Road Vehicle - Dataset Ninja

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.

Homepage

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.

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.

Sample annotation mask from Road Vehicle

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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.

Rows 1-10 of 21

Class ㅤ	Images ㅤ	Objects ㅤ	Count on image average	Area on image average
car➔ rectangle	1621	5474	3.38	5.5%
bus➔ rectangle	1558	3339	2.14	10.37%
motorbike➔ rectangle	1186	2284	1.93	4.25%
three wheelers -CNG-➔ rectangle	1169	2986	2.55	3.8%
rickshaw➔ rectangle	1020	3539	3.47	8.33%
truck➔ rectangle	843	1495	1.77	10.5%
pickup➔ rectangle	793	1225	1.54	5.69%
minivan➔ rectangle	575	932	1.62	3.34%
suv➔ rectangle	538	858	1.59	3.08%
van➔ 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.

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.

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.

Rows 1-10 of 24348

Object ID ㅤ	Class ㅤ	Image name click row to open	Image size height x width	Height ㅤ	Height ㅤ	Width ㅤ	Width ㅤ	Area ㅤ
1➔	motorbike rectangle	Pias--171-_jpg.rf.b7121ed4eb1ae48b1ba8675c0378cd97.jpg	359 x 640	97px	27.02%	38px	5.94%	1.6%
2➔	car rectangle	Pias--171-_jpg.rf.b7121ed4eb1ae48b1ba8675c0378cd97.jpg	359 x 640	36px	10.03%	88px	13.75%	1.38%
3➔	car rectangle	Pias--171-_jpg.rf.b7121ed4eb1ae48b1ba8675c0378cd97.jpg	359 x 640	36px	10.03%	109px	17.03%	1.71%
4➔	car rectangle	Pias--171-_jpg.rf.b7121ed4eb1ae48b1ba8675c0378cd97.jpg	359 x 640	30px	8.36%	30px	4.69%	0.39%
5➔	car rectangle	Pias--171-_jpg.rf.b7121ed4eb1ae48b1ba8675c0378cd97.jpg	359 x 640	76px	21.17%	110px	17.19%	3.64%
6➔	car rectangle	Pias--171-_jpg.rf.b7121ed4eb1ae48b1ba8675c0378cd97.jpg	359 x 640	110px	30.64%	232px	36.25%	11.11%
7➔	car rectangle	Pias--171-_jpg.rf.b7121ed4eb1ae48b1ba8675c0378cd97.jpg	359 x 640	106px	29.53%	140px	21.88%	6.46%
8➔	car rectangle	Pias--171-_jpg.rf.b7121ed4eb1ae48b1ba8675c0378cd97.jpg	359 x 640	27px	7.52%	62px	9.69%	0.73%
9➔	car rectangle	Pias--171-_jpg.rf.b7121ed4eb1ae48b1ba8675c0378cd97.jpg	359 x 640	30px	8.36%	33px	5.16%	0.43%
10➔	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 = { may },
  note = { visited on 2024-05-16 },
}

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.

. . .

Disclaimer #

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