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Vehicle Dataset for YOLO Dataset

300061534
Tagsurveillance
Taskobject detection
Release YearMade in 2022
LicenseDbCL v1.0
Download393 MB

Introduction #

Nadin Pethiyagoda

Authors introduce the Vehicle Dataset for YOLO, a meticulously curated collection of labeled images that assembles a diverse range of vehicle types, rendering it a valuable resource for computer vision and object detection enthusiasts. This dataset consists of a total of 3000 images, with 2100 designated for train and 900 for valid. It has been constructed by amalgamating data from various sources, including Kaggle, the Stanford Car Dataset, and web scraping, ensuring a rich and varied set of examples. This dataset encompasses six distinct classes: car, threewheel, bus, truck, motorbike, and van, presenting numerous opportunities for the development and refinement of YOLO-based models tailored for vehicle detection tasks.

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Dataset LinkHomepage

Summary #

Vehicle Dataset for YOLO is a dataset for an object detection task. Possible applications of the dataset could be in the smart city industry.

The dataset consists of 3000 images with 3830 labeled objects belonging to 6 different classes including car, motorbike, threewheel, and other: van, bus, and truck.

Images in the Vehicle Dataset for YOLO dataset have bounding box annotations. There is 1 unlabeled image (i.e. without annotations). There are 2 splits in the dataset: train (2100 images) and valid (900 images). The dataset was released in 2022.

Here are the visualized examples for the classes:

Explore #

Vehicle Dataset for YOLO dataset has 3000 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 Dataset for YOLOSample image from Vehicle Dataset for YOLO
OpenSample annotation mask from Vehicle Dataset for YOLOSample image from Vehicle Dataset for YOLO
OpenSample annotation mask from Vehicle Dataset for YOLOSample image from Vehicle Dataset for YOLO
OpenSample annotation mask from Vehicle Dataset for YOLOSample image from Vehicle Dataset for YOLO
OpenSample annotation mask from Vehicle Dataset for YOLOSample image from Vehicle Dataset for YOLO
OpenSample annotation mask from Vehicle Dataset for YOLOSample image from Vehicle Dataset for YOLO
OpenSample annotation mask from Vehicle Dataset for YOLOSample image from Vehicle Dataset for YOLO
OpenSample annotation mask from Vehicle Dataset for YOLOSample image from Vehicle Dataset for YOLO
OpenSample annotation mask from Vehicle Dataset for YOLOSample image from Vehicle Dataset for YOLO
OpenSample annotation mask from Vehicle Dataset for YOLOSample image from Vehicle Dataset for YOLO
OpenSample annotation mask from Vehicle Dataset for YOLOSample image from Vehicle Dataset for YOLO
OpenSample annotation mask from Vehicle Dataset for YOLOSample image from Vehicle Dataset for YOLO
OpenSample annotation mask from Vehicle Dataset for YOLOSample image from Vehicle Dataset for YOLO
OpenSample annotation mask from Vehicle Dataset for YOLOSample image from Vehicle Dataset for YOLO
OpenSample annotation mask from Vehicle Dataset for YOLOSample image from Vehicle Dataset for YOLO
OpenSample annotation mask from Vehicle Dataset for YOLOSample image from Vehicle Dataset for YOLO
OpenSample annotation mask from Vehicle Dataset for YOLOSample image from Vehicle Dataset for YOLO
OpenSample annotation mask from Vehicle Dataset for YOLOSample image from Vehicle Dataset for YOLO
OpenSample annotation mask from Vehicle Dataset for YOLOSample image from Vehicle Dataset for YOLO
OpenSample annotation mask from Vehicle Dataset for YOLOSample image from Vehicle Dataset for YOLO
OpenSample annotation mask from Vehicle Dataset for YOLOSample image from Vehicle Dataset for YOLO
OpenSample annotation mask from Vehicle Dataset for YOLOSample image from Vehicle Dataset for YOLO
OpenSample annotation mask from Vehicle Dataset for YOLOSample image from Vehicle Dataset for YOLO
OpenSample annotation mask from Vehicle Dataset for YOLOSample image from Vehicle Dataset for YOLO
OpenSample annotation mask from Vehicle Dataset for YOLOSample image from Vehicle Dataset for YOLO
OpenSample annotation mask from Vehicle Dataset for YOLOSample image from Vehicle Dataset for YOLO
OpenSample annotation mask from Vehicle Dataset for YOLOSample image from Vehicle Dataset for YOLO
OpenSample annotation mask from Vehicle Dataset for YOLOSample image from Vehicle Dataset for YOLO
OpenSample annotation mask from Vehicle Dataset for YOLOSample image from Vehicle Dataset for YOLO
OpenSample annotation mask from Vehicle Dataset for YOLOSample image from Vehicle Dataset for YOLO
πŸ‘€
Have a look at 3000 images
View images along with annotations and tags, search and filter by various parameters

Class balance #

There are 6 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 1-6 of 6
Class
γ…€
Images
γ…€
Objects
γ…€
Count on image
average
Area on image
average
carβž”
rectangle
590
662
1.12
41.79%
motorbikeβž”
rectangle
537
697
1.3
47.49%
threewheelβž”
rectangle
518
706
1.36
43.46%
vanβž”
rectangle
515
548
1.06
60.84%
truckβž”
rectangle
504
629
1.25
57.02%
busβž”
rectangle
504
588
1.17
51.3%

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 1-6 of 6
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
threewheel
rectangle
706
32.47%
100%
0.16%
25px
4.86%
5376px
100%
299px
59.18%
20px
3.24%
7112px
100%
motorbike
rectangle
697
37.18%
99.6%
0.12%
15px
3.4%
1442px
100%
260px
58.77%
12px
3.08%
1300px
100%
car
rectangle
662
37.39%
92.37%
0.09%
11px
3.63%
3032px
100%
403px
51.32%
16px
2.4%
5996px
99.71%
truck
rectangle
629
45.94%
100%
0.23%
27px
6.75%
2048px
100%
405px
64.86%
8px
2.05%
2251px
100%
bus
rectangle
588
44.03%
99.69%
0.46%
20px
7.72%
339px
100%
150px
62.9%
13px
4.06%
499px
100%
van
rectangle
548
57.32%
98.1%
0.33%
20px
4.44%
1446px
99.8%
368px
71.58%
35px
5%
2589px
100%

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 3830 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 3830
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”
car
rectangle
car11.jpg
600 x 1200
369px
61.5%
1036px
86.33%
53.09%
2βž”
motorbike
rectangle
2009_002651.jpg
375 x 500
206px
54.93%
228px
45.6%
25.05%
3βž”
threewheel
rectangle
threewheel91.jpeg
987 x 740
808px
81.86%
595px
80.41%
65.82%
4βž”
van
rectangle
indianvan419.jpeg
296 x 565
252px
85.14%
473px
83.72%
71.27%
5βž”
threewheel
rectangle
45041.jpg
480 x 640
479px
99.79%
473px
73.91%
73.75%
6βž”
bus
rectangle
dikwella-sri-lanka-january-2-260nw-252877207.jpg
280 x 393
78px
27.86%
84px
21.37%
5.95%
7βž”
bus
rectangle
dikwella-sri-lanka-january-2-260nw-252877207.jpg
280 x 393
71px
25.36%
64px
16.28%
4.13%
8βž”
truck
rectangle
srilankalorry190.jpeg
477 x 637
436px
91.4%
437px
68.6%
62.71%
9βž”
bus
rectangle
1302906767_b2e92de824_w.jpg
219 x 400
197px
89.95%
383px
95.75%
86.13%
10βž”
van
rectangle
H6LJGJSPG51G.jpg
350 x 500
258px
73.71%
488px
97.6%
71.95%

License #

Vehicle Dataset for YOLO is under DbCL v1.0 license.

Source

Citation #

If you make use of the Vehicle Dataset for YOLO data, please cite the following reference:

@dataset{Vehicle Dataset for YOLO,
  author={Nadin Pethiyagoda},
  title={Vehicle Dataset for YOLO},
  year={2022},
  url={https://www.kaggle.com/datasets/nadinpethiyagoda/vehicle-dataset-for-yolo}
}

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-dataset-for-yolo-dataset,
  title = { Visualization Tools for Vehicle Dataset for YOLO Dataset },
  type = { Computer Vision Tools },
  author = { Dataset Ninja },
  howpublished = { \url{ https://datasetninja.com/vehicle-dataset-for-yolo } },
  url = { https://datasetninja.com/vehicle-dataset-for-yolo },
  journal = { Dataset Ninja },
  publisher = { Dataset Ninja },
  year = { 2024 },
  month = { feb },
  note = { visited on 2024-02-24 },
}

Download #

Dataset Vehicle Dataset for YOLO 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 Dataset for YOLO', 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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