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---
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](#dataset-card-for-cartoon-set)
- [Table of Contents](#table-of-contents)
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Usage](#usage)
- [Dataset Structure](#dataset-structure)
- [Data Instances](#data-instances)
- [Data Fields](#data-fields)
- [Data Splits](#data-splits)
- [Dataset Creation](#dataset-creation)
- [Licensing Information](#licensing-information)
- [Citation Information](#citation-information)
- [Contributions](#contributions)
## 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](https://google.github.io/cartoonset/) 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:**
```python
import datasets
from io import BytesIO
from PIL import Image
ds = datasets.load_dataset("cgarciae/cartoonset", "10k")
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:**
```python
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:
```python
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](#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...',
}
```
### 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 fields are also available:
- `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}
}
```
### Contributions
|