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---
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pretty_name: Cartoon Set
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size_categories:
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- 10K<n<100K
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task_categories:
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- image-classification
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license: cc-by-4.0
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---
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# Dataset Card for Cartoon Set
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## Table of Contents
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- [Dataset Card for Cartoon Set](#dataset-card-for-cartoon-set)
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- [Table of Contents](#table-of-contents)
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- [Dataset Description](#dataset-description)
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- [Dataset Summary](#dataset-summary)
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- [Usage](#usage)
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- [Dataset Structure](#dataset-structure)
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- [Data Instances](#data-instances)
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- [Data Fields](#data-fields)
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- [Data Splits](#data-splits)
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- [Dataset Creation](#dataset-creation)
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- [Licensing Information](#licensing-information)
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- [Citation Information](#citation-information)
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- [Contributions](#contributions)
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## Dataset Description
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- **Homepage:** https://google.github.io/cartoonset/
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- **Repository:** https://github.com/google/cartoonset/
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- **Paper:** XGAN: Unsupervised Image-to-Image Translation for Many-to-Many Mappings
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- **Leaderboard:**
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- **Point of Contact:**
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### Dataset Summary
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[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.
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#### Usage
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`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:
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**Using PIL:**
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```python
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import datasets
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from io import BytesIO
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from PIL import Image
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ds = datasets.load_dataset("cgarciae/cartoonset", "10k")
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def process_fn(sample):
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img = Image.open(BytesIO(sample["img_bytes"]))
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...
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return {"img": img}
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ds = ds.map(process_fn, remove_columns=["img_bytes"])
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```
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**Using TensorFlow:**
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```python
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import datasets
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import tensorflow as tf
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hfds = datasets.load_dataset("cgarciae/cartoonset", "10k") # or "100k"
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ds = tf.data.Dataset.from_generator(
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lambda: hfds,
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output_signature={
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"img_bytes": tf.TensorSpec(shape=(), dtype=tf.string),
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},
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)
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def process_fn(sample):
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img = tf.image.decode_png(sample["img_bytes"], channels=3)
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...
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return {"img": img}
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ds = ds.map(process_fn)
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```
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**Additional features:**
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You can also access the features that generated each sample e.g:
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```python
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ds = datasets.load_dataset("cgarciae/cartoonset", "10k+features") # or "100k+features"
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```
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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.
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## Dataset Structure
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### Data Instances
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A sample from the training set is provided below:
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```
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{
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'img_bytes': b'0x...',
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}
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```
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### Data Fields
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- `img_bytes`: A byte string containing the raw data of a 500x500 PNG image.
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If `+features` is appended to the dataset name, the following additional fields are also available:
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- `eye_angle`, `eye_angle_num_categories`
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- `eye_lashes`, `eye_lashes_num_categories`
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- `eye_lid`, `eye_lid_num_categories`
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- `chin_length`, `chin_length_num_categories`
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- `eyebrow_weight`, `eyebrow_weight_num_categories`
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- `eyebrow_shape`, `eyebrow_shape_num_categories`
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- `eyebrow_thickness`, `eyebrow_thickness_num_categories`
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- `face_shape`, `face_shape_num_categories`
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- `facial_hair`, `facial_hair_num_categories`
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- `facial_hair_num_categories`, `facial_hair_num_categories`
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- `hair`, `hair_num_categories`
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- `hair_num_categories`, `hair_num_categories`
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- `eye_color`, `eye_color_num_categories`
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- `face_color`, `face_color_num_categories`
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- `hair_color`, `hair_color_num_categories`
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- `glasses`, `glasses_num_categories`
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- `glasses_color`, `glasses_color_num_categories`
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- `eyes_slant`, `eye_slant_num_categories`
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- `eyebrow_width`, `eyebrow_width_num_categories`
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- `eye_eyebrow_distance`, `eye_eyebrow_distance_num_categories`
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-
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### Data Splits
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Train
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## Dataset Creation
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### Licensing Information
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This data is licensed by Google LLC under a Creative Commons Attribution 4.0 International License.
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### Citation Information
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```
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@article{DBLP:journals/corr/abs-1711-05139,
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author = {Amelie Royer and
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Konstantinos Bousmalis and
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Stephan Gouws and
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Fred Bertsch and
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Inbar Mosseri and
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Forrester Cole and
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Kevin Murphy},
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title = {{XGAN:} Unsupervised Image-to-Image Translation for many-to-many Mappings},
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journal = {CoRR},
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volume = {abs/1711.05139},
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year = {2017},
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url = {http://arxiv.org/abs/1711.05139},
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eprinttype = {arXiv},
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eprint = {1711.05139},
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timestamp = {Mon, 13 Aug 2018 16:47:38 +0200},
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biburl = {https://dblp.org/rec/journals/corr/abs-1711-05139.bib},
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bibsource = {dblp computer science bibliography, https://dblp.org}
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}
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```
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### Contributions
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