Are you searching for a comprehensive car model database? Look no further—Cars.com offers an extensive database of car makes and models, featuring detailed information to meet a wide range of needs. This rich resource includes data on make, model, year, specifications, pricing, features, and much more.

Whether you're an automotive business, a market researcher, or a developer building innovative car-related applications, this data of cars is an invaluable asset for performing in-depth vehicle analysis and trend forecasting.

What’s Included in the Vehicles Data:

• Car Models Database: Gain detailed insights into manufacturers' various car models, from compact cars to luxury sedans.
• Year: Access manufacturing year data to analyze trends in new releases and vintage classics.
• Specifications: Delve into technical details like engine types, horsepower, fuel efficiency, and transmission options.
• Pricing: Leverage current and historical pricing data to compare values and analyze market trends.
• Features: Explore safety features, entertainment systems, and comfort upgrades to evaluate vehicle appeal.
• Reviews and Ratings: Tap into customer and expert reviews to understand real-world vehicle performance and satisfaction.

This car datasets collection is regularly updated to provide the most accurate and reliable information. Whether you're developing an app, conducting market research, or simply staying informed about the latest trends, this car models database is your go-to resource for reliable vehicle data.

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The Comprehensive Vehicle Make and Model Dataset provides a detailed list of automotive manufacturers and their corresponding models. This dataset includes data on various car makes (manufacturers) and models (specific car names under each make), making it ideal for use in automotive research, machine learning projects, or data enrichment tasks related to the automotive industry.

Dataset Features: Make: The name of the car manufacturer (e.g., Toyota, Ford, BMW). Model: The specific car model associated with each manufacturer (e.g., Camry, F-150, X5).

This dataset is structured to be easily accessible for relational databases, making it suitable for building relational models where car makes are linked to their models. It is especially useful for tasks like recommendation systems, market analysis, trend analysis, or training machine learning models that require automotive industry data.

Use Cases: Recommendation Engines: Develop systems that recommend car models based on user preferences. Market Research: Analyze the popularity or trends in specific car makes and models. Data Enrichment: Enrich datasets with car make and model information for enhanced data quality.

Data Structure: Each entry in the dataset consists of: Make: Manufacturer name. Models: List of car models associated with that make.

About this Dataset

This list includes all makes, models, years, and trim specifications for the fifty one makes listed below. Includes makes that were released after the year 2000 up until now. Trim data includes unique specifications such as engine data, AWD/FWD/2WD, and additional technology packages just to name a few. Useful data for populating databases, programming that requires accurately identifying/defining a car, and many other purposes. Other websites would typically sell data like this for >100$ (go check for yourself!!). Car Makes Included: Acura, Alfa Romeo, Am General, Audi, BMW, Buick, Cadillac, Chevrolet, Chrysler, Daewoo, Dodge, FIAT, Fisker, Ford, GMC, Genesis, Honda, Hummer, Hyundai, INFINITI, Isuzu, Jaguar, Jeep, Kia, Land Rover, Lexus, Lincoln, Lotus, Maserati, Maybach, Mazda, Mercedes-Benz, Mercury, MINI, Mitsubishi, Nissan, Oldsmobile, Panoz, Plymouth, Pontiac, Porsche, RAM, Saab, Saturn, Scion, smart, Subaru, Suzuki, Toyota, Volkswagen, Volvo

Category

Product Lists

Keywords

cars,automotive,auto,vehicle

Row Count

45888

Price

$56.00

Vehicle Make, Model, and Color Database Market Outlook

According to our latest research, the global Vehicle Make, Model, and Color Database market size reached USD 2.1 billion in 2024. The market is expected to grow at a CAGR of 7.3% during the forecast period, reaching a value of USD 3.9 billion by 2033. This robust growth is driven by the increasing digitization of automotive data, the proliferation of connected vehicles, and the heightened demand for real-time vehicle information across multiple industries. As per our latest research, the integration of advanced analytics and artificial intelligence within vehicle databases is further accelerating market expansion, enabling more precise and actionable insights for end-users globally.

The primary growth factor for the Vehicle Make, Model, and Color Database market is the escalating need for accurate and comprehensive vehicle information across diverse sectors. Automotive dealerships, insurance companies, and law enforcement agencies are increasingly relying on these databases to streamline operations, enhance customer experience, and improve decision-making processes. The rise in vehicle thefts, fraudulent insurance claims, and the need for efficient fleet management solutions have all contributed to a surge in demand for reliable vehicle data. Furthermore, the growing trend toward digital transformation within the automotive industry has led to the adoption of sophisticated database solutions, which offer seamless integration with existing IT infrastructures and ensure data accuracy and security.

Another significant growth driver is the rapid advancement in data collection technologies and the expanding sources of vehicle-related data. The proliferation of IoT-enabled vehicles, telematics, and connected car platforms has resulted in an exponential increase in the volume and variety of vehicle data available for analysis. This has enabled database providers to offer more granular and up-to-date information, catering to the specific requirements of end-users such as automotive manufacturers, government agencies, and transportation companies. The integration of machine learning and big data analytics further enhances the value proposition of these databases, enabling predictive insights and real-time data validation that support critical business functions and regulatory compliance.

The market is also witnessing increased collaboration between original equipment manufacturers (OEMs), aftermarket players, and technology providers to standardize and enrich vehicle data. These partnerships are essential for ensuring data consistency, interoperability, and scalability across different platforms and geographies. The adoption of cloud-based database solutions has further democratized access to vehicle data, allowing small and medium enterprises (SMEs) to leverage sophisticated analytics without significant upfront investments. Additionally, regulatory initiatives aimed at improving road safety and vehicle traceability are fueling the demand for comprehensive and up-to-date vehicle databases, particularly in emerging markets where vehicle ownership is on the rise.

From a regional perspective, North America continues to dominate the Vehicle Make, Model, and Color Database market, accounting for the largest share in 2024. This is attributed to the region's mature automotive ecosystem, high vehicle penetration, and early adoption of advanced data management technologies. Europe follows closely, driven by stringent regulatory requirements and a strong focus on vehicle safety and compliance. The Asia Pacific region is poised for the fastest growth during the forecast period, supported by rapid urbanization, increasing vehicle sales, and significant investments in digital infrastructure. Latin America and the Middle East & Africa are also emerging as promising markets, with growing awareness of the benefits of robust vehicle data management systems and the expansion of automotive and transportation sectors.

Database Type Analysis</h2

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.

Vehicle Make Model and Color Database Market Outlook

According to our latest research, the global Vehicle Make Model and Color Database market size in 2024 is valued at approximately USD 1.78 billion. The market is poised for robust expansion, exhibiting a compound annual growth rate (CAGR) of 11.2% from 2025 to 2033. By the end of 2033, the market is projected to reach USD 4.73 billion. This growth is primarily driven by the rising need for advanced vehicle identification systems across diverse sectors, including automotive, insurance, and law enforcement, as organizations increasingly prioritize data-driven operations and regulatory compliance.

One of the primary growth factors fueling the Vehicle Make Model and Color Database market is the surge in digitization within the automotive sector. The proliferation of connected vehicles, IoT-enabled fleet management solutions, and the widespread adoption of smart city initiatives have led to a substantial increase in the volume and complexity of vehicular data. Automotive dealerships, insurance providers, and fleet management companies are leveraging these databases to streamline their operations, improve customer service, and enhance security protocols. The ability to quickly and accurately identify vehicles by make, model, and color is becoming indispensable for managing inventories, processing insurance claims, and maintaining regulatory compliance. This digitization trend is expected to intensify as more organizations recognize the value of comprehensive, real-time vehicle data.

Another significant driver is the escalating demand for robust vehicle identification systems by law enforcement agencies and governmental bodies. The rise in vehicle-related crimes, coupled with the need for efficient traffic management, has compelled authorities to invest in advanced database solutions. These databases enable law enforcement agencies to rapidly identify stolen or suspicious vehicles, support automated license plate recognition systems, and contribute to the overall safety and security of urban environments. Furthermore, the integration of artificial intelligence and machine learning algorithms into these databases enhances their accuracy and predictive capabilities, allowing for proactive threat detection and incident response. As public safety concerns continue to mount, the adoption of vehicle make, model, and color databases by the public sector is expected to grow steadily.

The expansion of the global automotive aftermarket also plays a pivotal role in the growth of the Vehicle Make Model and Color Database market. As the average vehicle lifespan increases and the demand for used vehicles rises, accurate and up-to-date vehicle information becomes crucial for dealerships, car rental services, and insurance companies. These organizations rely on comprehensive databases to verify vehicle histories, assess risk profiles, and optimize pricing strategies. Additionally, the increasing popularity of online vehicle marketplaces and digital sales platforms further amplifies the need for reliable and easily accessible vehicle data. This trend is likely to persist as consumers and businesses continue to favor digital channels for vehicle transactions and management.

Regionally, North America currently dominates the Vehicle Make Model and Color Database market, accounting for a significant share of global revenue in 2024. The region’s leadership is attributed to its advanced automotive ecosystem, high penetration of digital technologies, and strong presence of key market players. However, the Asia Pacific region is anticipated to witness the highest growth rate during the forecast period, driven by rapid urbanization, increasing vehicle ownership, and government initiatives aimed at modernizing transportation infrastructure. Europe also remains a critical market, benefiting from stringent regulatory standards and a mature automotive industry. Collectively, these regional dynamics underscore the global nature of the market and highlight the diverse opportunities for stakeholders across different geographies.

Database Type Analysis

The Vehicle Make Model and Color Database market is segmented by database type into structured, unstructured, and hybrid databases. Structured databases, which utilize a predefined schema and organized data models, remain the dominant segment due to their reliability, ease of integration, and compatibility with existing enter

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).

In addition, we append our Persistent ID, telephone numbers, and demographics for a complete file that can support your direct mail and email marketing, 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 -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

Dataset

This dataset was created by Rahul Raj

Released under Data files © Original Authors

Contents

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.

Problem Statement

The noise level of cars could be an indicator of both car’s condition and manufacturing quality. Drivers could use noise level to determine if a potential car suits their needs, or if their current car is in a healthy state. On the other hand, manufactures could use noise level to assess their cars' quality compared to the market. Luxury cars compete to have low noise level, while sports car usually neglect this factor. In this project we compile data from different sources to arrive to a dataset having cars’ manufacturing specification mapped to its noise level at different speed. The compiled dataset could be utilized in evaluating cars noise level or in analyzing which technical specification has the major effect on cars’ noise level.

For mor information about automobile noise level:

• An overview of automobile noise and vibration control

• Noise, vibration, and harshness From Wikipedia

Executive Summary

Initially we scrape data from https://www.auto-decibel-db.com (hereafter referred AD). This website has nearly 2000 data entries about cars' cabin noise level. Each car in the website has its cabin noise (measured in decibel) at different speed. The website doesn't provide further information about the source or the methodology of its collected data, yet it's the most comprehensive data about the subject I could found. Another source which might be used for verification can be found at https://www.edmunds.com. While edmunds.com states its methodology of collecting noise level, its dataset is embedded in PDF files and is not comprehensive compared to the former.

After scrapping the noise level of cars, we use the available information we have about each car to find its specification. In the scrapped dataset from AD there's 4 features which can be used to identify same car's specification in other datasets: brand, model, year, and spec. After looking up the Web for websites and APIs having detailed and comprehensive data about cars, we decided on http://www.carqueryapi.com API (hereafter referred CQA). Though it's not accurate for some cars, and it has different spelling from our AD, it's the most accessible data we could find. In this section we map each car in AD to its equivalence in cqa using the 3 features: brand, model, and year. We first specify the model_id in CQA and then we will use model_id to retrieve the full specification of the car. Due to the limitation imposed by caranddriver.com on the number of requests (60 requests), we used Tor bridge to alternate IP address.

Finally, we look up for the full specification of each car in CQA using its model_id. In this section we added 60 features of specification of nearly a 1000 car in AD. We refer to each feature pulled from CQA by a postfix added to its column name: '_cqa'. At the end we succeeded in getting specification of 1067 car out of 1895 in AD. We couldn’t find specification for all cars in AD due to either different naming of cars between AD and QC, or the car doesn’t exist in QC.

Web scrapping sources and API

auto-decibel-db.com: This website has nearly 2000 data entries about cars' cabin noise level. Each car in the website has its cabin noise (measured in decibel) at different speed.

carqueryapi.com: a JSON based API for retrieving detailed car and truck information, including year, make, model, trim, and specifications. It has 73419 vichle in its database.

Open questions to explore

What technical specification have the major effect on car noise level? What are the patterns observed on car noise level through years, brands, and specification? Can we use car noise level to indicate car's brand, year, specification?

Context

Automobile data holds immense importance as it offers insights into the functioning and efficiency of the automotive industry. It provides valuable information about car models, specifications, sales trends, consumer demographics, and preferences, which car manufacturers and dealerships can leverage to optimize their operations and enhance customer experiences. By analyzing data on vehicle reliability, fuel efficiency, safety ratings, and resale values, the automotive industry can identify trends and implement strategies to produce more reliable and environmentally friendly vehicles, improve safety standards, and enhance the overall value of cars for consumers. Moreover, regulatory bodies and policymakers rely on this data to enforce regulations, set emissions standards, and make informed decisions regarding automotive policies and environmental impacts. Researchers and analysts use car purchase data to study market trends, assess the environmental impact of various vehicle types, and develop strategies for sustainable growth within the industry. In essence, car purchase data serves as a foundation for informed decision-making, operational efficiency, and the overall advancement of the automotive sector.

Content

This dataset comprises diverse parameters relating to car purchases and ownership on a global scale. The dataset prominently incorporates fields such as 'First Name', 'Last Name', 'Country', 'Car Brand', 'Car Model', 'Car Color', 'Year of Manufacture', and 'Credit Card Type'. These columns collectively provide comprehensive insights into customer demographics, vehicle details, and payment information. Researchers and industry experts can leverage this dataset to analyze trends in car purchasing behavior, optimize the customer car-buying experience, evaluate the popularity of car brands and models, and understand payment preferences within the automotive industry.

Dataset Glossary (Column-wise)

• First Name - The first name of the car purchaser.
• Last Name - The last name of the car purchaser.
• Country - The country of residence of the car purchaser.
• Car Brand - The brand or manufacturer of the purchased car.
• Car Model - The specific model of the purchased car.
• Car Color - The color of the purchased car.
• Year of Manufacture - The year the car was manufactured.
• Credit Card Type - The type of credit card used for the car purchase.

Structure of the Dataset

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Acknowledgement

The dataset provided here is a simulated example and was generated using the online platform found at Mockaroo. This web-based tool offers a service that enables the creation of customizable mock datasets that closely resemble real data. It is primarily intended for use by developers, testers, and data experts who require sample data for a range of uses, including testing databases, filling applications with demonstration data, and crafting lifelike illustrations for presentations and tutorials. To explore further details, you can visit their website.

Cover Photo by: Freepik

Thumbnail by: Car icons created by Freepik - Flaticon

The fields available include make, model, year, trim, style, fuel type, MSRP, and many more.

We have developed this file to be tied to our Consumer Demographics Database so additional demographics can be applied as needed. Each record is ranked by confidence and only the highest quality data is used. This file contains over 180 million records in addition to over 1 million+ fresh automotive intender records per day.

Note - all Consumer packages can include necessary PII (address, email, phone, DOB, etc.) for merging, linking, and activation of the data.

BIGDBM Privacy Policy: https://bigdbm.com/privacy.html

Vehicle Product Information Catalog with factory specifications, build-plant data and technical details for vehicles manufactured since 1981.

Used Car Price Prediction Dataset is a comprehensive collection of automotive information extracted from the popular automotive marketplace website, https://www.cars.com. This dataset comprises 4,009 data points, each representing a unique vehicle listing, and includes nine distinct features providing valuable insights into the world of automobiles.

• Brand & Model: Identify the brand or company name along with the specific model of each vehicle.
• Model Year: Discover the manufacturing year of the vehicles, crucial for assessing depreciation and technology advancements.
• Mileage: Obtain the mileage of each vehicle, a key indicator of wear and tear and potential maintenance requirements.
• Fuel Type: Learn about the type of fuel the vehicles run on, whether it's gasoline, diesel, electric, or hybrid.
• Engine Type: Understand the engine specifications, shedding light on performance and efficiency.
• Transmission: Determine the transmission type, whether automatic, manual, or another variant.
• Exterior & Interior Colors: Explore the aesthetic aspects of the vehicles, including exterior and interior color options.
• Accident History: Discover whether a vehicle has a prior history of accidents or damage, crucial for informed decision-making.
• Clean Title: Evaluate the availability of a clean title, which can impact the vehicle's resale value and legal status.
• Price: Access the listed prices for each vehicle, aiding in price comparison and budgeting.

This dataset is a valuable resource for automotive enthusiasts, buyers, and researchers interested in analyzing trends, making informed purchasing decisions or conducting studies related to the automotive industry and consumer preferences. Whether you are a data analyst, car buyer, or researcher, this dataset offers a wealth of information to explore and analyze.

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.

(1) This is the dataset simulated by high resolution atmospheric model of which horizontal resolution is 60km-mesh over the globe (GCM), and 20km over Japan and surroundings (RCM), respetively. The climate of the latter half of the 20th century is simulated for 6000 years (3000 years for the Japan area), and the climates 1.5 K(*2), 2 K (*1) and 4 K warmer than the pre-industrial climate are simulated for 1566, 3240 and 5400 years, respectivley, to see the effect of global warming. (2) Huge number of ensembles enable not only with statistics but also with high accuracy to estimate the future change of extreme events such as typoons and localized torrential downpours. In addtion, this dataset provides the highly reliable information on the impact of natural disasters due to climate change on future societies. (3) This dataset provides the climate projections which adaptations against global warming are based on in various fields, for example, disaster prevention, urban planning, environmetal protection, and so on. It would realize the global warming adaptations consistent not only among issues but also among regions. (4) Total size of this dataset is 3 PB (3 × the 15th power of 10 bytes).

(*1) Datasets of the climates 2K warmer than the pre-industorial climate (d4PDF 2K) is available on 10th August, 2018. (*2) Datasets of the climates 1.5K warmer than the pre-industorial climate (d4PDF 1.5K) is available on 8th February, 2022.

Live-updated database of open safety recalls and technical service bulletins issued by OEMs and NHTSA.

About Dataset

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you are given dataset which contains information about automobiles. The dataset contains 399 rows of 9 features

DATA OVERVIEW:

The dataset consists of the following columns:

• Name: Unique identifier for each automobile.
• MPG: Fuel efficiency measured in miles per gallon.
• Cylinders: Number of cylinders in the engine.
• Displacement: Engine displacement, indicating its size or capacity.
• Horsepower: Power output of the engine.
• Weight: Weight of the automobile.
• Acceleration: Capability to increase speed, measured in seconds.
• Model Year: Year of manufacture for the automobile model.
• Origin: Country or region of origin for each automobile.