The CVS Database provides a catalogue of original vehicle dimensions, for use in vehicle safety research and collision investigation. The purpose of this database is to provide users with a comprehensive listing of vehicle dimensions commonly used in the field of collision investigation and reconstruction, for the North American fleet of passenger cars, light trucks, vans and SUV’s. The database includes model years dating back to 2011 and is comprised of both commonly available dimensions such as overall length, wheelbase and track widths, and also several dimensions which are not typically readily available from the manufacturers, nor from automotive publications. Note – To obtain database of model years dating back to 1971, please contact Transport Canada.

Car Model Form Original Data

## Overview

Car Model Form Original Data is a dataset for object detection tasks - it contains Cars Model annotations for 2,554 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 light-duty vehicle inventory dataset provides information on vehicle registrations by vehicle type (car vs. truck), fuel type, and model year showing the changes in adoption trends over time and average fuel economies. This data is part of a suite of state and local energy profile data available at the "State and Local Energy Profile Data Suite" link below and builds on Cities-LEAP energy modeling, available at the "EERE Cities-LEAP Page" link below. Examples of how to use the data to inform energy planning can be found at the "Example Uses" link below.

Car Model

## Overview

Car Model is a dataset for classification tasks - it contains Cars annotations for 3,953 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 [MIT license](https://creativecommons.org/licenses/MIT).

Fuel economy data are the result of vehicle testing done at the Environmental Protection Agency's National Vehicle and Fuel Emissions Laboratory in Ann Arbor, Michigan, and by vehicle manufacturers with oversight by EPA.In 2016, the Department of Justice alleged violations of the Clean Air Act by Volkswagen (including Audi and Porsche) covering all of Volkswagen's 2.0L and 3.0L diesel vehicles sold in the United States since model year 2009. All relevant data from the affected vehicles have been removed from this website until there is an EPA-approved emissions.EPA has issued a Notice of Violation to Fiat Chrysler Automobiles N.V. and FCA US LLC for Model Year 2014-2016 light-duty diesel vehicles (Ram 1500 and Jeep Grand Cherokee). All relevant data from the affected vehicles has been removed from this website until further information is available.

The NHTSA Vehicle Crash Test Database contains engineering data measured during various types of research, the New Car Assessment Program (NCAP), and compliance crash tests. Information in this database refers to the performance and response of vehicles and other structures in impacts. This database is not intended to support general consumer safety issues. For general consumer information please see the NHTSA's information on buying a safer car.

Alesco Data's Automotive records are updated monthly from millions of proprietary sourced vehicle transactions. These incoming transactions are processed through compilation rules and are either added as new, incremental records to our file, or contribute to validating existing records.

Our recent focus is on compiling new vehicle ownership, and the file includes over 14.2 million late model vehicle owners (2020-2025).

We also append our Persistent ID, telephone numbers, and demographics for a complete file that can support your direct mail and email marketing campaigns, lead validation, and identity verification needs. A Persistent ID is assigned to each vehicle record and tracks consumers as they change addresses or phone numbers, and vehicles as they change owners.

The database is not derived from state motor vehicle databases and therefore not subject to the Shelby Act also known as the Driver's Privacy Protection Act (DPPA) of 2000. The data is deterministic and sources include sales and service data, warranty data and notifications, aftermarket repair and maintenance facilities, and scheduled maintenance records.

Fields Included: Make Model Year VIN Data Vehicle Class Code (crossover, SUV, full-size, mid-size, small) Vehicle Fuel Code (gas, flex, hybrid) Vehicle Style Code (sport, pickup, utility, sedan) Mileage Number of Vehicles per Household First seen date Last seen date Email

Model Cars

## Overview

Model Cars is a dataset for object detection tasks - it contains Cars annotations for 200 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).

The Synset Boulevard dataset contains a total of 259,200 synthetically generated images of cars from a frontal traffic camera perspective, annotated by vehicle makes, models and years of construction for machine learning methods (ML) in the scope (task) of vehicle make and model recognition (VMMR).

The data set contains 162 vehicle models from 43 brands with 200 images each, as well as 8 sub-data sets each to be able to investigate different imaging qualities. In addition to the classification annotations, the data set also contains label images for semantic segmentation, as well as information on image and scene properties, as well as vehicle color.

The dataset was presented in May 2024 by Anne Sielemann, Stefan Wolf, Masoud Roschani, Jens Ziehn and Jürgen Beyerer in the publication: Sielemann, A., Wolf, S., Roschani, M., Ziehn, J. and Beyerer, J. (2024). Synset Boulevard: A Synthetic Image Dataset for VMMR. In 2024 IEEE International Conference on Robotics and Automation (ICRA).

The model information is based on information from the ADAC online database (www.adac.de/rund-ums-fahrzeug/autokatalog/marken-modelle).

The data was generated using the simulation environment OCTANE (www.octane.org), which uses the Cycles ray tracer of the Blender project.

The dataset's website provides detailed information on the generation process and model assumptions. The dataset is therefore also intended to be used for the suitability analysis of simulated, synthetic datasets.

The data set was developed as part of the Fraunhofer PREPARE program in the "ML4Safety" project with the funding code PREPARE 40-02702, as well as funded by the "Invest BW" funding program of the Ministry of Economic Affairs, Labour and Tourism as part of the "FeinSyn" research project.

These datasets provide vehicle counts broken down by ZIP code, model year, fuel type, make and duty (light/heavy) of registered vehicles with specific as of dates.

Gain valuable insights into the automotive market with our comprehensive Car Prices Dataset. Designed for businesses, analysts, and researchers, this dataset provides real-time and historical car pricing data to support market analysis, pricing strategies, and trend forecasting.

Dataset Features

Vehicle Listings: Access detailed car listings, including make, model, year, trim, and specifications. Ideal for tracking market trends and pricing fluctuations. Pricing Data: Get real-time and historical car prices from multiple sources, including dealerships, marketplaces, and private sellers. Market Trends & Valuations: Analyze price changes over time, compare vehicle depreciation rates, and identify emerging pricing trends. Dealer & Seller Information: Extract seller details, including dealership names, locations, and contact information for lead generation and competitive analysis.

Customizable Subsets for Specific Needs Our Car Prices Dataset is fully customizable, allowing you to filter data based on vehicle type, location, price range, and other key attributes. Whether you need a broad dataset for market research or a focused subset for competitive analysis, we tailor the dataset to your needs.

Popular Use Cases

Market Analysis & Pricing Strategy: Track vehicle price trends, compare competitor pricing, and optimize pricing strategies for dealerships and resellers. Automotive Valuation & Depreciation Studies: Analyze historical pricing data to assess vehicle depreciation rates and predict future values. Competitive Intelligence: Monitor competitor pricing, dealership inventory, and promotional offers to stay ahead in the market. Lead Generation & Sales Optimization: Identify potential buyers and sellers, track demand for specific vehicle models, and enhance sales strategies. AI & Predictive Analytics: Leverage structured car pricing data for AI-driven forecasting, automated pricing models, and trend prediction.

Whether you're tracking car prices, analyzing market trends, or optimizing sales strategies, our Car Prices Dataset provides the structured data you need. Get started today and customize your dataset to fit your business objectives.

Automobile: Sales: ytd: PC: MPV: by Brand: FAW Group data was reported at 1,684.000 Unit in Mar 2025. This records an increase from the previous number of 1,199.000 Unit for Feb 2025. Automobile: Sales: ytd: PC: MPV: by Brand: FAW Group data is updated monthly, averaging 4,474.000 Unit from Jan 2015 (Median) to Mar 2025, with 67 observations. The data reached an all-time high of 14,159.000 Unit in Dec 2015 and a record low of 0.000 Unit in Dec 2017. Automobile: Sales: ytd: PC: MPV: by Brand: FAW Group data remains active status in CEIC and is reported by China Association of Automobile Manufacturers. The data is categorized under China Premium Database’s Automobile Sector – Table CN.RAB: Automobile Sales: Year-to-date: Passenger Car: MPV by Brand Models.

The data set contains registered vehicle population count by various criteria such as vehicle class, vehicle status, vechicle make, vehicle model, vehicle year, plate class, plate declaration, county, weight related class and other vehicle decriptors.

The data describe freeway car-following behavior (such as velocity, acceleration, and relative position) for the car-following instances observed during 6 data collection runs, collected using an Instrumented Research Vehicle (IRV) along freeways and arterials in western Massachusetts in the summer of 2016 to better understand work zone driver behaviors. The USDOT Volpe National Transportation Systems Center (Volpe Center) identified, isolated, and classified individual car following instances from within the raw datasets (classification parameters included roadway type, level of congestion, and speed limit), then processed, refined, and cleaned the dataset. This table contains metadata about each data collection run. See also the instances table (https://datahub.transportation.gov/Automobiles/Enhancing-Microsimulation-Models-for-Improved-Work/74ug-57tr) and radar table (https://datahub.transportation.gov/Automobiles/Enhancing-Microsimulation-Models-for-Improved-Work/4qbx-egtn).

Application and use cases

1 )Market Analysis: Evaluate overall trends and regional variations in car sales to assess manufacturer performance, model preferences, and demographic insights. 2) Seasonal Patterns and Competitor Analysis: Investigate seasonal and cyclical patterns in sales. 3) Forecasting and Predictive Analysis Use historical data for forecasting and predict future market trends. Support marketing, advertising, and investment decisions based on insights. 4) Supply Chain and Inventory Optimization: Provide valuable data for stakeholders in the automotive industry.

AI Data center to the Edge INTEL AI course

In this project, using Inception v3 model USA's most stolen cars was analysed and modeled t to predict the most stolen car.

Inception v3

The main hallmark of this architecture is the improved utilization of the computing resources inside the network. This was achieved by a carefully crafted design that allows for increasing the depth and width of the network while keeping the computational budget constant. To optimize quality, the architectural decisions were based on the Hebbian principle and the intuition of multi-scale processing. One particular incarnation used in our submission for ILSVRC 2014 is called GoogLeNet, a 22 layers deep network, the quality of which is assessed in the context of classification and detection.

The necessary python packages to install is given in environment.yml file

environment.yml

DATASET

This is an overview of the VMMR dataset introduced in "A Large and Diverse Dataset for Improved Vehicle Make and Model Recognition".

Overview Despite the ongoing research and practical interests, car make and model analysis only attracts few attentions in the computer vision community. We believe the lack of high quality datasets greatly limits the exploration of the community in this domain. To this end, we collected and organized a large-scale and comprehensive image database called VMMRdb, where each image is labeled with the corresponding make, model and production year of the vehicle.

Description The Vehicle Make and Model Recognition dataset (VMMRdb) is large in scale and diversity, containing 9,170 classes consisting of 291,752 images, covering models manufactured between 1950 and 2016. VMMRdb dataset contains images that were taken by different users, different imaging devices, and multiple view angles, ensuring a wide range of variations to account for various scenarios that could be encountered in a real-life scenario. The cars are not well aligned, and some images contain irrelevant background. The data covers vehicles from 712 areas covering all 412 sub-domains corresponding to US metro areas. Our dataset can be used as a baseline for training a robust model in several real-life scenarios for traffic surveillance.

VMMRdb data distribution

The distribution of images in different classes of the dataset. Each circle is associated with a class, and its size represents the number of images in the class. The classes with labels are the ones including more than 100 images.

Citation If you use this dataset, please cite the following paper:

A Large and Diverse Dataset for Improved Vehicle Make and Model Recognition F. Tafazzoli, K. Nishiyama and H. Frigui In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR) Workshops 2017.

Dataset

This dataset was created by adham elkomy

Released under Database: Open Database, Contents: © Original Authors

Contents

1) Data Introduction • The Tesla Car Models Dataset is a classification dataset built from images of Tesla’s flagship electric vehicles: Model S, Model 3, Model X, and Model Y. Each model has distinctive visual and functional characteristics, making this dataset suitable for EV model classification, brand recognition, and computer vision-based vehicle identification model training.

2) Data Utilization (1) Characteristics of the Tesla Car Models Dataset: • The dataset consists of images categorized by Tesla’s four major models, reflecting visually distinguishable features such as exterior design, proportions, and color variations. • It includes images taken in both indoor and outdoor environments, making it well-suited for domain generalization evaluation and practical model training.

(2) Applications of the Tesla Car Models Dataset: • Vehicle model classification: Can be used to train deep learning models that classify Tesla vehicles into Model S, 3, X, or Y based on visual input. • Electric vehicle brand recognition research: Useful for developing automated systems that identify Tesla vehicles by learning their unique design features.