Datasets:
cifar10

Task Categories: image-classification
Languages: English
Multilinguality: monolingual
Size Categories: 10K<n<100K
Language Creators: found
Annotations Creators: crowdsourced
Licenses: unknown
Dataset Preview
Go to dataset viewer
img (image)label (class label)
0 (airplane)
6 (frog)
0 (airplane)
2 (bird)
7 (horse)
2 (bird)
1 (automobile)
2 (bird)
4 (deer)
1 (automobile)
5 (dog)
6 (frog)
6 (frog)
3 (cat)
1 (automobile)
3 (cat)
5 (dog)
5 (dog)
8 (ship)
1 (automobile)
4 (deer)
2 (bird)
3 (cat)
2 (bird)
1 (automobile)
2 (bird)
8 (ship)
9 (truck)
5 (dog)
0 (airplane)
7 (horse)
6 (frog)
7 (horse)
6 (frog)
8 (ship)
8 (ship)
7 (horse)
4 (deer)
9 (truck)
1 (automobile)
2 (bird)
6 (frog)
5 (dog)
9 (truck)
4 (deer)
2 (bird)
5 (dog)
1 (automobile)
0 (airplane)
1 (automobile)
1 (automobile)
9 (truck)
0 (airplane)
7 (horse)
5 (dog)
3 (cat)
9 (truck)
6 (frog)
3 (cat)
3 (cat)
3 (cat)
4 (deer)
1 (automobile)
5 (dog)
9 (truck)
7 (horse)
7 (horse)
2 (bird)
9 (truck)
0 (airplane)
2 (bird)
8 (ship)
5 (dog)
9 (truck)
6 (frog)
7 (horse)
8 (ship)
4 (deer)
0 (airplane)
4 (deer)
9 (truck)
2 (bird)
7 (horse)
1 (automobile)
0 (airplane)
5 (dog)
1 (automobile)
8 (ship)
1 (automobile)
6 (frog)
5 (dog)
9 (truck)
7 (horse)
0 (airplane)
4 (deer)
5 (dog)
2 (bird)
3 (cat)
5 (dog)
5 (dog)
End of preview (truncated to 100 rows)

Dataset Card for CIFAR-10

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

  • image-classification: The goal of this task is to classify a given image into one of 10 classes. The leaderboard is available here.

Languages

English

Dataset Structure

Data Instances

A sample from the training set is provided below:

{
  'img': <PIL.PngImagePlugin.PngImageFile image mode=RGB size=32x32 at 0x201FA6EE748>,
  'label': 0
}

Data Fields

  • img: A PIL.Image.Image object containing the 32x32 image. Note that when accessing the image column: dataset[0]["image"] the image file is automatically decoded. Decoding of a large number of image files might take a significant amount of time. Thus it is important to first query the sample index before the "image" column, i.e. dataset[0]["image"] should always be preferred over dataset["image"][0]
  • 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

[More Information Needed]

Source Data

Initial Data Collection and Normalization

[More Information Needed]

Who are the source language producers?

[More Information Needed]

Annotations

Annotation process

[More Information Needed]

Who are the annotators?

[More Information Needed]

Personal and Sensitive Information

[More Information Needed]

Considerations for Using the Data

Social Impact of Dataset

[More Information Needed]

Discussion of Biases

[More Information Needed]

Other Known Limitations

[More Information Needed]

Additional Information

Dataset Curators

[More Information Needed]

Licensing Information

[More Information Needed]

Citation Information

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

Contributions

Thanks to @czabo for adding this dataset.

Models trained or fine-tuned on cifar10