Object Detection Cars

## Overview

Object Detection Cars is a dataset for object detection tasks - it contains Car annotations for 633 images.

## Getting Started

You can download this dataset for use within your own projects, or fork it into a workspace on Roboflow to create your own model.

  ## License

  This dataset is available under the [CC BY 4.0 license](https://creativecommons.org/licenses/CC BY 4.0).

https://i.imgur.com/ztezlER.png" alt="Image example">

Overview

This dataset contains 627 images of various vehicle classes for object detection. These images are derived from the Open Images open source computer vision datasets.

This dataset only scratches the surface of the Open Images dataset for vehicles!

https://i.imgur.com/4ZHN8kk.png" alt="Image example">

Use Cases

• Train object detector to differentiate between a car, bus, motorcycle, ambulance, and truck.
• Checkpoint object detector for autonomous vehicle detector
• Test object detector on high density of ambulances in vehicles
• Train ambulance detector
• Explore the quality and range of Open Image dataset

Tools Used to Derive Dataset

https://i.imgur.com/1U0M573.png" alt="Image example">

These images were gathered via the OIDv4 Toolkit This toolkit allows you to pick an object class and retrieve a set number of images from that class with bound box lables.

We provide this dataset as an example of the ability to query the OID for a given subdomain. This dataset can easily be scaled up - please reach out to us if that interests you.

Dataset Description: Car Object Detection in Road Traffic

Overview:

This dataset is designed for car object detection in road traffic scenes (Images with shape 1080x1920x3). The dataset is derived from publicly available video content on YouTube, specifically from the video with the Creative Commons Attribution license, available here. https://youtu.be/MNn9qKG2UFI?si=uJz_WicTCl8zfrVl" alt="youtube video">

Source:

• Video Source: YouTube Video.
• License: Creative Commons Attribution (reuse allowed) more details here.
• Dataset Contents: The dataset consists of a collection of image frames extracted from the video. Each image frame captures various scenes from road traffic. Car objects within these frames are annotated with bounding boxes.

Annotation Details:

• Bounding Boxes: Each image frame contains annotated bounding boxes around car objects, marking their locations in the scene.
• Classes: The dataset is focused on car object detection, and car objects are labeled as the target class (aka one class only).
• Data Format: Images are provided in JPEG format.
• Annotation files are provided in YOLO text format.
• We used labelImg GUI to label this dataset in YOLO format, more details are in this GitHub repo.

Use Cases:

• Object Detection: This dataset can be used to train and evaluate object detection models, with an emphasis on detecting cars in road traffic scenarios.

Acknowledgments: We acknowledge and thank the creator of the original video for making it available under a Creative Commons Attribution license. Their contribution enables the development of datasets and research in the field of computer vision and object detection.

Disclaimer: This dataset is provided for educational and research purposes and should be used in compliance with YouTube's terms of service and the Creative Commons Attribution license.

This dataset features over 1,000,000 high-quality images of cars, sourced globally from photographers, enthusiasts, and automotive content creators. Optimized for AI and machine learning applications, it provides richly annotated and visually diverse automotive imagery suitable for a wide array of use cases in mobility, computer vision, and retail.

Key Features: 1. Comprehensive Metadata: each image includes full EXIF data and detailed annotations such as car make, model, year, body type, view angle (front, rear, side, interior), and condition (e.g., showroom, on-road, vintage, damaged). Ideal for training in classification, detection, OCR for license plates, and damage assessment.

1. Unique Sourcing Capabilities: the dataset is built from images submitted through a proprietary gamified photography platform with auto-themed competitions. Custom datasets can be delivered within 72 hours targeting specific brands, regions, lighting conditions, or functional contexts (e.g., race cars, commercial vehicles, taxis).

2. Global Diversity: contributors from over 100 countries ensure broad coverage of car types, manufacturing regions, driving orientations, and environmental settings—from luxury sedans in urban Europe to pickups in rural America and tuk-tuks in Southeast Asia.

3. High-Quality Imagery: images range from standard to ultra-HD and include professional-grade automotive photography, dealership shots, roadside captures, and street-level scenes. A mix of static and dynamic compositions supports diverse model training.

4. Popularity Scores: each image includes a popularity score derived from GuruShots competition performance, offering valuable signals for consumer appeal, aesthetic evaluation, and trend modeling.

5. AI-Ready Design: this dataset is structured for use in applications like vehicle detection, make/model recognition, automated insurance assessment, smart parking systems, and visual search. It’s compatible with all major ML frameworks and edge-device deployments.

6. Licensing & Compliance: fully compliant with privacy and automotive content use standards, offering transparent and flexible licensing for commercial and academic use.

Use Cases: 1. Training AI for vehicle recognition in smart city, surveillance, and autonomous driving systems. 2. Powering car search engines, automotive e-commerce platforms, and dealership inventory tools. 3. Supporting damage detection, condition grading, and automated insurance workflows. 4. Enhancing mobility research, traffic analytics, and vision-based safety systems.

This dataset delivers a large-scale, high-fidelity foundation for AI innovation in transportation, automotive tech, and intelligent infrastructure. Custom dataset curation and region-specific filters are available. Contact us to learn more!

Aerial Multi-Vehicle Detection Dataset: Efficient road traffic monitoring is playing a fundamental role in successfully resolving traffic congestion in cities. Unmanned Aerial Vehicles (UAVs) or drones equipped with cameras are an attractive proposition to provide flexible and infrastructure-free traffic monitoring. Due to the affordability of such drones, computer vision solutions for traffic monitoring have been widely used. Therefore, this dataset provide images that can be used for either training or evaluating Traffic Monitoring applications. More specifically, it can be used for training an aerial vehicle detection algorithm, benchmark an already trained vehicle detection algorithm, enhance an existing dataset and aid in traffic monitoring and analysis of road segments.

The dataset construction involved manually collecting all aerial images of vehicles using UAV drones and manually annotated into three classes 'Car', 'Bus', and ''Truck'.The aerial images were collected through manual flights in road segments in Nicosia or Limassol, Cyprus, during busy hours. The images are in High Quality, Full HD (1080p) to 4k (2160p) but are usually resized before training. All images were manually annotated and inspected afterward with the vehicles that indicate 'Car' for small to medium sized vehicles, 'Bus' for busses, and 'Truck' for large sized vehicles and trucks. All annotations were converted into VOC and COCO formats for training in numerous frameworks. The data collection took part in different periods, covering busy road segments in the cities of Nicosia and Limassol in Cyprus. The altitude of the flights varies between 150 to 250 meters high, with a top view perspective. Some of the images found in this dataset are taken from Harpy Data dataset [1]

The dataset includes a total of 9048 images of which 904 are split for validation, 905 for testing, and the rest 7239 for training.

Subset	Images	Car	Bus	Truck
Training	7239	200301	1601	6247
Validation	904	23397	193	727
Testing	905	24715	208	770

It is advised to further enhance the dataset so that random augmentations are probabilistically applied to each image prior to adding it to the batch for training. Specifically, there are a number of possible transformations such as geometric (rotations, translations, horizontal axis mirroring, cropping, and zooming), as well as image manipulations (illumination changes, color shifting, blurring, sharpening, and shadowing).

[1] Makrigiorgis, R., 2021. Harpy Data Dataset. [online] Kios.ucy.ac.cy. Available at: <https://www.kios.ucy.ac.cy/harpydata/> [Accessed 22 September 2022].

**NOTE** If you use this dataset in your research/publication please cite us using the following :

Rafael Makrigiorgis, Panayiotis Kolios, & Christos Kyrkou. (2022). Aerial Multi-Vehicle Detection Dataset (1.0) [Data set]. Zenodo. https://doi.org/10.5281/zenodo.7053442

Car And Truck Detection

## Overview

Car And Truck Detection is a dataset for object detection tasks - it contains Cars Trucks annotations for 3,617 images.

## Getting Started

You can download this dataset for use within your own projects, or fork it into a workspace on Roboflow to create your own model.

  ## License

  This dataset is available under the [CC BY 4.0 license](https://creativecommons.org/licenses/CC BY 4.0).

Cars Object Tracking

Dataset comprises 10,000+ video frames featuring both light vehicles (cars) and heavy vehicles (minivans). This extensive collection is meticulously designed for research in multi-object tracking and object detection, providing a robust foundation for developing and evaluating various tracking algorithms for road safety system development. By utilizing this dataset, researchers can significantly enhance their understanding of vehicle dynamics and improve… See the full description on the dataset page: https://huggingface.co/datasets/UniDataPro/cars-object-tracking.

Great dataset to practice object detection algorithms. Data structured in YOLOv8 format with train, validation and test datasets along with labels. Also includes the yaml file. There is also a separate test video loaded so if you want to evaluate your model using the test video, you can also use that. Evaluation can be done using the test images as well as the test video. Data is from Roboflow website.

This dataset contains annotated images of Polish roads, specifically curated for object detection tasks. The data was collected using a car camera on roads in Poland, primarily in Kraków. The images capture a diverse range of scenarios, including different road types and various lighting conditions (day and night).

Classes:

• Car (Vehicles without a trailer)
• Different-Traffic-Sign (Other traffic signs than warning and prohibition signs, mostly information and order signs)
• Green-Traffic-Light (Green traffic lights for cars only; green lights for pedestrians are not annotated)
• Motorcycle
• Pedestrian (People and cyclists)
• Pedestrian-Crossing (Pedestrian crossings)
• Prohibition-Sign (All prohibition signs)
• Red-Traffic-Light (Red traffic lights for cars only; lights for pedestrians are not annotated)
• Speed-Limit-Sign (Speed limit signs)
• Truck (Vehicles with a trailer)
• Warning-Sign (Warning signs)

Annotation Process:

Annotations were carried out using Roboflow. A total of 2,000 images were manually labeled, while an additional 9,000 images were generated through data augmentation. The labeled techniques applied were crop, saturation, brightness, and exposure adjustments.

Image Statistics Before Data Augmentation:

Approximately

• 400 cars per 100 photos
• 30 different-traffic-signs per photos
• 80 red-traffic-lights per photos
• 70 pedestrians per photos
• 50 warning signs per photos
• 50 pedestrian-crossings per photos
• 40 green-traffic-lights per photos
• 40 prohibition signs per photos
• 40 trucks per photos
• 20 speed-limit-signs per photos
• 2 motorcycles per photos

The photos were taken on both normal roads and highways, under various conditions, including day and night. All photos were initially 1920x1080 pixels. After cropping, some images may be slightly smaller. No preprocessing steps were applied to the photos.

Annotations are provided in YOLO format.

Image Statistics Before Data Augmentation:

Set	Photos	Car	Different-Traffic-Sign	Red-Traffic-Light	Pedestrian	Warning-Sign	Pedestrian-Crossing	Green-Traffic-Light	Prohibition-Sign	Truck	Speed-Limit-Sign	Motorcycle
Test Set	166	687	547	163	137	79	82	52	48	66	22	4
Train Set	1178	4766	3370	805	812	544	476	402	396	409	230	38
Validation Set	327	1343	945	232	228	163	112	87	112	137	59	10

Image Statistics After Data Augmentation:

Set	Photos	Car	Different-Traffic-Sign	Red-Traffic-Light	Pedestrian	Warning-Sign	Pedestrian-Crossing	Green-Traffic-Light	Prohibition-Sign	Truck	Speed-Limit-Sign	Motorcycle
Test Set	996	4122	3282	978	822	474	492	312	288	396	132	24
Train Set	7068	28596	20220	4830	4872	3264	2856	2412	2376	2454	1380	228
Validation Set	1962	8058	5670	1392	1368	978	672	522	672	822	354	60

People And Car Detection

## Overview

People And Car Detection is a dataset for object detection tasks - it contains Person annotations for 7,819 images.

## Getting Started

You can download this dataset for use within your own projects, or fork it into a workspace on Roboflow to create your own model.

  ## License

  This dataset is available under the [CC BY 4.0 license](https://creativecommons.org/licenses/CC BY 4.0).

Mini Traffic Detection dataset comprises 8 classes with 30 instances each, divided into 70% for training and 30% for validation. Primarily designed for computer vision tasks, it focuses on traffic object detection. It's an excellent choice for transfer learning with Detectron2 for custom object detection and segmentation projects. The dataset includes classes such as bicycle, bus, car, motorcycle, person, traffic_light, truck, and stop_sign.

A dataset suitable for spatiotemporal object detection is constructed using several aerial video clips of traffic in different road segments in Nicosia, Cyprus, captured using UAVs, rather than single areas in low resolution satelite images as other datasets. By compiling multiple sequences of images extracted from these videos, the dataset accumulates a substantial corpus of 6,600 frames. The dataset encapsulates 3 classes: ‘car’, ‘truck’ and ‘bus’ with a distribution of 81165, 1541, and 1625 respectively in the case that we only use the even frame annotations, which approximately doubles when considering the entire dataset. An additional challenge of the dataset that mirrors real world application is the fact that the classes are not balanced, as there is a significantly larger number of cars compared to trucks and buses, as in a regular transportation network. The images have Full-HD resolution, with object sizes approximately between 20x20 to 150x150 pixels. The dataset was prepared in the YOLO format. The dataset was split into 80% for training and the remaining 20% for validation. The importance of such a dataset lies in its capability to encapsulate both spatial and temporal nuances. We note the frames belonging in the same continuous sequence as such the dataset can potentially be used to develop approaches that operate on multiple sequential frames for object detection by sampling a number of frames from the same sequence. Dataset Feature Description Total Images ~6600 Image Sizes 1920x1080 Classes Car,Bus,Truck Data Collection Collect from UAVs at different locations in Nicosia, Cyprus Data Format PNG Labelling Format YOLO

here ?

1. just uploaded it here to make it easy for me and others to use
2. There is no data similar to it on kaggle

Vehicles-OpenImages > 416x416

https://public.roboflow.ai/object-detection/vehicles-openimages

Provided by Jacob Solawetz License: CC BY 4.0

https://i.imgur.com/ztezlER.png" alt="Image example">

Overview

This dataset contains 627 images of various vehicle classes for object detection. These images are derived from the Open Images open source computer vision datasets.

This dataset only scratches the surface of the Open Images dataset for vehicles!

https://i.imgur.com/4ZHN8kk.png" alt="Image example">

Use Cases

• Train object detector to differentiate between a car, bus, motorcycle, ambulance, and truck.
• Checkpoint object detector for autonomous vehicle detector
• Test object detector on high density of ambulances in vehicles
• Train ambulance detector
• Explore the quality and range of Open Image dataset

Tools Used to Derive Dataset

https://i.imgur.com/1U0M573.png" alt="Image example">

These images were gathered via the OIDv4 Toolkit This toolkit allows you to pick an object class and retrieve a set number of images from that class with bound box lables.

We provide this dataset as an example of the ability to query the OID for a given subdomain. This dataset can easily be scaled up - please reach out to us if that interests you.

1. Overview This dataset is a collection of 100,000+ images of cars in multiple scenes that are ready to use for optimizing the accuracy of computer vision models. All of the contents is sourced from PIXTA's stock library of 100M+ Asian-featured images and videos. PIXTA is the largest platform of visual materials in the Asia Pacific region offering fully-managed services, high quality contents and data, and powerful tools for businesses & organisations to enable their creative and machine learning projects.

2. Annotated Imagery Data of car images This dataset contains 4,000+ images of cars. Each data set is supported by both AI and human review process to ensure labelling consistency and accuracy. Contact us for more custom datasets.

3. About PIXTA PIXTASTOCK is the largest Asian-featured stock platform providing data, contents, tools and services since 2005. PIXTA experiences 15 years of integrating advanced AI technology in managing, curating, processing over 100M visual materials and serving global leading brands for their creative and data demands.

Car detection, often referred to as object detection in the context of computer vision and deep learning, is the task of identifying and locating cars within images or video frames...

• This dataset contains 499 images, each with bounding box annotations for cars.
• The annotations are provided in the YOLO text format, which includes class labels and bounding box coordinates.
• This dataset is useful for object detection tasks such as vehicle recognition and traffic analysis.

If you use this dataset, please cite this paper: Puertas, E.; De-Las-Heras, G.; Fernández-Andrés, J.; Sánchez-Soriano, J. Dataset: Roundabout Aerial Images for Vehicle Detection. Data 2022, 7, 47. https://doi.org/10.3390/data7040047

This publication presents a dataset of Spanish roundabouts aerial images taken from an UAV, along with annotations in PASCAL VOC XML files that indicate the position of vehicles within them. Additionally, a CSV file is attached containing information related to the location and characteristics of the captured roundabouts. This work details the process followed to obtain them: image capture, processing and labeling. The dataset consists of 985,260 total instances: 947,400 cars, 19,596 cycles, 2,262 trucks, 7,008 buses and 2,208 empty roundabouts, in 61,896 1920x1080px JPG images. These are divided into 15,474 extracted images from 8 roundabouts with different traffic flows and 46,422 images created using data augmentation techniques. The purpose of this dataset is to help research on computer vision on the road, as such labeled images are not abundant. It can be used to train supervised learning models, such as convolutional neural networks, which are very popular in object detection.

Roundabout (scenes)	Frames	Car	Truck	Cycle	Bus	Empty
1 (00001)	1,996	34,558	0	4229	0	0
2 (00002)	514	743	0	0	0	157
3 (00003-00017)	1,795	4822	58	0	0	0
4 (00018-00033)	1,027	6615	0	0	0	0
5 (00034-00049)	1,261	2248	0	550	0	81
6 (00050-00052)	5,501	180,342	1420	120	1376	0
7 (00053)	2,036	5,789	562	0	226	92
8 (00054)	1,344	1,733	222	0	150	222
Total	15,474	236,850	2,262	4,899	1,752	552
Data augmentation	x4	x4	x4	x4	x4	x4
Total	61,896	947,400	9048	19,596	7,008	2,208

person straight straight-right crossing-zebra obstacle red-area

CV IE Car Object Detection

## Overview

CV IE Car Object Detection is a dataset for object detection tasks - it contains Cars LVXf annotations for 355 images.

## Getting Started

You can download this dataset for use within your own projects, or fork it into a workspace on Roboflow to create your own model.

  ## License

  This dataset is available under the [CC BY 4.0 license](https://creativecommons.org/licenses/CC BY 4.0).

This dataset is prepared for our 2019 year "Amateur Drone Detection and Tracking" project. There are more than 4000 amateur drone pictures in the dataset, which is usually trained with amateur (like dji phantom) drones. In addition, the dataset contains non-drone, drone-like "negative" objects. This dataset was used with Yolov2-tiny, Yolov3-voc versions. Generally suitable for working with Yolo architecture and darknet framework.