Vehicle Detection 8 Classes

Introduction #

Saksham Jain

The Vehicle Detection 8 Classes Dataset is a robust collection designed for comprehensive object detection tasks, specifically concentrating on identifying and localizing vehicles. Comprising a substantial 8218 images, the dataset boasts an impressive 26098 annotated objects distributed among 8 distinct classes, encompassing vehicles like car, light_motor_vehicle, multi-axle, along with others such as auto, truck, bus, motorcycle, and tractor. With a focus on traffic analysis, each image within the dataset is equipped with boundary-box annotations, allowing for precise delineation and identification of vehicles, offering a valuable resource for applications related to traffic monitoring, object detection, and machine learning model training specifically tailored for traffic-related scenarios.

Please note, that highway_3776_2020-08-26 and highway_3708_2020-08-26 images have wrong labels.

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Summary #

Vehicle Detection 8 Classes is a dataset for an object detection task. Possible applications of the dataset could be in the traffic monitoring industry.

The dataset consists of 8218 images with 26098 labeled objects belonging to 8 different classes including car, light_motor_vehicle, multi-axle, and other: auto, truck, bus, motorcycle, and tractor.

Images in the Vehicle Detection 8 Classes dataset have bounding box annotations. There are 18 (0% of the total) unlabeled images (i.e. without annotations). There is 1 split in the dataset: train (8218 images). The dataset was released in 2020.

Here are the visualized examples for the classes:

Explore #

Vehicle Detection 8 Classes dataset has 8218 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 Detection 8 Classes

Sample image from Vehicle Detection 8 Classes

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Have a look at 8218 images

Because of dataset's license preview is limited to 12 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.

Rows 1-8 of 8

Class ㅤ	Images ㅤ	Objects ㅤ	Count on image average	Area on image average
car➔ rectangle	5797	11425	1.97	2.19%
light_motor_vehicle➔ rectangle	4131	7285	1.76	0.78%
multi-axle➔ rectangle	2607	2963	1.14	3.38%
auto➔ rectangle	1229	1319	1.07	4.82%
truck➔ rectangle	1078	1147	1.06	7.23%
bus➔ rectangle	937	969	1.03	3.46%
motorcycle➔ rectangle	727	819	1.13	1.09%
tractor➔ rectangle	170	171	1.01	2.67%

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 1-8 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
car rectangle	11425	1.11%	9.92%	0.05%	8px	1.92%	254px	49.52%	57px	12.76%	8px	1.92%	121px	22.36%
light_motor_vehicle rectangle	7285	0.44%	3.52%	0.05%	13px	3.12%	146px	31.01%	42px	9.46%	6px	1.44%	80px	15.62%
multi-axle rectangle	2963	2.98%	29.61%	0.11%	17px	4.09%	374px	79.57%	104px	22.56%	10px	2.4%	207px	43.99%
auto rectangle	1319	4.5%	26.89%	0.16%	22px	5.29%	369px	77.64%	127px	27.98%	11px	2.64%	212px	43.03%
truck rectangle	1147	6.8%	40.25%	0.16%	21px	5.05%	405px	95.43%	166px	36.96%	12px	2.88%	213px	51.2%
bus rectangle	969	3.35%	21.58%	0.15%	17px	4.09%	429px	69.95%	115px	25.19%	13px	3.12%	206px	35.34%
motorcycle rectangle	819	0.97%	5.98%	0.11%	17px	4.09%	167px	40.14%	58px	13.04%	10px	2.4%	84px	16.59%
tractor rectangle	171	2.65%	10.56%	0.2%	25px	6.01%	234px	50.48%	96px	22.31%	14px	3.37%	101px	24.28%

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 26098 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 26098

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	Highway_1111_2020-07-30_jpg.rf.786c07c97da4224393ddd29f49f86d13.jpg	416 x 416	77px	18.51%	44px	10.58%	1.96%
2➔	motorcycle rectangle	Highway_1111_2020-07-30_jpg.rf.786c07c97da4224393ddd29f49f86d13.jpg	416 x 416	37px	8.89%	18px	4.33%	0.38%
3➔	car rectangle	Highway_1111_2020-07-30_jpg.rf.786c07c97da4224393ddd29f49f86d13.jpg	416 x 416	25px	6.01%	16px	3.85%	0.23%
4➔	bus rectangle	Highway_140_2020-07-30_jpg.rf.b22d8e63ac5e51d185d2967c6af95f5e.jpg	416 x 416	90px	21.63%	49px	11.78%	2.55%
5➔	car rectangle	Highway_140_2020-07-30_jpg.rf.b22d8e63ac5e51d185d2967c6af95f5e.jpg	416 x 416	80px	19.23%	54px	12.98%	2.5%
6➔	light_motor_vehicle rectangle	Highway_140_2020-07-30_jpg.rf.b22d8e63ac5e51d185d2967c6af95f5e.jpg	416 x 416	20px	4.81%	12px	2.88%	0.14%
7➔	multi-axle rectangle	ulu1924_jpg.rf.8e8fc09cc4c8897c2d20a74ab3b4d64f.jpg	416 x 416	113px	27.16%	62px	14.9%	4.05%
8➔	multi-axle rectangle	ulu1924_jpg.rf.8e8fc09cc4c8897c2d20a74ab3b4d64f.jpg	416 x 416	155px	37.26%	83px	19.95%	7.43%
9➔	car rectangle	ulu678_jpg.rf.fcdf0fb77641782d865940a5d8a939a4.jpg	416 x 416	132px	31.73%	52px	12.5%	3.97%
10➔	bus rectangle	ulu678_jpg.rf.fcdf0fb77641782d865940a5d8a939a4.jpg	416 x 416	126px	30.29%	58px	13.94%	4.22%

License #

License is unknown for the Vehicle Detection 8 Classes dataset.

Source

Citation #

If you make use of the Vehicle Detection 8 Classes data, please cite the following reference:

@dataset{Vehicle Detection 8 Classes,
  author={Saksham Jain},
  title={Vehicle Detection 8 Classes},
  year={2020},
  url={https://www.kaggle.com/datasets/sakshamjn/vehicle-detection-8-classes-object-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-detection-8-classes-dataset,
  title = { Visualization Tools for Vehicle Detection 8 Classes Dataset },
  type = { Computer Vision Tools },
  author = { Dataset Ninja },
  howpublished = { \url{ https://datasetninja.com/vehicle-detection-8-classes } },
  url = { https://datasetninja.com/vehicle-detection-8-classes },
  journal = { Dataset Ninja },
  publisher = { Dataset Ninja },
  year = { 2024 },
  month = { jul },
  note = { visited on 2024-07-27 },
}

Download #

Please visit dataset homepage to download the data.

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