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.

The Stanford Cars Dataset contains 16,185 images of 196 types of cars, ideal for fine-grained object recognition and multi-view car classification.

A dataset of 16,185 images of cars, with each image labeled with the make, model, and year of the car. The dataset was created by researchers at Stanford University and is used for research in computer vision and machine learning.

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

This dataset is a copy of a subset of the full Stanford Cars dataset

• Stanford Cars Dataset, Stanford AI
• Stanford Cars Dataset, Papers with Code

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: 1. bike, moped --remapped to--> motorbike 2. cng, leguna, easybike, smart fortwo Convertible 2012, and all other specific car makes with named classes (such as Acura TL Type-S 2008) --remapped to--> vehicle 3. 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: 1. bike, moped --remapped to--> motorbike 2. cng, leguna, easybike, smart fortwo Convertible 2012, and all other specific car makes with named classes (such as Acura TL Type-S 2008) --remapped to--> vehicle 3. 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: 1. bike, moped --remapped to--> motorbike 2. cng, leguna, easybike, smart fortwo Convertible 2012, and all other specific car makes with named classes (such as Acura TL Type-S 2008) --remapped to--> vehicle 3. 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

jackjcoop/stanford-cars-ellipse dataset hosted on Hugging Face and contributed by the HF Datasets community

zededa/stanford-cars dataset hosted on Hugging Face and contributed by the HF Datasets community

Introduction

This is a version of the Stanford Cars dataset introduced in [1].

Usage

The train, val and test split are given in the respective json files in this GitHub repo, in this folder. Please refer to the README file of the repository for the folder structure.

These files can be used with any framework of choice.

References

[1] Jonathan Krause and Michael Stark and Jia Deng and Li Fei-Fei. 3D Object Representations for Fine-Grained Categorization. 4th International IEEE Workshop on 3D Representation and Recognition (3dRR-13) (2013)

Car Parts Stanford Cars

## Overview

Car Parts Stanford Cars is a dataset for object detection tasks - it contains Cars And Components annotations for 693 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).

Qualeafclover/webdataset-stanford-cars dataset hosted on Hugging Face and contributed by the HF Datasets community

Dataset

This dataset was created by --

Contents

Stanford Cars Yolov5

## Overview

Stanford Cars Yolov5 is a dataset for object detection tasks - it contains Cars annotations for 8,132 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).

roborovski/stanford-cars dataset hosted on Hugging Face and contributed by the HF Datasets community

Dataset Card for "stanford-cars"

More Information needed

The Stanford Car Images dataset contains about 8000 images each in train and test sets, spread over 196 classes.

It is intended for research purposes only. This present dataset is derived from the Stanford Car Images dataset on the following principles.

1. The train set is further split into training and validation, in a 2:1 ratio.
2. The training and validation images are augmented using albumentations. The amount of augmentation is calibrated so that in the final output, we have roughly equal number of cars per class.

The idea of having a fixed augmented set of images, rather than relying on keras on-the-fly augmentation, is to feed the model better balanced data. In addition, we are able to use keras augmentation on top of albementations augmentations, if desired.

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.

https://ai.stanford.edu/~jkrause/cars/car_dataset.html

Average test set classification accuracy on miniimageNet.

Average test set classification accuracy on Caltech-UCSD Birds.

Dataset

This dataset was created by Saurabh Sawhney

Contents

Stanford_Cars

## Overview

Stanford_Cars is a dataset for object detection tasks - it contains Class annotations for 3,272 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).

Average test set classification accuracy on different loss function.