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Datasets:
cyberagent
/
crello

Tasks:
Unconditional Image Generation
Modalities:
Image
Text
Time-series
Formats:
parquet
Languages:
English
Size:
10K - 100K
ArXiv:
Tags:
graphic design
design templates
Libraries:
Datasets
Dask
License:
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metadata
annotations_creators:
  - no-annotation
language:
  - en
language_creators:
  - found
license:
  - other
multilinguality:
  - monolingual
pretty_name: crello
size_categories:
  - 1K<n<10K
source_datasets:
  - original
tags:
  - graphic design
  - design templates
task_categories:
  - unconditional-image-generation
task_ids: []

Dataset Card for Crello

Table of Contents

Dataset Description

Dataset Summary

The Crello dataset is compiled for the study of vector graphic documents. The dataset contains document meta-data such as canvas size and pre-rendered elements such as images or text boxes. The original templates was collected from crello.com (now create.vista.com) and converted to low-resolution format suitable for machine learning analysis.

Supported Tasks and Leaderboards

CanvasVAE studies unsupervised document generation.

Languages

Almost all design templates use English.

Dataset Structure

Data Instances

Each instance has scalar attributes (canvas) and sequence attributes (elements). Categorical values are stored as integer values. Check ClassLabel features of the dataset for the list of categorical labels.

{'id': '5bd1c85078e1194aa6186300',
 'length': 11,
 'group': 4,
 'format': 10,
 'canvas_width': 40,
 'canvas_height': 41,
 'category': 1,
 'type': [3, 3, 3, 3, 3, 3, 4, 1, 4, 4, 3],
 'left': [0.8712766170501709,
  0.42127659916877747,
  0.14468085765838623,
  0.8585106134414673,
  0.8819149136543274,
  0.043617021292448044,
  0.30696335434913635,
  0.0010638297535479069,
  0.7409376502037048,
  0.4813956916332245,
  0.30744680762290955],
 'top': [0.0,
  0.0,
  0.874365508556366,
  0.6967005133628845,
  0.6967005133628845,
  0.9060913920402527,
  0.8984156250953674,
  0.113832488656044,
  0.434346079826355,
  0.14983975887298584,
  0.05710659921169281],
 'width': [0.12989361584186554,
  0.2329787164926529,
  0.42127659916877747,
  0.0010638297535479069,
  0.0010638297535479069,
  0.20425531268119812,
  0.2213829755783081,
  0.5641489624977112,
  0.2148936241865158,
  0.2951063811779022,
  0.6446808576583862],
 'height': [1.0,
  0.5164974331855774,
  0.06598984450101852,
  0.24365481734275818,
  0.24365481734275818,
  0.0012690355069935322,
  0.026649747043848038,
  0.7272842526435852,
  0.2753807008266449,
  0.2842639684677124,
  0.7271573543548584],
 'opacity': [1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0],
 'text': ['',
  '',
  '',
  '',
  '',
  '',
  'Powerful Ways',
  '',
  '11',
  'Powerful Ways\nTo Take Care\nOf  Your Skin\nIn Summer',
  ''],
 'font': [0, 0, 0, 0, 0, 0, 24, 0, 24, 189, 0],
 'font_size': [0.0,
  0.0,
  0.0,
  0.0,
  0.0,
  0.0,
  21.504981994628906,
  0.0,
  217.0,
  43.18538284301758,
  0.0],
 'text_align': [0, 0, 0, 0, 0, 0, 2, 0, 2, 2, 0],
 'angle': [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0],
 'color': [[236.0, 96.0, 104.0],
  [236.0, 96.0, 104.0],
  [29.0, 29.0, 27.0],
  [29.0, 29.0, 27.0],
  [29.0, 29.0, 27.0],
  [29.0, 29.0, 27.0],
  [0.0, 0.0, 1.0],
  [228.0, 148.0, 151.0],
  [0.0, 0.0, 1.0],
  [0.0, 0.0, 1.0],
  [29.0, 29.0, 27.0]],
 'image': [<PIL.PngImagePlugin.PngImageFile image mode=RGBA size=256x256>,
  <PIL.PngImagePlugin.PngImageFile image mode=RGBA size=256x256>,
  <PIL.PngImagePlugin.PngImageFile image mode=RGBA size=256x256>,
  <PIL.PngImagePlugin.PngImageFile image mode=RGBA size=256x256>,
  <PIL.PngImagePlugin.PngImageFile image mode=RGBA size=256x256>,
  <PIL.PngImagePlugin.PngImageFile image mode=RGBA size=256x256>,
  <PIL.PngImagePlugin.PngImageFile image mode=RGBA size=256x256>,
  <PIL.PngImagePlugin.PngImageFile image mode=RGBA size=256x256>,
  <PIL.PngImagePlugin.PngImageFile image mode=RGBA size=256x256>,
  <PIL.PngImagePlugin.PngImageFile image mode=RGBA size=256x256>,
  <PIL.PngImagePlugin.PngImageFile image mode=RGBA size=256x256>]}

Data Fields

Context features (Canvas attributes)

Field Type Shape Description
id string () Template ID from crello.com
group string () Broad design group, such as social media posts or blog headers
format string () Detailed design format, such as Instagram post or postcard
category string () Topic category of the design, such as holiday celebration
canvas_width int64 () Canvas pixel width
canvas_height int64 () Canvas pixel height
length int64 () Length of sequence features

Sequence features (Element attributes)

Field Type Shape Description
type string () Element type, such as vector shape, image, or text
left float32 () Element left position normalized to [0, 1] range w.r.t. canvas_width
top float32 () Element top position normalized to [0, 1] range w.r.t. canvas_height
width float32 () Element width normalized to [0, 1] range w.r.t. canvas_width
height float32 () Element height normalized to [0, 1] range w.r.t. canvas_height
color int64 (3,) Extracted main RGB color of the element
opacity float32 () Opacity in [0, 1] range
image_bytes string () Pre-rendered 256x256 preview of the element encoded in PNG format
text string () Text content in UTF-8 encoding for text element
font string () Font family name for text element
font_size float32 () Font size (height) in pixels
text_align string () Horizontal text alignment, left, center, right for text element
angle float32 () Element rotation angle (radian) w.r.t. the center of the element

Note that the color and pre-rendered images are do not necessarily accurately reproduce the original design templates. The original template is accessible at the following URL if still available.

https://create.vista.com/artboard/?template=<template_id>

Data Splits

The Crello dataset has 3 splits: train, validation, and test. The current split is generated such that the same title of the original template shows up in only in one split.

Split Count
train 18659
validaton 2391
test 2371

Dataset Creation

Curation Rationale

The Crello dataset is compiled for the general study of vector graphic documents, with the goal of producing a dataset that offers complete vector graphic information suitable for neural methodologies.

Source Data

Initial Data Collection and Normalization

The dataset is initially scraped from the former crello.com, and pre-processed to

Who are the source language producers?

While create.vista.com) owns those templates, the templates seems to be originially created by certain group of design studios.

Personal and Sensitive Information

The dataset does not contain any personal information about the creator, but may contain a picture of people in the design template.

Considerations for Using the Data

Social Impact of Dataset

This dataset was developed for advancing the general study of vector graphic documents, especially for generative systems of graphic design. Successful systems might enable automation of creative workflow that human designers get involved in.

Discussion of Biases

The templates contained in the dataset reflect the biases appearing in the source data, which could present gender biases in specific design categories.

Other Known Limitations

Due to the unknown data specification of the source data, the color and pre-rendered images do not necessarily accurately reproduce the original design templates. The original template is accessible at the following URL if still available.

https://create.vista.com/artboard/?template=<template_id>

Additional Information

Dataset Curators

The Crello dataset was developed by Kota Yamaguchi.

Licensing Information

The origin of the dataset is create.vista.com (formally, crello.com). The distributor ("We") do not own the copyrights of the original design templates. By using Crello dataset, the user of this dataset ("You") must agree to the VistaCreate License Agreements.

The dataset is distributed under CDLA-Permissive-2.0 license.

Note

We do not re-distribute the original files as we are not allowed by terms.

Citation Information 

@article{yamaguchi2021canvasvae,
  title={CanvasVAE: Learning to Generate Vector Graphic Documents},
  author={Yamaguchi, Kota},
  journal={ICCV},
  year={2021}
}

Contributions

Thanks to @kyamagu for adding this dataset.