Dataset Ninja LogoDataset Ninja:

Road Vehicle Dataset

3004212838
Tagenergy-and-utilities, self-driving
Taskobject detection
Release YearMade in 2021
LicenseDbCL v1.0
Download139 MB

Introduction #

Ashfak Yeafi

The Road Vehicle dataset contains Bangladesh road valencias images with annotation. There are two separate splits in this dataset, one contains train images and the other contains valid images. The author hopes it will be a great asset for autonomous vehicles and traffic management projects. The dataset is properly made for YOLO v5 real-time vehicle detection project.

Dataset LinkHomepage

Summary #

Road Vehicle Images Dataset is a dataset for an object detection task. Possible applications of the dataset could be in the utilities and automotive industries.

The dataset consists of 3004 images with 24348 labeled objects belonging to 21 different classes including car, bus, motorbike, and other: three wheelers -CNG-, rickshaw, truck, pickup, minivan, suv, van, bicycle, auto rickshaw, human hauler, wheelbarrow, ambulance, minibus, taxi, army vehicle, scooter, policecar, and garbagevan.

Images in the Road Vehicle dataset have bounding box annotations. There are 2 (0% of the total) unlabeled images (i.e. without annotations). There are 2 splits in the dataset: train (2704 images) and valid (300 images). The dataset was released in 2021.

Dataset Poster

Explore #

Road Vehicle dataset has 3004 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 Road VehicleSample image from Road Vehicle
OpenSample annotation mask from Road VehicleSample image from Road Vehicle
OpenSample annotation mask from Road VehicleSample image from Road Vehicle
OpenSample annotation mask from Road VehicleSample image from Road Vehicle
OpenSample annotation mask from Road VehicleSample image from Road Vehicle
OpenSample annotation mask from Road VehicleSample image from Road Vehicle
OpenSample annotation mask from Road VehicleSample image from Road Vehicle
OpenSample annotation mask from Road VehicleSample image from Road Vehicle
OpenSample annotation mask from Road VehicleSample image from Road Vehicle
OpenSample annotation mask from Road VehicleSample image from Road Vehicle
OpenSample annotation mask from Road VehicleSample image from Road Vehicle
OpenSample annotation mask from Road VehicleSample image from Road Vehicle
OpenSample annotation mask from Road VehicleSample image from Road Vehicle
OpenSample annotation mask from Road VehicleSample image from Road Vehicle
OpenSample annotation mask from Road VehicleSample image from Road Vehicle
OpenSample annotation mask from Road VehicleSample image from Road Vehicle
OpenSample annotation mask from Road VehicleSample image from Road Vehicle
OpenSample annotation mask from Road VehicleSample image from Road Vehicle
OpenSample annotation mask from Road VehicleSample image from Road Vehicle
OpenSample annotation mask from Road VehicleSample image from Road Vehicle
OpenSample annotation mask from Road VehicleSample image from Road Vehicle
OpenSample annotation mask from Road VehicleSample image from Road Vehicle
OpenSample annotation mask from Road VehicleSample image from Road Vehicle
OpenSample annotation mask from Road VehicleSample image from Road Vehicle
OpenSample annotation mask from Road VehicleSample image from Road Vehicle
OpenSample annotation mask from Road VehicleSample image from Road Vehicle
OpenSample annotation mask from Road VehicleSample image from Road Vehicle
OpenSample annotation mask from Road VehicleSample image from Road Vehicle
OpenSample annotation mask from Road VehicleSample image from Road Vehicle
OpenSample annotation mask from Road VehicleSample image from Road Vehicle
πŸ‘€
Have a look at 3004 images
View images along with annotations and tags, search and filter by various parameters

Class balance #

There are 21 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 1-10 of 21
Class
γ…€
Images
γ…€
Objects
γ…€
Count on image
average
Area on image
average
carβž”
rectangle
1621
5474
3.38
5.5%
busβž”
rectangle
1558
3339
2.14
10.37%
motorbikeβž”
rectangle
1186
2284
1.93
4.25%
three wheelers -CNG-βž”
rectangle
1169
2986
2.55
3.8%
rickshawβž”
rectangle
1020
3539
3.47
8.33%
truckβž”
rectangle
843
1495
1.77
10.5%
pickupβž”
rectangle
793
1225
1.54
5.69%
minivanβž”
rectangle
575
932
1.62
3.34%
suvβž”
rectangle
538
858
1.59
3.08%
vanβž”
rectangle
448
755
1.69
3.26%

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 1-21 of 21
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
car
rectangle
5474
1.72%
53.59%
0%
3px
0.71%
327px
75.56%
40px
10.2%
3px
0.47%
619px
96.72%
rickshaw
rectangle
3539
2.58%
77.87%
0%
1px
0.16%
425px
98.89%
62px
15.91%
1px
0.09%
634px
99.06%
bus
rectangle
3339
4.9%
95.1%
0%
1px
0.16%
564px
100%
76px
20.14%
1px
0.09%
640px
100%
three wheelers -CNG-
rectangle
2986
1.58%
44.36%
0%
1px
0.16%
325px
76.67%
45px
11.55%
1px
0.09%
489px
76.41%
motorbike
rectangle
2284
2.23%
84.75%
0%
3px
0.62%
433px
89.28%
45px
11.43%
2px
0.31%
640px
100%
truck
rectangle
1495
6%
98.59%
0.01%
4px
0.96%
533px
100%
84px
22.54%
5px
0.78%
638px
99.69%
pickup
rectangle
1225
3.72%
83.99%
0.01%
5px
1.09%
546px
99.53%
66px
17.78%
4px
0.62%
613px
95.78%
minivan
rectangle
932
2.07%
47.62%
0%
1px
0.16%
298px
82.22%
46px
11.84%
1px
0.09%
456px
71.25%
suv
rectangle
858
1.95%
75.61%
0%
1px
0.16%
357px
99.17%
46px
11.18%
1px
0.09%
528px
82.5%
van
rectangle
755
1.96%
54.35%
0.01%
4px
0.94%
290px
80.56%
45px
11.59%
4px
0.62%
507px
79.22%
bicycle
rectangle
459
1.03%
31.27%
0%
4px
0.94%
414px
64.72%
43px
10.41%
2px
0.31%
307px
48.33%
auto rickshaw
rectangle
372
1.33%
28.7%
0.01%
4px
1.32%
352px
85.83%
45px
11.13%
3px
0.47%
270px
42.78%
human hauler
rectangle
169
1.49%
34.24%
0.01%
6px
1.09%
322px
89.44%
43px
10.93%
6px
0.94%
320px
50%
wheelbarrow
rectangle
120
3.89%
51.92%
0%
1px
0.16%
318px
88.33%
63px
17.06%
1px
0.09%
471px
73.59%
minibus
rectangle
95
2.94%
29.02%
0.02%
5px
1.25%
274px
76.11%
57px
13.67%
7px
1.09%
282px
45%
ambulance
rectangle
70
2.04%
35.66%
0.04%
12px
2.65%
265px
73.61%
46px
11.99%
10px
1.56%
310px
48.44%
taxi
rectangle
60
0.85%
4.16%
0.02%
6px
1.4%
86px
20.19%
34px
8.61%
10px
1.56%
132px
32.78%
army vehicle
rectangle
43
2.21%
24.69%
0.06%
14px
2.91%
252px
52.5%
55px
13.35%
14px
2.19%
301px
47.78%
scooter
rectangle
38
1.33%
11.85%
0.01%
8px
1.41%
226px
62.78%
47px
11.68%
4px
0.62%
146px
30.42%
policecar
rectangle
32
1%
6.1%
0.06%
8px
2.22%
111px
31.01%
39px
10.11%
11px
1.72%
126px
19.69%
garbagevan
rectangle
3
1.24%
1.78%
0.52%
52px
14.44%
79px
21.94%
64px
17.68%
23px
3.59%
54px
8.44%

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 24348 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 24348
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”
motorbike
rectangle
Pias--171-_jpg.rf.b7121ed4eb1ae48b1ba8675c0378cd97.jpg
359 x 640
97px
27.02%
38px
5.94%
1.6%
2βž”
car
rectangle
Pias--171-_jpg.rf.b7121ed4eb1ae48b1ba8675c0378cd97.jpg
359 x 640
36px
10.03%
88px
13.75%
1.38%
3βž”
car
rectangle
Pias--171-_jpg.rf.b7121ed4eb1ae48b1ba8675c0378cd97.jpg
359 x 640
36px
10.03%
109px
17.03%
1.71%
4βž”
car
rectangle
Pias--171-_jpg.rf.b7121ed4eb1ae48b1ba8675c0378cd97.jpg
359 x 640
30px
8.36%
30px
4.69%
0.39%
5βž”
car
rectangle
Pias--171-_jpg.rf.b7121ed4eb1ae48b1ba8675c0378cd97.jpg
359 x 640
76px
21.17%
110px
17.19%
3.64%
6βž”
car
rectangle
Pias--171-_jpg.rf.b7121ed4eb1ae48b1ba8675c0378cd97.jpg
359 x 640
110px
30.64%
232px
36.25%
11.11%
7βž”
car
rectangle
Pias--171-_jpg.rf.b7121ed4eb1ae48b1ba8675c0378cd97.jpg
359 x 640
106px
29.53%
140px
21.88%
6.46%
8βž”
car
rectangle
Pias--171-_jpg.rf.b7121ed4eb1ae48b1ba8675c0378cd97.jpg
359 x 640
27px
7.52%
62px
9.69%
0.73%
9βž”
car
rectangle
Pias--171-_jpg.rf.b7121ed4eb1ae48b1ba8675c0378cd97.jpg
359 x 640
30px
8.36%
33px
5.16%
0.43%
10βž”
car
rectangle
Pias--171-_jpg.rf.b7121ed4eb1ae48b1ba8675c0378cd97.jpg
359 x 640
42px
11.7%
43px
6.72%
0.79%

License #

Road Vehicle Images Dataset is under DbCL v1.0 license.

Source

Citation #

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

@dataset{Road Vehicle,
	author={Ashfak Yeafi},
	title={Road Vehicle Images Dataset},
	year={2021},
	url={https://www.kaggle.com/datasets/ashfakyeafi/road-vehicle-images-dataset}
}

Source

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

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

Download #

Dataset Road Vehicle 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='Road Vehicle', 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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