metadata

pretty_name: Cartoon Set
size_categories:
  - 10K<n<100K
task_categories:
  - image
  - computer-vision
  - generative-modelling
license: cc-by-4.0

Dataset Card for Cartoon Set

Dataset Card for Cartoon Set

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)

Additional features: You can also access the features that generated each sample e.g:

ds = datasets.load_dataset("cgarciae/cartoonset", "10k+features") # or "100k+features"

Apart from img_bytes these configurations add a total of 18 * 2 additional int features, these come in {feature}, {feature}_num_categories pairs where num_categories indicates the number of categories for that feature. See Data Fields for the complete list of features.

Dataset Structure

Data Instances

A sample from the training set is provided below:

{
  'img_bytes': b'0x...',
}

If +features is added to the dataset name, the following additional fields are provided:

{
  'img_bytes': b'0x...',
  'eye_angle': 0,
  'eye_angle_num_categories': 3,
  'eye_lashes': 0,
  'eye_lashes_num_categories': 2,
  'eye_lid': 0,
  'eye_lid_num_categories': 2,
  'chin_length': 2,
  'chin_length_num_categories': 3,
  ...
}

Data Fields

img_bytes: A byte string containing the raw data of a 500x500 PNG image.

If +features is appended to the dataset name, the following additional int32 fields are provided:

eye_angle
eye_angle_num_categories
eye_lashes
eye_lashes_num_categories
eye_lid
eye_lid_num_categories
chin_length
chin_length_num_categories
eyebrow_weight
eyebrow_weight_num_categories
eyebrow_shape
eyebrow_shape_num_categories
eyebrow_thickness
eyebrow_thickness_num_categories
face_shape
face_shape_num_categories
facial_hair
facial_hair_num_categories
facial_hair_num_categories
facial_hair_num_categories
hair
hair_num_categories
hair_num_categories
hair_num_categories
eye_color
eye_color_num_categories
face_color
face_color_num_categories
hair_color
hair_color_num_categories
glasses
glasses_num_categories
glasses_color
glasses_color_num_categories
eyes_slant
eye_slant_num_categories
eyebrow_width
eyebrow_width_num_categories
eye_eyebrow_distance
eye_eyebrow_distance_num_categories

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}
}

Datasets:

cgarciae
/

cartoonset