Vehicle Dataset for YOLO

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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Homepage

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.

Sample annotation mask from Vehicle Dataset for YOLO

Sample image from Vehicle Dataset for YOLO

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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.

Rows 1-6 of 6

Class ㅤ	Images ㅤ	Objects ㅤ	Count on image average	Area on image average
car➔ rectangle	590	662	1.12	41.79%
motorbike➔ rectangle	537	697	1.3	47.49%
threewheel➔ rectangle	518	706	1.36	43.46%
van➔ rectangle	515	548	1.06	60.84%
truck➔ rectangle	504	629	1.25	57.02%
bus➔ 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.

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.

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.

Rows 1-10 of 3830

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➔	car rectangle	car11.jpg	600 x 1200	369px	61.5%	1036px	86.33%	53.09%
2➔	motorbike rectangle	2009_002651.jpg	375 x 500	206px	54.93%	228px	45.6%	25.05%
3➔	threewheel rectangle	threewheel91.jpeg	987 x 740	808px	81.86%	595px	80.41%	65.82%
4➔	van rectangle	indianvan419.jpeg	296 x 565	252px	85.14%	473px	83.72%	71.27%
5➔	threewheel rectangle	45041.jpg	480 x 640	479px	99.79%	473px	73.91%	73.75%
6➔	bus rectangle	dikwella-sri-lanka-january-2-260nw-252877207.jpg	280 x 393	78px	27.86%	84px	21.37%	5.95%
7➔	bus rectangle	dikwella-sri-lanka-january-2-260nw-252877207.jpg	280 x 393	71px	25.36%	64px	16.28%	4.13%
8➔	truck rectangle	srilankalorry190.jpeg	477 x 637	436px	91.4%	437px	68.6%	62.71%
9➔	bus rectangle	1302906767_b2e92de824_w.jpg	219 x 400	197px	89.95%	383px	95.75%	86.13%
10➔	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 = { nov },
  note = { visited on 2024-11-21 },
}

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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