This dataset consists of various types of cars. The dataset is organized into 2 folders (train, test) and contains subfolders for each car category. There are 4,165 images (JPG) and 7 classes of cars.

Please give credit to this dataset if you download it.

The Stanford Cars dataset consists of 196 classes of cars with a total of 16,185 images, taken from the rear. The data is divided into almost a 50-50 train/test split with 8,144 training images and 8,041 testing images. Categories are typically at the level of Make, Model, Year. The images are 360×240.

Cars Detecting And How Many

## Overview

Cars Detecting And How Many is a dataset for object detection tasks - it contains Cars annotations for 3,496 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).

Stanford Cars Dataset

  Dataset Overview

Splits: Training: 8144 images used for model training. Test: 8041 images used for evaluation. Contrast: 8041 images with high contrast for robustness testing. Gaussian Noise: 8041 images corrupted by Gaussian noise for robustness testing. Impulse Noise: 8041 images corrupted by impulse noise for robustness testing. JPEG Compression: 8041 compressed images for robustness testing. Motion Blur: 8041 images with motion blur for… See the full description on the dataset page: https://huggingface.co/datasets/tanganke/stanford_cars.

Dataset

This dataset was created by Bijan Acharya

Released under Data files © Original Authors

Contents

Here are a few use cases for this project:

1. Law Enforcement/Security: The model will be beneficial for law enforcement agencies to track and identify stolen vehicles, suspicious vehicles, or vehicles involved in crimes based on plate numbers. It can also be used for parking violations or to monitor traffic congestions in real-time.

2. Parking Management: This model can be used in parking lots or garages to automatically read plate numbers and record the entry and exit of vehicles. It may also streamline the payment process by connecting the plate number with the owners' payment details.

3. Toll Collection: The model can be integrated into toll booth systems to automatically identify the vehicle type and plate number, facilitating automatic digital payment and reducing manual labor and waiting times.

4. Traffic Flow Management: Government or related transportation authorities could use this model to monitor road traffic, identify traffic patterns, and make data-driven decisions on road expansion or traffic light timings.

5. Shipping and Logistics: The model could be used in warehouses or shipping yards to identify, track, and manage trucks entering and exiting the premises, ensuring efficient logistical movement.

Context

3D object representations are valuable resources for multi-view object class detection and scene understanding. Fine-grained recognition is a growing subfield of computer vision that has many real-world applications on distinguishing subtle appearances differences. This cars dataset contains great training and testing sets for forming models that can tell cars from one another. Data originated from Stanford University AI Lab (specific reference below in Acknowledgment section).

Content

The Cars dataset contains 16,185 images of 196 classes of cars. The data is split into 8,144 training images and 8,041 testing images, where each class has been split roughly in a 50-50 split. Classes are typically at the level of Make, Model, Year, ex. 2012 Tesla Model S or 2012 BMW M3 coupe.

Acknowledgements

Data source and banner image: http://ai.stanford.edu/~jkrause/cars/car_dataset.html contains all bounding boxes and labels for both training and tests.

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

3D Object Representations for Fine-Grained Categorization

Jonathan Krause, Michael Stark, Jia Deng, Li Fei-Fei

4th IEEE Workshop on 3D Representation and Recognition, at ICCV 2013 (3dRR-13). Sydney, Australia. Dec. 8, 2013.

Inspiration

• Can you form a model that can tell the difference between cars by type or colour?
• Which cars are manufactured by Tesla vs BMW?

Overview

The original Udacity Self Driving Car Dataset is missing labels for thousands of pedestrians, bikers, cars, and traffic lights. This will result in poor model performance. When used in the context of self driving cars, this could even lead to human fatalities.

We re-labeled the dataset to correct errors and omissions. We have provided convenient downloads in many formats including VOC XML, COCO JSON, Tensorflow Object Detection TFRecords, and more.

Some examples of labels missing from the original dataset: https://i.imgur.com/A5J3qSt.jpg" alt="Examples of Missing Labels">

Stats

The dataset contains 97,942 labels across 11 classes and 15,000 images. There are 1,720 null examples (images with no labels).

All images are 1920x1200 (download size ~3.1 GB). We have also provided a version downsampled to 512x512 (download size ~580 MB) that is suitable for most common machine learning models (including YOLO v3, Mask R-CNN, SSD, and mobilenet).

Annotations have been hand-checked for accuracy by Roboflow.

https://i.imgur.com/bOFkueI.pnghttps://" alt="Class Balance">

Annotation Distribution: https://i.imgur.com/NwcrQKK.png" alt="Annotation Heatmap">

Use Cases

Udacity is building an open source self driving car! You might also try using this dataset to do person-detection and tracking.

Using this Dataset

Our updates to the dataset are released under the MIT License (the same license as the original annotations and images).

Note: the dataset contains many duplicated bounding boxes for the same subject which we have not corrected. You will probably want to filter them by taking the IOU for classes that are 100% overlapping or it could affect your model performance (expecially in stoplight detection which seems to suffer from an especially severe case of duplicated bounding boxes).

About Roboflow

Roboflow makes managing, preprocessing, augmenting, and versioning datasets for computer vision seamless.

Developers reduce 50% of their boilerplate code when using Roboflow's workflow, save training time, and increase model reproducibility. :fa-spacer:

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.

SpaceKnow uses satellite (SAR) data to capture activity in electric vehicles and automotive factories.

Data is updated daily, has an average lag of 4-6 days, and history back to 2017.

The insights provide you with level and change data that monitors the area which is covered with assembled light vehicles in square meters.

We offer 3 delivery options: CSV, API, and Insights Dashboard

Available companies Rivian (NASDAQ: RIVN) for employee parking, logistics, logistic centers, product distribution & product in the US. (See use-case write up on page 4) TESLA (NASDAQ: TSLA) indices for product, logistics & employee parking for Fremont, Nevada, Shanghai, Texas, Berlin, and Global level Lucid Motors (NASDAQ: LCID) for employee parking, logistics & product in US

Why get SpaceKnow's EV datasets?

Monitor the company’s business activity: Near-real-time insights into the business activities of Rivian allow users to better understand and anticipate the company’s performance.

Assess Risk: Use satellite activity data to assess the risks associated with investing in the company.

Types of Indices Available Continuous Feed Index (CFI) is a daily aggregation of the area of metallic objects in square meters. There are two types of CFI indices. The first one is CFI-R which gives you level data, so it shows how many square meters are covered by metallic objects (for example assembled cars). The second one is CFI-S which gives you change data, so it shows you how many square meters have changed within the locations between two consecutive satellite images.

How to interpret the data SpaceKnow indices can be compared with the related economic indicators or KPIs. If the economic indicator is in monthly terms, perform a 30-day rolling sum and pick the last day of the month to compare with the economic indicator. Each data point will reflect approximately the sum of the month. If the economic indicator is in quarterly terms, perform a 90-day rolling sum and pick the last day of the 90-day to compare with the economic indicator. Each data point will reflect approximately the sum of the quarter.

Product index This index monitors the area covered by manufactured cars. The larger the area covered by the assembled cars, the larger and faster the production of a particular facility. The index rises as production increases.

Product distribution index This index monitors the area covered by assembled cars that are ready for distribution. The index covers locations in the Rivian factory. The distribution is done via trucks and trains.

Employee parking index Like the previous index, this one indicates the area covered by cars, but those that belong to factory employees. This index is a good indicator of factory construction, closures, and capacity utilization. The index rises as more employees work in the factory.

Logistics index The index monitors the movement of materials supply trucks in particular car factories.

Logistics Centers index The index monitors the movement of supply trucks in warehouses.

Where the data comes from: SpaceKnow brings you information advantages by applying machine learning and AI algorithms to synthetic aperture radar and optical satellite imagery. The company’s infrastructure searches and downloads new imagery every day, and the computations of the data take place within less than 24 hours.

In contrast to traditional economic data, which are released in monthly and quarterly terms, SpaceKnow data is high-frequency and available daily. It is possible to observe the latest movements in the EV industry with just a 4-6 day lag, on average.

The EV data help you to estimate the performance of the EV sector and the business activity of the selected companies.

The backbone of SpaceKnow’s high-quality data is the locations from which data is extracted. All locations are thoroughly researched and validated by an in-house team of annotators and data analysts.

Each individual location is precisely defined so that the resulting data does not contain noise such as surrounding traffic or changing vegetation with the season.

We use radar imagery and our own algorithms, so the final indices are not devalued by weather conditions such as rain or heavy clouds.

→ Reach out to get a free trial

Use Case - Rivian:

SpaceKnow uses the quarterly production and delivery data of Rivian as a benchmark. Rivian targeted to produce 25,000 cars in 2022. To achieve this target, the company had to increase production by 45% by producing 10,683 cars in Q4. However the production was 10,020 and the target was slightly missed by reaching total production of 24,337 cars for FY22.

SpaceKnow indices help us to observe the company’s operations, and we are able to monitor if the company is set to meet its forecasts or not. We deliver five different indices for Rivian, and these indices observe logistic centers, employee parking lot, logistics, product, and prod...

Total Vehicle Sales in the United States decreased to 15.30 Million in June from 15.70 Million in May of 2025. This dataset provides the latest reported value for - United States Total Vehicle Sales - plus previous releases, historical high and low, short-term forecast and long-term prediction, economic calendar, survey consensus and news.

Context

With the rise in the variety of cars with differentiated capabilities and features such as model, production year, category, brand, fuel type, engine volume, mileage, cylinders, colour, airbags and many more, we are bringing a car price prediction challenge for all. We all aspire to own a car within budget with the best features available. To solve the price problem we have created a dataset of 19237 for the training dataset and 8245 for the test dataset.

Data Description

Train.csv - 19237 rows x 18 columns (Includes Price Columns as Target) - Attributes - ID - Price: price of the care(Target Column) - Levy - Manufacturer - Model - Prod. year - Category - Leather interior - Fuel type - Engine volume - Mileage - Cylinders - Gear box type - Drive wheels - Doors - Wheel - Color - Airbags Test.csv - 8245 rows x 17 columns

Billy's Vintage Cards

This is a .zip image dataset with .txt files autotagged on Civitai. I used this dataset to create my image LoRA model "Billy's Vintage Cars" All images were AI-Generated, which means I am NOT affiliated with any Company/Manufacturer/Brand, and the dataset should be used for research and historical reasons. Enjoy! Billy's Vintage Cars Dataset © 2025 by Robb-0 is licensed under CC BY 4.0

The Comprehensive Cars (CompCars) dataset contains data from two scenarios, including images from web-nature and surveillance-nature. The web-nature data contains 163 car makes with 1,716 car models. There are a total of 136,726 images capturing the entire cars and 27,618 images capturing the car parts. The full car images are labeled with bounding boxes and viewpoints. Each car model is labeled with five attributes, including maximum speed, displacement, number of doors, number of seats, and type of car. The surveillance-nature data contains 50,000 car images captured in the front view.

The dataset can be used for the tasks of:

Fine-grained classification Attribute prediction Car model verification

The dataset can be also used for other tasks such as image ranking, multi-task learning, and 3D reconstruction.

Here are a few use cases for this project:

1. Vehicle Detection and Tracking Systems: The model could be used in traffic control systems to track the movement of cars on roads. It could identify the number and type of cars on certain routes at different times, useful for traffic flow analysis and planning.

2. Automated Parking Management: The "Cars" model could be integrated into an automated parking management system. It would identify and classify vehicles as they enter and leave, enhancing car park efficiency and security by tracking license plates(placa).

3. Augmented Reality Gaming: Use the model in AR gaming apps, where players can interact with real-world cars detected in the game. The model could identify cars and corresponding license plates that then become elements in an AR environment.

4. Security and Surveillance: In a public or private security context, the model could monitor surveillance footage to identify particular cars or license plates, alerting authorities of any suspicious or unauthorized vehicles present.

5. Autonomous Vehicle Systems: The model could be used in the development and enhancement of autonomous vehicle systems. Identifying nearby cars and their license plates could play a key role in autonomous decision-making processes, improving road safety.

This dataset shows the number of vehicles that have passed under a gantry on that particular day. This dataset does not show trips, it only shows segments. Segments are compiled to make trips. There are 10 gantries on the InterCounty Connector (ICC) and 5 interchanges. The eastbound gantries are 101, 105, 107, 109, 113, and the westbound gantries are 102, 106, 108, 110, 114. The dataset has a column for each gantry going east and west, then a total for each gantry. The ICC is an all electronic tolling road which opened February 2011. The first opening was a partial opening, with only the first interchange being available for use.There was a free period from February 23, 2011 through March 6, 2011. The rest of the ICC opened in November 2011, and there was another free period from November 22, 2011 through December 4, 2011. There are a few days where a low number of traffic passed under gantries (rows 196,198, 269,271...), these were either testing periods or construction vehicles.

We collected and annotated a dataset containing 105,544 annotated vehicle instances from 24700 image frames within seven different videos, sourced online under creative commons license. The video frames are annotated using DarkLabel tool. In the interest of reusability and generalisation of the deep learning model, we consider the diversity within the collected dataset. This diversity includes changes of lighting amongst the video, as well as other factors such as weather conditions, angle of observation, varying speed of the moving vehicles, traffic flow, and road conditions etc. The videos collected obviously include stationary vehicles, to perform the validation of stopped vehicle detection method. It can be noticed that the road conditions (e.g., motorways, city, country roads), directions, data capture timings and camera views, vary in the dataset producing annotated dataset with diversity. the dataset may have several uses such as vehicle detection, vehicle identification, stopped vehicle detection on smart motorways and local roads (smart city applications) and many more.

Here are a few use cases for this project:

1. Vehicle Surveillance: The "Cars" model could be invaluable in surveillance systems to monitor and identify different car classes at certain locations such as parking lots, highways, or border crossings. This could be used for both security reasons and for traffic management.

2. Automated Toll Booths: It can be applied in automated toll booths to identify the class of incoming cars and apply specific charges based on that. It also helps streamline traffic flow and increase efficiency.

3. Vehicle Recognition in Traffic Monitoring: An application could include monitoring traffic patterns and congestion. The model could be used to identify the mix of car classes on the road at any given time, thus allowing road authorities to understand traffic patterns better.

4. Auto Maintenance and Repair Shops: Auto maintenance and repair shops can use the model to identify the class of cars coming to their shop, allowing for more efficient parts management and targeted service.

5. Automobile Sales and Marketing: Companies in the automobile industry can use this model to better understand the popularity and utilization of different car classes in various locations. This would help them in making informed decisions about product stocking, marketing strategies, and sales projections.

Context

contain cars images classify them

Content

From Microsoft Bing Image Search under license (modify , share and use for commercial purpose).

Acknowledgements

Thanks to Bing image microsoft

Inspiration

i have created this dataset after doing soo much effort please do create notebooks.

This dataset provides values for CAR PRODUCTION reported in several countries. The data includes current values, previous releases, historical highs and record lows, release frequency, reported unit and currency.