Stanford Cars Dataset

Stanford Cars Dataset


Stanford Cars Dataset


Stanford Cars Dataset

Use Case

Computer Vision


3D object representations are valuable resources for multi-view object class detection and scene understanding.

Stanford Cars Dataset

About Dataset


Representations of 3D objects are helpful for recognizing objects from different views and understanding scenes better. Fine-grained recognition, a part of computer vision, focuses on spotting small differences in appearances. This dataset about cars is excellent for training and testing models that can distinguish between different types of cars. The data comes from Stanford University AI Lab (more details in the Acknowledgment section).


The Cars dataset has 16,185 pictures of 196 types of cars. These pictures are divided into two groups: 8,144 are used for training models, and 8,041 are for testing them. Each type of car, like a 2012 Tesla Model S or a 2012 BMW M3 coupe, is split roughly equally between training and testing sets.


Data source and banner image: 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.


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

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