|
--- |
|
license: |
|
- cc-by-4.0 |
|
size_categories: |
|
- 10K<n<100K |
|
task_categories: |
|
- other |
|
tags: |
|
- image |
|
- computer-vision |
|
- generative-modeling |
|
pretty_name: Cartoon Set |
|
--- |
|
|
|
# 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 sample image](https://huggingface.co/datasets/cgarciae/cartoonset/resolve/main/sample.png) |
|
|
|
[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") # 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:** |
|
```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: |
|
```python |
|
{ |
|
'img_bytes': b'0x...', |
|
} |
|
``` |
|
If `+features` is added to the dataset name, the following additional fields are provided: |
|
|
|
```python |
|
{ |
|
'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} |
|
} |
|
``` |
|
### Contributions |
|
|