Dataset Card for CIFAR-10

Table of Contents

Dataset Description

Dataset Summary

The CIFAR-10 dataset consists of 60000 32x32 colour images in 10 classes, with 6000 images per class. There are 50000 training images and 10000 test images. The dataset is divided into five training batches and one test batch, each with 10000 images. The test batch contains exactly 1000 randomly-selected images from each class. The training batches contain the remaining images in random order, but some training batches may contain more images from one class than another. Between them, the training batches contain exactly 5000 images from each class.

Supported Tasks and Leaderboards

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Languages

English

Dataset Structure

Data Instances

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Data Fields

  • img: 32x32x3 image
  • label: 0-9 with the following correspondence
        0 airplane 
        1 automobile 
        2 bird 
        3 cat 
        4 deer
        5 dog
        6 frog
        7 horse
        8 ship
        9 truck

Data Splits

Train and Test

Dataset Creation

Curation Rationale

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Source Data

Initial Data Collection and Normalization

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Who are the source language producers?

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Annotations

Annotation process

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Who are the annotators?

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Personal and Sensitive Information

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Considerations for Using the Data

Social Impact of Dataset

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Discussion of Biases

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Other Known Limitations

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Additional Information

Dataset Curators

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Licensing Information

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Citation Information

@TECHREPORT{Krizhevsky09learningmultiple,
    author = {Alex Krizhevsky},
    title = {Learning multiple layers of features from tiny images},
    institution = {},
    year = {2009}
}

Models trained or fine-tuned on cifar10

None yet