Introduction #
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.
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.
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.
Class ã…¤ | Images ã…¤ | Objects ã…¤ | Count on image average | Area on image average |
---|---|---|---|---|
carâž” rectangle | 5797 | 11425 | 1.97 | 2.19% |
light_motor_vehicleâž” rectangle | 4131 | 7285 | 1.76 | 0.78% |
multi-axleâž” rectangle | 2607 | 2963 | 1.14 | 3.38% |
autoâž” rectangle | 1229 | 1319 | 1.07 | 4.82% |
truckâž” rectangle | 1078 | 1147 | 1.06 | 7.23% |
busâž” rectangle | 937 | 969 | 1.03 | 3.46% |
motorcycleâž” rectangle | 727 | 819 | 1.13 | 1.09% |
tractorâž” 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.
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.
Object ID ã…¤ | Class ã…¤ | Image name click row to open | Image size height x width | Height ã…¤ | Height ã…¤ | Width ã…¤ | Width ã…¤ | Area ã…¤ |
---|---|---|---|---|---|---|---|---|
1âž” | car rectangle | Highway_1111_2020-07-30_jpg.rf.786c07c97da4224393ddd29f49f86d13.jpg | 416 x 416 | 77px | 18.51% | 44px | 10.58% | 1.96% |
2âž” | motorcycle rectangle | Highway_1111_2020-07-30_jpg.rf.786c07c97da4224393ddd29f49f86d13.jpg | 416 x 416 | 37px | 8.89% | 18px | 4.33% | 0.38% |
3âž” | car rectangle | Highway_1111_2020-07-30_jpg.rf.786c07c97da4224393ddd29f49f86d13.jpg | 416 x 416 | 25px | 6.01% | 16px | 3.85% | 0.23% |
4âž” | bus rectangle | Highway_140_2020-07-30_jpg.rf.b22d8e63ac5e51d185d2967c6af95f5e.jpg | 416 x 416 | 90px | 21.63% | 49px | 11.78% | 2.55% |
5âž” | car rectangle | Highway_140_2020-07-30_jpg.rf.b22d8e63ac5e51d185d2967c6af95f5e.jpg | 416 x 416 | 80px | 19.23% | 54px | 12.98% | 2.5% |
6âž” | light_motor_vehicle rectangle | Highway_140_2020-07-30_jpg.rf.b22d8e63ac5e51d185d2967c6af95f5e.jpg | 416 x 416 | 20px | 4.81% | 12px | 2.88% | 0.14% |
7âž” | multi-axle rectangle | ulu1924_jpg.rf.8e8fc09cc4c8897c2d20a74ab3b4d64f.jpg | 416 x 416 | 113px | 27.16% | 62px | 14.9% | 4.05% |
8âž” | multi-axle rectangle | ulu1924_jpg.rf.8e8fc09cc4c8897c2d20a74ab3b4d64f.jpg | 416 x 416 | 155px | 37.26% | 83px | 19.95% | 7.43% |
9âž” | car rectangle | ulu678_jpg.rf.fcdf0fb77641782d865940a5d8a939a4.jpg | 416 x 416 | 132px | 31.73% | 52px | 12.5% | 3.97% |
10âž” | 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.
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}
}
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 = { nov },
note = { visited on 2024-11-11 },
}
Download #
Please visit dataset homepage to download the data.
Disclaimer #
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