README.md

metadata

pretty_name: Cartoon Set
size_categories:
  - 10K<n<100K
task_categories:
  - image-classification
license: cc-by-4.0

Dataset Card for Cartoon Set

Table of Contents

• Dataset Card for Cartoon Set
  • Table of Contents
  • Dataset Description
    • Dataset Summary
    • Supported Tasks and Leaderboards
    • Languages
  • Dataset Structure
    • Data Instances
    • Data Fields
    • Data Splits
  • Dataset Creation
    • Curation Rationale
    • Source Data
      • Initial Data Collection and Normalization
      • Who are the source language producers?
    • Annotations
      • Annotation process
      • Who are the annotators?
    • Personal and Sensitive Information
  • Considerations for Using the Data
    • Social Impact of Dataset
    • Discussion of Biases
    • Other Known Limitations
  • Additional Information
    • Dataset Curators
    • Licensing Information
    • Citation Information
    • Contributions

Dataset Description

• Homepage: https://google.github.io/cartoonset/
• Repository:
• Paper: XGAN: Unsupervised Image-to-Image Translation for Many-to-Many Mappings
• Leaderboard:
• Point of Contact:

Dataset Summary

Cartoon Set is a collection of random, 2D cartoon avatar images. The cartoons vary in 10 artwork categories, 4 color categories, and 4 proportion categories, with a total of ~1013 possible combinations. We provide sets of 10k and 100k randomly chosen cartoons and labeled attributes.

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](https://paperswithcode.com/sota/image-classification-on-Cartoon Set).

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.