurrence matrix is an extremely valuable tool that shows you the images for every pair of classes: how many images have objects of both classes at the same time. If you click any cell, you will see those images. We added the tooltip with an explanation for every cell for your convenience, just hover the mouse over a cell to preview the description.

Images #

Explore every single image in the dataset with respect to the number of annotations of each class it has. Click a row to preview selected image. Sort by any column to find anomalies and edge cases. Use horizontal scroll if the table has many columns for a large number of classes in the dataset.

Object distribution #

Interactive heatmap chart for every class with object distribution shows how many images are in the dataset with a certain number of objects of a specific class. Users can click cell and see the list of all corresponding images.

Class sizes #

The table below gives various size properties of objects for every class. Click a row to see the image with annotations of the selected class. Sort columns to find classes with the smallest or largest objects or understand the size differences between classes.

Search
Rows 1-2 of 2
Class
Object count
Avg area
Max area
Min area
Min height
Min height
Max height
Max height
Avg height
Avg height
Min width
Min width
Max width
Max width
turbine
rectangle
17798
4.21%
91.43%
0%
1px
0.09%
1687px
100%
247px
23.29%
9px
0.47%
1917px
99.84%
cable tower
rectangle
491
7.55%
51.18%
0.18%
77px
6.64%
1891px
99.67%
509px
38.26%
35px
1.82%
1079px
52.69%

Spatial Heatmap #

The heatmaps below give the spatial distributions of all objects for every class. These visualizations provide insights into the most probable and rare object locations on the image. It helps analyze objects' placements in a dataset.

Spatial Heatmap

Objects #

Table contains all 18289 objects. Click a row to preview an image with annotations, and use search or pagination to navigate. Sort columns to find outliers in the dataset.

Search
Rows 1-10 of 18289
Object ID
γ…€
Class
γ…€
Image name
click row to open
Image size
height x width
Height
γ…€
Height
γ…€
Width
γ…€
Width
γ…€
Area
γ…€
1βž”
turbine
rectangle
windmill51_jpg.rf.5d46fabcc53fa6386958d337da633219.jpg
1080 x 1920
989px
91.57%
1917px
99.84%
91.43%
2βž”
turbine
rectangle
windmill52_jpg.rf.f4cd67da99ff46b09260304387574d33.jpg
1080 x 1920
835px
77.31%
309px
16.09%
12.44%
3βž”
turbine
rectangle
windmill52_jpg.rf.f4cd67da99ff46b09260304387574d33.jpg
1080 x 1920
340px
31.48%
139px
7.24%
2.28%
4βž”
turbine
rectangle
windmill32_jpg.rf.d4b9bcbcb058a757ecdea2dabaeca2c1.jpg
1080 x 1920
1077px
99.72%
361px
18.8%
18.75%
5βž”
turbine
rectangle
windmill32_jpg.rf.d4b9bcbcb058a757ecdea2dabaeca2c1.jpg
1080 x 1920
123px
11.39%
105px
5.47%
0.62%
6βž”
turbine
rectangle
windmill32_jpg.rf.d4b9bcbcb058a757ecdea2dabaeca2c1.jpg
1080 x 1920
73px
6.76%
82px
4.27%
0.29%
7βž”
turbine
rectangle
windmill32_jpg.rf.d4b9bcbcb058a757ecdea2dabaeca2c1.jpg
1080 x 1920
33px
3.06%
53px
2.76%
0.08%
8βž”
turbine
rectangle
windmill92_jpg.rf.90c6b6bba4aaa64f209ece0777829bb7.jpg
1080 x 1920
341px
31.57%
220px
11.46%
3.62%
9βž”
turbine
rectangle
windmill80_jpg.rf.e2c24b84f656663359065183a302b505.jpg
1080 x 1920
1px
0.09%
9px
0.47%
0%
10βž”
turbine
rectangle
windmill80_jpg.rf.e2c24b84f656663359065183a302b505.jpg
1080 x 1920
774px
71.67%
523px
27.24%
19.52%

License #

Object Detection Dataset - Wind Turbines is under CC0 1.0 license.

Source

Citation #

If you make use of the Wind Turbines (by Kyle Graupe) data, please cite the following reference:

@dataset{Wind Turbines (by Kyle Graupe),
	author={Kyle Graupe},
	title={Object Detection Dataset - Wind Turbines},
	year={2023},
	url={https://www.kaggle.com/datasets/kylegraupe/wind-turbine-image-dataset-for-computer-vision}
}

Source

If you are happy with Dataset Ninja and use provided visualizations and tools in your work, please cite us:

@misc{ visualization-tools-for-wind-turbines-dataset,
  title = { Visualization Tools for Wind Turbines (by Kyle Graupe) Dataset },
  type = { Computer Vision Tools },
  author = { Dataset Ninja },
  howpublished = { \url{ https://datasetninja.com/wind-turbines } },
  url = { https://datasetninja.com/wind-turbines },
  journal = { Dataset Ninja },
  publisher = { Dataset Ninja },
  year = { 2024 },
  month = { mar },
  note = { visited on 2024-03-05 },
}

Download #

Dataset Wind Turbines (by Kyle Graupe) can be downloaded in Supervisely format:

As an alternative, it can be downloaded with dataset-tools package:

pip install --upgrade dataset-tools

… using following python code:

import dataset_tools as dtools

dtools.download(dataset='Wind Turbines (by Kyle Graupe)', dst_dir='~/dataset-ninja/')

Make sure not to overlook the python code example available on the Supervisely Developer Portal. It will give you a clear idea of how to effortlessly work with the downloaded dataset.

The data in original format can be downloaded here.

. . .

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