Stanford_Car Computer Vision Project
This dataset is a copy of a subset of the full Stanford Cars dataset
The original dataset contained 16,185 images of 196 classes of cars.
The classes are typically at the level of Make, Model, Year, e.g. 2012 Tesla Model S or 2012 BMW M3 coupe in the original dataset, and in this subset of the full dataset (v3
, TestData and v4
, original_raw-images).
v4
(original_raw-images) contains a generated version of the original, raw images, without any modified classes
v8
(classes-Modified_raw-images) contains a generated version of the raw images, with the Modify Classes preprocessing feature used to remap or omit the following classes:
bike
,moped
--remapped to-->motorbike
cng
,leguna
,easybike
,smart fortwo Convertible 2012
, and all other specific car makes with named classes (such asAcura TL Type-S 2008
) --remapped to-->vehicle
rickshaw
,boat
,bicycle
--> omitted
v9
(FAST-model_mergedAllClasses-augmented_by3x) contains a generated version of the raw images, with the Modify Classes preprocessing feature used to remap or omit the following classes:
bike
,moped
--remapped to-->motorbike
cng
,leguna
,easybike
,smart fortwo Convertible 2012
, and all other specific car makes with named classes (such asAcura TL Type-S 2008
) --remapped to-->vehicle
rickshaw
,boat
,bicycle
--> omitted
v10
(ACCURATE-model_mergedAllClasses-augmented_by3x) contains a generated version of the raw images, with the Modify Classes preprocessing feature used to remap or omit the following classes:
bike
,moped
--remapped to-->motorbike
cng
,leguna
,easybike
,smart fortwo Convertible 2012
, and all other specific car makes with named classes (such asAcura TL Type-S 2008
) --remapped to-->vehicle
rickshaw
,boat
,bicycle
--> omitted
Citation:
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. pdf BibTex slides
Trained Model API
This project has a trained model available that you can try in your browser and use to get predictions via our Hosted Inference API and other deployment methods.
Find utilities and guides to help you start using the Stanford_Car project in your project.