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Vehicle Detection 8 Classes Dataset

821881305
Tagsurveillance
Taskobject detection
Release YearMade in 2020
Licenseunknown

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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Dataset LinkHomepage

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.

OpenSample annotation mask from Vehicle Detection 8 ClassesSample image from Vehicle Detection 8 Classes
OpenSample annotation mask from Vehicle Detection 8 ClassesSample image from Vehicle Detection 8 Classes
OpenSample annotation mask from Vehicle Detection 8 ClassesSample image from Vehicle Detection 8 Classes
OpenSample annotation mask from Vehicle Detection 8 ClassesSample image from Vehicle Detection 8 Classes
OpenSample annotation mask from Vehicle Detection 8 ClassesSample image from Vehicle Detection 8 Classes
OpenSample annotation mask from Vehicle Detection 8 ClassesSample image from Vehicle Detection 8 Classes
OpenSample annotation mask from Vehicle Detection 8 ClassesSample image from Vehicle Detection 8 Classes
OpenSample annotation mask from Vehicle Detection 8 ClassesSample image from Vehicle Detection 8 Classes
OpenSample annotation mask from Vehicle Detection 8 ClassesSample image from Vehicle Detection 8 Classes
OpenSample annotation mask from Vehicle Detection 8 ClassesSample image from Vehicle Detection 8 Classes
OpenSample annotation mask from Vehicle Detection 8 ClassesSample image from Vehicle Detection 8 Classes
OpenSample annotation mask from Vehicle Detection 8 ClassesSample 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.

Search
Rows 1-8 of 8
Class
γ…€
Images
γ…€
Objects
γ…€
Count on image
average
Area on image
average
carβž”
rectangle
5797
11425
1.97
2.19%
light_motor_vehicleβž”
rectangle
4131
7285
1.76
0.78%
multi-axleβž”
rectangle
2607
2963
1.14
3.38%
autoβž”
rectangle
1229
1319
1.07
4.82%
truckβž”
rectangle
1078
1147
1.06
7.23%
busβž”
rectangle
937
969
1.03
3.46%
motorcycleβž”
rectangle
727
819
1.13
1.09%
tractorβž”
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.

Search
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.

Spatial Heatmap

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.

Search
Rows 1-10 of 26098
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
Highway_1111_2020-07-30_jpg.rf.786c07c97da4224393ddd29f49f86d13.jpg
416 x 416
77px
18.51%
44px
10.58%
1.96%
2βž”
motorcycle
rectangle
Highway_1111_2020-07-30_jpg.rf.786c07c97da4224393ddd29f49f86d13.jpg
416 x 416
37px
8.89%
18px
4.33%
0.38%
3βž”
car
rectangle
Highway_1111_2020-07-30_jpg.rf.786c07c97da4224393ddd29f49f86d13.jpg
416 x 416
25px
6.01%
16px
3.85%
0.23%
4βž”
bus
rectangle
Highway_140_2020-07-30_jpg.rf.b22d8e63ac5e51d185d2967c6af95f5e.jpg
416 x 416
90px
21.63%
49px
11.78%
2.55%
5βž”
car
rectangle
Highway_140_2020-07-30_jpg.rf.b22d8e63ac5e51d185d2967c6af95f5e.jpg
416 x 416
80px
19.23%
54px
12.98%
2.5%
6βž”
light_motor_vehicle
rectangle
Highway_140_2020-07-30_jpg.rf.b22d8e63ac5e51d185d2967c6af95f5e.jpg
416 x 416
20px
4.81%
12px
2.88%
0.14%
7βž”
multi-axle
rectangle
ulu1924_jpg.rf.8e8fc09cc4c8897c2d20a74ab3b4d64f.jpg
416 x 416
113px
27.16%
62px
14.9%
4.05%
8βž”
multi-axle
rectangle
ulu1924_jpg.rf.8e8fc09cc4c8897c2d20a74ab3b4d64f.jpg
416 x 416
155px
37.26%
83px
19.95%
7.43%
9βž”
car
rectangle
ulu678_jpg.rf.fcdf0fb77641782d865940a5d8a939a4.jpg
416 x 416
132px
31.73%
52px
12.5%
3.97%
10βž”
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 = { mar },
  note = { visited on 2024-03-05 },
}

Download #

Please visit dataset homepage to download the data.

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