The YOLOv8 Car Object Detection Dataset provides a large collection of images annotated with bounding boxes for cars across diverse real-world environments, supporting the development of robust AI models for autonomous driving, robotics, and surveillance.

The Traffic Vehicles Object Detection dataset is a valuable resource containing 1,201 images capturing the dynamic world of traffic, featuring 11,134 meticulously labeled objects. These objects are classified into seven distinct categories, including common vehicles like car, two_wheeler, as well as blur_number_plate, and other essential elements such as auto, number_plate, bus, and truck. The dataset's origins lie in the collection of training images from traffic scenes and CCTV footage, followed by precise object annotation and labeling, making it an ideal tool for object detection tasks in the realm of transportation and surveillance.

nihany/car-object-detection dataset hosted on Hugging Face and contributed by the HF Datasets community

This dataset contians labelled vehicle images gathered from Kaggle, Standford Car Dataset and Web scraping.

Dataset size: 3000 images Each class: 500 images Format: YOLO (txt) Train/Valid split: 70%(2100 images) : 30 (900 images)

Classes:

Car Threewheel Bus Truck Motorbike Van

The Car Object Detection Dataset by GTS.AI is a comprehensive dataset featuring car images and videos captured from various regions and lighting conditions. Designed for computer vision, autonomous driving, and smart traffic systems, it supports deep learning applications for vehicle detection and localization. Each dataset undergoes manual annotation, quality control, and ISO-certified validation.

Car Object Detection Project

## Overview

Car Object Detection Project is a dataset for object detection tasks - it contains Cars 1RJ0 annotations for 3,835 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).

Vehicle Detection Image Dataset

Introduction

Welcome to the Vehicle Detection Image Dataset! This dataset is meticulously curated for object detection and tracking tasks, with a specific focus on vehicle detection. It serves as a valuable resource for researchers, developers, and enthusiasts seeking to advance the capabilities of computer vision systems.

Objective

The primary aim of this dataset is to facilitate precise object detection tasks, particularly in identifying and tracking vehicles within images. Whether you are engaged in academic research, developing commercial applications, or exploring the frontiers of computer vision, this dataset provides a solid foundation for your projects.

Preprocessing and Augmentation

Both versions of the dataset undergo essential preprocessing steps, including resizing and orientation adjustments. Additionally, the Apply_Grayscale version undergoes augmentation to introduce grayscale variations, thereby enriching the dataset and improving model robustness.

1. Apply_Grayscale

• This version comprises grayscale images and is further augmented to enhance the diversity of training data.

https://www.googleapis.com/download/storage/v1/b/kaggle-user-content/o/inbox%2F14850461%2F4f23bd8094c892d1b6986c767b42baf4%2Fv2.png?generation=1712264632232641&alt=media" alt="">

2. No_Apply_Grayscale

• This version includes images without applying grayscale augmentation.

https://www.googleapis.com/download/storage/v1/b/kaggle-user-content/o/inbox%2F14850461%2Fbfb10eb2a4db31a62eb4615da824c387%2Fdetails_v1.png?generation=1712264660626280&alt=media" alt="">

Data Formats

To ensure compatibility with a wide range of object detection frameworks and tools, each version of the dataset is available in multiple formats:

1. COCO
2. YOLOv8
3. YOLOv9
4. TensorFlow

These formats facilitate seamless integration into various machine learning frameworks and libraries, empowering users to leverage their preferred development environments.

Real-Time Object Detection

In addition to image datasets, we also provide a video for real-time object detection evaluation. This video allows users to test the performance of their models in real-world scenarios, providing invaluable insights into the effectiveness of their detection algorithms.

Getting Started

To begin exploring the Vehicle Detection Image Dataset, simply download the version and format that best suits your project requirements. Whether you are an experienced practitioner or just embarking on your journey in computer vision, this dataset offers a valuable resource for advancing your understanding and capabilities in object detection and tracking tasks.

Citation

If you utilize this dataset in your work, we kindly request that you cite the following:

Parisa Karimi Darabi. (2024). Vehicle Detection Image Dataset: Suitable for Object Detection and tracking Tasks. Retrieved from https://www.kaggle.com/datasets/pkdarabi/vehicle-detection-image-dataset/

Feedback and Contributions

I welcome feedback and contributions from the Kaggle community to continually enhance the quality and usability of this dataset. Please feel free to reach out if you have suggestions, questions, or additional data and annotations to contribute. Together, we can drive innovation and progress in computer vision.

BrainAI Car Object Detection

## Overview

BrainAI Car Object Detection is a dataset for object detection tasks - it contains Directions annotations for 3,913 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 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.

This dataset is designed for the detection of persons and cars in surveillance camera footage. It can be utilized for various useful applications, including:

• Security Systems: Enhancing security measures by accurately detecting and tracking persons and cars in real-time surveillance videos.
• Traffic Monitoring: Analyzing traffic patterns, estimating congestion levels, and optimizing traffic flow by detecting and counting cars on roads or at intersections.
• Safety Enhancement: Identifying potential hazards and ensuring public safety by detecting unauthorized access or suspicious activities involving persons and cars.
• Crowd Management: Monitoring crowded areas and public events to ensure safety, identify crowd density, and estimate crowd movement by detecting and tracking persons.
• Parking Systems: Optimizing parking lot management by detecting available parking spots and monitoring the entry and exit of vehicles.
• Smart Cities: Contributing to the development of smart city infrastructure by integrating the detection of persons and cars into intelligent systems for efficient urban planning and management.

This dataset is based on images collected from various sources, including:

https://universe.roboflow.com/radoslaw-kawczak/virat-ve02s

https://universe.roboflow.com/seminar-object-detection/cars-o1ljf

With this dataset, you can train and develop machine learning models capable of accurately detecting persons and cars, thus empowering surveillance and security systems with advanced object recognition capabilities.

This dataset provides a collection of labeled aerial images of vehicles taken from drones in the campus of Prince Sultan University, Saudi Arabia.

The PSU dataset was collected from two sources: an open dataset of aerial images available on Github repo Aerial-car-dataset, available online on: https://github.com/jekhor/aerial-cars-dataset

and our own images acquired after flying a 3DR SOLO drone equipped with a GoPro Hero 4 camera, in an outdoor environment at PSU parking lot. The drone recorded videos from which frames were extracted and manually labeled. Since we are only interested in a single class, images with no cars have been removed from the dataset. The training/testing split was made randomly.

https://www.googleapis.com/download/storage/v1/b/kaggle-user-content/o/inbox%2F12447376%2F7f567f46d0bd35c3a3d5939356d28bee%2FScreenshot%20from%202023-08-29%2013-23-49.png?generation=1693304666549998&alt=media" alt=""> Total number of images: 270

If you use this dataset in a research paper, please cite the following reference: Ammar, A., Koubaa, A., Ahmed, M., Saad, A. and Benjdira, B., 2021. Vehicle detection from aerial images using deep learning: A comparative study. Electronics, 10(7), p.820.

A comprehensive dataset for detecting and classifying traffic vehicles in diverse road and environmental conditions, suitable for training advanced computer vision and autonomous driving models.

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

This dataset was created by VivekPadmanaban

Contents

Vehicle Detection Dataset

This dataset is designed for vehicle detection tasks, featuring a comprehensive collection of images annotated for object detection. This dataset, originally sourced from Roboflow (https://universe.roboflow.com/object-detection-sn8ac/ai-traffic-system), was exported on May 29, 2025, at 4:59 PM GMT and is now publicly available on Kaggle under the CC BY 4.0 license.

Overview

• Purpose: The dataset supports the development of computer vision models for detecting various types of vehicles in traffic scenarios.
• Classes: The dataset includes annotations for 7 vehicle types:
  • Bicycle
  • Bus
  • Car
  • Motorbike
  • Rickshaw
  • Truck
  • Van
• Number of Images: The dataset contains 9,440 images, split into training, validation, and test sets:
  • Training: Images located in ../train/images
  • Validation: Images located in ../valid/images
  • Test: Images located in ../test/images
• Annotation Format: Images are annotated in YOLOv11 format, suitable for training state-of-the-art object detection models.
• Pre-processing: Each image has been resized to 640x640 pixels (stretched). No additional image augmentation techniques were applied.

Source and Creation

This dataset was created and exported via Roboflow, an end-to-end computer vision platform that facilitates collaboration, image collection, annotation, dataset creation, model training, and deployment. The dataset is part of the ai-traffic-system project (version 1) under the workspace object-detection-sn8ac. For more details, visit: https://universe.roboflow.com/object-detection-sn8ac/ai-traffic-system/dataset/1.

Usage

This dataset is ideal for researchers, data scientists, and developers working on vehicle detection and traffic monitoring systems. It can be used to: - Train and evaluate deep learning models for object detection, particularly using the YOLOv11 framework. - Develop AI-powered traffic management systems, autonomous driving applications, or urban mobility solutions. - Explore computer vision techniques for real-world traffic scenarios.

For advanced training notebooks compatible with this dataset, check out: https://github.com/roboflow/notebooks. To explore additional datasets and pre-trained models, visit: https://universe.roboflow.com.

License

The dataset is licensed under CC BY 4.0, allowing for flexible use, sharing, and adaptation, provided appropriate credit is given to the original source.

This dataset is a valuable resource for building robust vehicle detection models and advancing computer vision applications in traffic systems.

This dataset contains 29,800 labeled entries for vehicle detection tasks. Each entry provides bounding box coordinates for a car within an image, along with its dimensions and class label. The dataset is well-suited for object detection, vehicle counting, and traffic surveillance model training.

Columns:

• filename – Image file name containing the car(s)
• width – Width of the image in pixels
• height – Height of the image in pixels
• class – Object class label (e.g., "car")
• xmin – Left x-coordinate of the bounding box
• ymin – Top y-coordinate of the bounding box
• xmax – Right x-coordinate of the bounding box
• ymax – Bottom y-coordinate of the bounding box

Potential Uses:

• Training YOLO, Faster R-CNN, or SSD object detection models
• Vehicle recognition in CCTV footage
• Autonomous driving system datasets
• Traffic monitoring and analysis

Format: CSV file with 8 columns containing bounding box annotations for each object in an image.

A comprehensive dataset of car images annotated for object detection tasks. It includes train, validation, and test sets with bounding box coordinates for each vehicle.

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).

Dataset

This dataset was created by Kinda Mashal

Released under Apache 2.0

Contents

A meticulously curated and annotated dataset of car images, segmented into seven categories representing different makes and models. Designed for computer vision, image classification, and object detection applications.