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Vehicle Wheel Detection Dataset

67584
Tagself-driving
Taskobject detection
Release YearMade in 2023
LicenseCC0 1.0
Download2 GB

Introduction #

The dataset, known as Vehicle Wheel Detection | Axle Detection, comprises an exceptionally challenging collection featuring over 6,000 images dedicated to the detection of vehicle wheels. These images are sourced from a diverse range of more than 2,000 locations, with each image undergoing meticulous manual inspection and validation by computer vision experts at Datacluster Labs. This extensive dataset encompasses a broad spectrum of vehicles, including trucks, cars, bicycles, and more.

Parameter Details
Dataset size 6000+ images
Captured by Over 2000+ crowdsource contributors
Resolution HD and above
Location Captured with 2000+ locations
Diversity Various lighting conditions like day, night, varied distances, view points etc.
Device used Captured using mobile phones in 2020-2022
Usage Wheel Detection, Wheel Counting, Object detection
ExpandExpand
Dataset LinkHomepage

Summary #

Vehicle Wheel Detection | Axle Detection is a dataset for an object detection task. It is used in the automotive industry.

The dataset consists of 675 images with 2993 labeled objects belonging to 8 different classes including wheel, 2_wheeler, 3_wheeler, and other: 4_wheeler, 6_wheeler, 8_wheeler, auto, and 10_wheeler.

Images in the Vehicle Wheel Detection dataset have bounding box annotations. There are 6 (1% of the total) unlabeled images (i.e. without annotations). There are no pre-defined train/val/test splits in the dataset. The dataset was released in 2023 by the DataCluster Labs, India.

Dataset Poster

Explore #

Vehicle Wheel Detection dataset has 675 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 Vehicle Wheel DetectionSample image from Vehicle Wheel Detection
OpenSample annotation mask from Vehicle Wheel DetectionSample image from Vehicle Wheel Detection
OpenSample annotation mask from Vehicle Wheel DetectionSample image from Vehicle Wheel Detection
OpenSample annotation mask from Vehicle Wheel DetectionSample image from Vehicle Wheel Detection
OpenSample annotation mask from Vehicle Wheel DetectionSample image from Vehicle Wheel Detection
OpenSample annotation mask from Vehicle Wheel DetectionSample image from Vehicle Wheel Detection
OpenSample annotation mask from Vehicle Wheel DetectionSample image from Vehicle Wheel Detection
OpenSample annotation mask from Vehicle Wheel DetectionSample image from Vehicle Wheel Detection
OpenSample annotation mask from Vehicle Wheel DetectionSample image from Vehicle Wheel Detection
OpenSample annotation mask from Vehicle Wheel DetectionSample image from Vehicle Wheel Detection
OpenSample annotation mask from Vehicle Wheel DetectionSample image from Vehicle Wheel Detection
OpenSample annotation mask from Vehicle Wheel DetectionSample image from Vehicle Wheel Detection
OpenSample annotation mask from Vehicle Wheel DetectionSample image from Vehicle Wheel Detection
OpenSample annotation mask from Vehicle Wheel DetectionSample image from Vehicle Wheel Detection
OpenSample annotation mask from Vehicle Wheel DetectionSample image from Vehicle Wheel Detection
OpenSample annotation mask from Vehicle Wheel DetectionSample image from Vehicle Wheel Detection
OpenSample annotation mask from Vehicle Wheel DetectionSample image from Vehicle Wheel Detection
OpenSample annotation mask from Vehicle Wheel DetectionSample image from Vehicle Wheel Detection
OpenSample annotation mask from Vehicle Wheel DetectionSample image from Vehicle Wheel Detection
OpenSample annotation mask from Vehicle Wheel DetectionSample image from Vehicle Wheel Detection
OpenSample annotation mask from Vehicle Wheel DetectionSample image from Vehicle Wheel Detection
OpenSample annotation mask from Vehicle Wheel DetectionSample image from Vehicle Wheel Detection
OpenSample annotation mask from Vehicle Wheel DetectionSample image from Vehicle Wheel Detection
OpenSample annotation mask from Vehicle Wheel DetectionSample image from Vehicle Wheel Detection
OpenSample annotation mask from Vehicle Wheel DetectionSample image from Vehicle Wheel Detection
OpenSample annotation mask from Vehicle Wheel DetectionSample image from Vehicle Wheel Detection
OpenSample annotation mask from Vehicle Wheel DetectionSample image from Vehicle Wheel Detection
OpenSample annotation mask from Vehicle Wheel DetectionSample image from Vehicle Wheel Detection
OpenSample annotation mask from Vehicle Wheel DetectionSample image from Vehicle Wheel Detection
OpenSample annotation mask from Vehicle Wheel DetectionSample image from Vehicle Wheel Detection
πŸ‘€
Have a look at 675 images
View images along with annotations and tags, search and filter by various parameters

Class balance #

There are 8 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 1-8 of 8
Class
γ…€
Images
γ…€
Objects
γ…€
Count on image
average
Area on image
average
wheelβž”
rectangle
564
1692
3
3.12%
2_wheelerβž”
rectangle
425
629
1.48
28.74%
3_wheelerβž”
rectangle
345
432
1.25
14.88%
4_wheelerβž”
rectangle
115
183
1.59
6.36%
6_wheelerβž”
rectangle
34
44
1.29
35.94%
8_wheelerβž”
rectangle
8
9
1.12
11.68%
autoβž”
rectangle
3
3
1
40.12%
10_wheelerβž”
rectangle
1
1
1
14.62%

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 1-8 of 8
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
wheel
rectangle
1692
1.04%
32.57%
0%
8px
0.25%
2258px
59.43%
240px
6.73%
8px
0.26%
2291px
61.89%
2_wheeler
rectangle
629
19.65%
100%
0.02%
49px
1.5%
5760px
100%
1165px
32.74%
35px
1.38%
4320px
100%
3_wheeler
rectangle
432
11.99%
99.02%
0.06%
86px
2.4%
4000px
100%
919px
25.77%
79px
2.53%
3120px
100%
4_wheeler
rectangle
183
4.14%
47.2%
0.06%
55px
1.69%
3211px
80.28%
525px
14.59%
50px
2.57%
2047px
67.69%
6_wheeler
rectangle
44
27.84%
100%
0.25%
102px
3.98%
4160px
100%
1414px
37.84%
108px
4.41%
3008px
100%
8_wheeler
rectangle
9
10.39%
32.87%
0.2%
127px
3.89%
1300px
37.5%
693px
20.27%
128px
5.23%
2146px
87.66%
auto
rectangle
3
40.12%
75.9%
6.29%
417px
16.29%
3522px
76.43%
2031px
52.89%
741px
38.59%
3432px
99.31%
10_wheeler
rectangle
1
14.62%
14.62%
14.62%
957px
29.32%
957px
29.32%
957px
29.32%
1221px
49.88%
1221px
49.88%

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 2993 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 2993
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”
wheel
rectangle
20210430_07_54_03_000_3lfMUU1rI2fHQ2oTEhRa9UMEY8v2_T_2250_4000 (1).jpg
4000 x 2250
769px
19.23%
359px
15.96%
3.07%
2βž”
2_wheeler
rectangle
20210430_07_54_03_000_3lfMUU1rI2fHQ2oTEhRa9UMEY8v2_T_2250_4000 (1).jpg
4000 x 2250
2329px
58.23%
1385px
61.56%
35.84%
3βž”
wheel
rectangle
20210503_17_19_21_000_x1Al88rRcbgbIm5Y4D4pjFVpufq2_F_4000_3000.jpg
4000 x 3000
127px
3.17%
107px
3.57%
0.11%
4βž”
wheel
rectangle
20210503_17_19_21_000_x1Al88rRcbgbIm5Y4D4pjFVpufq2_F_4000_3000.jpg
4000 x 3000
461px
11.53%
322px
10.73%
1.24%
5βž”
2_wheeler
rectangle
20210503_17_19_21_000_x1Al88rRcbgbIm5Y4D4pjFVpufq2_F_4000_3000.jpg
4000 x 3000
1176px
29.4%
673px
22.43%
6.6%
6βž”
wheel
rectangle
20210503_17_19_21_000_x1Al88rRcbgbIm5Y4D4pjFVpufq2_F_4000_3000.jpg
4000 x 3000
121px
3.02%
105px
3.5%
0.11%
7βž”
wheel
rectangle
20210503_17_19_21_000_x1Al88rRcbgbIm5Y4D4pjFVpufq2_F_4000_3000.jpg
4000 x 3000
68px
1.7%
97px
3.23%
0.05%
8βž”
4_wheeler
rectangle
20210503_17_19_21_000_x1Al88rRcbgbIm5Y4D4pjFVpufq2_F_4000_3000.jpg
4000 x 3000
2668px
66.7%
672px
22.4%
14.94%
9βž”
3_wheeler
rectangle
20210503_17_19_21_000_x1Al88rRcbgbIm5Y4D4pjFVpufq2_F_4000_3000.jpg
4000 x 3000
624px
15.6%
824px
27.47%
4.28%
10βž”
4_wheeler
rectangle
20210503_17_19_21_000_x1Al88rRcbgbIm5Y4D4pjFVpufq2_F_4000_3000.jpg
4000 x 3000
441px
11.03%
746px
24.87%
2.74%

License #

Vehicle Wheel Detection | Axle Detection is under CC0 1.0 license.

Source

Citation #

If you make use of the Vehicle Wheel Detection data, please cite the following reference:

@dataset{Vehicle Wheel Detection,
	author={DataCluster Labs},
	title={Vehicle Wheel Detection | Axle Detection},
	year={2023},
	url={https://www.kaggle.com/datasets/dataclusterlabs/vehicle-wheel-detection}
}

Source

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

@misc{ visualization-tools-for-vehicle-wheel-detection-dataset,
  title = { Visualization Tools for Vehicle Wheel Detection Dataset },
  type = { Computer Vision Tools },
  author = { Dataset Ninja },
  howpublished = { \url{ https://datasetninja.com/vehicle-wheel-detection } },
  url = { https://datasetninja.com/vehicle-wheel-detection },
  journal = { Dataset Ninja },
  publisher = { Dataset Ninja },
  year = { 2024 },
  month = { jun },
  note = { visited on 2024-06-21 },
}

Download #

Dataset Vehicle Wheel Detection 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='Vehicle Wheel Detection', 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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