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 Description
- Homepage: https://google.github.io/cartoonset/
- Repository: https://github.com/google/cartoonset/
- 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 ~10^13 possible combinations. We provide sets of 10k and 100k randomly chosen cartoons and labeled attributes.
Usage
cartoonset
provides the images as PNG byte strings, this gives you a bit more flexibility into how to load the data. Here we show 2 ways:
Using PIL:
import datasets
from io import BytesIO
from PIL import Image
ds = datasets.load_dataset("cgarciae/cartoonset", "10k") # or "100k"
def process_fn(sample):
img = Image.open(BytesIO(sample["img_bytes"]))
...
return {"img": img}
ds = ds.map(process_fn, remove_columns=["img_bytes"])
Using TensorFlow:
import datasets
import tensorflow as tf
hfds = datasets.load_dataset("cgarciae/cartoonset", "10k") # or "100k"
ds = tf.data.Dataset.from_generator(
lambda: hfds,
output_signature={
"img_bytes": tf.TensorSpec(shape=(), dtype=tf.string),
},
)
def process_fn(sample):
img = tf.image.decode_png(sample["img_bytes"], channels=3)
...
return {"img": img}
ds = ds.map(process_fn)
Dataset Structure
Data Instances
A sample from the training set is provided below:
{
'img_bytes': b'0x...',
}
Data Fields
img_bytes
: A byte string containing the raw data of a 500x500 PNG image.
Data Splits
Train
Dataset Creation
Licensing Information
This data is licensed by Google LLC under a Creative Commons Attribution 4.0 International License.
Citation Information
@article{DBLP:journals/corr/abs-1711-05139,
author = {Amelie Royer and
Konstantinos Bousmalis and
Stephan Gouws and
Fred Bertsch and
Inbar Mosseri and
Forrester Cole and
Kevin Murphy},
title = {{XGAN:} Unsupervised Image-to-Image Translation for many-to-many Mappings},
journal = {CoRR},
volume = {abs/1711.05139},
year = {2017},
url = {http://arxiv.org/abs/1711.05139},
eprinttype = {arXiv},
eprint = {1711.05139},
timestamp = {Mon, 13 Aug 2018 16:47:38 +0200},
biburl = {https://dblp.org/rec/journals/corr/abs-1711-05139.bib},
bibsource = {dblp computer science bibliography, https://dblp.org}
}