metadata
annotations_creators:
- no-annotation
language_creators:
- found
language:
- en
license: cdla-permissive-2.0
multilinguality:
- monolingual
size_categories:
- 10K<n<100K
source_datasets:
- original
task_categories:
- unconditional-image-generation
task_ids: []
pretty_name: crello
tags:
- graphic design
- design templates
dataset_info:
features:
- name: id
dtype: string
- name: length
dtype: int64
- name: group
dtype:
class_label:
names:
'0': SM
'1': HC
'2': MM
'3': SMA
'4': EO
'5': BG
- name: format
dtype:
class_label:
names:
'0': Instagram Story
'1': Instagram
'2': Facebook
'3': Facebook cover
'4': Twitter
'5': Facebook AD
'6': Poster
'7': Instagram AD
'8': Tumblr
'9': Image
'10': Pinterest
'11': Flayer
'12': FB event cover
'13': Postcard
'14': Invitation
'15': Youtube
'16': Email header
'17': Medium Rectangle
'18': Poster US
'19': Graphic
'20': Large Rectangle
'21': Card
'22': Logo
'23': Title
'24': Skyscraper
'25': Leaderboard
'26': Presentation
'27': Gift Certificate
'28': VK Universal Post
'29': Youtube Thumbnail
'30': Business card
'31': Book Cover
'32': Presentation Wide
'33': VK Community Cover
'34': Certificate
'35': Zoom Background
'36': VK Post with Button
'37': T-Shirt
'38': Instagram Highlight Cover
'39': Coupon
'40': Letterhead
'41': IGTV Cover
'42': Schedule Planner
'43': Album Cover
'44': LinkedIn Cover
'45': Storyboard
'46': Recipe Card
'47': Invoice
'48': Resume
'49': Menu
'50': Mood Board
'51': Mind Map
'52': Label
'53': Newsletter
'54': Brochure
'55': Ticket
'56': Proposal
'57': Snapchat Geofilter
'58': Snapchat Moment Filter
'59': Twitch Offline Banner
'60': Twitch Profile Banner
'61': Infographic
'62': Mobile Presentation
'63': Photo Book
'64': Web Banner
'65': Gallery Image
'66': Calendar
- name: canvas_width
dtype:
class_label:
names:
'0': '1080'
'1': '1200'
'2': '940'
'3': '851'
'4': '360'
'5': '1190'
'6': '1920'
'7': '419'
'8': '1024'
'9': '600'
'10': '1600'
'11': '735'
'12': '595'
'13': '3000'
'14': '2560'
'15': '1500'
'16': '300'
'17': '540'
'18': '1296'
'19': '336'
'20': '500'
'21': '432'
'22': '560'
'23': '160'
'24': '1280'
'25': '728'
'26': '1000'
'27': '241'
'28': '1590'
'29': '792'
'30': '576'
'31': '537'
'32': '1008'
'33': '420'
'34': '1128'
'35': '396'
'36': '841'
'37': '800'
'38': '635'
'39': '240'
'40': '842'
- name: canvas_height
dtype:
class_label:
names:
'0': '1080'
'1': '1920'
'2': '315'
'3': '788'
'4': '628'
'5': '600'
'6': '504'
'7': '1683'
'8': '298'
'9': '500'
'10': '512'
'11': '1102'
'12': '1440'
'13': '200'
'14': '400'
'15': '250'
'16': '810'
'17': '1728'
'18': '1200'
'19': '280'
'20': '841'
'21': '288'
'22': '90'
'23': '1055'
'24': '720'
'25': '768'
'26': '700'
'27': '142'
'28': '612'
'29': '2560'
'30': '2000'
'31': '240'
'32': '216'
'33': '842'
'34': '1296'
'35': '2340'
'36': '654'
'37': '191'
'38': '1600'
'39': '297'
'40': '595'
'41': '480'
'42': '576'
'43': '320'
'44': '380'
'45': '141'
- name: category
dtype:
class_label:
names:
'0': holidaysCelebration
'1': foodDrinks
'2': fashionStyle
'3': businessFinance
'4': homeStuff
'5': handcraftArt
'6': beauty
'7': leisureEntertainment
'8': natureWildlife
'9': educationScience
'10': technology
'11': medical
'12': socialActivityCharity
'13': realEstateBuilding
'14': sportExtreme
'15': travelsVacations
'16': pets
'17': religions
'18': citiesPlaces
'19': industry
'20': transportation
'21': kidsParents
'22': all
- name: title
dtype: string
- name: type
sequence:
class_label:
names:
'0': svgElement
'1': textElement
'2': imageElement
'3': coloredBackground
'4': maskElement
- name: left
sequence: float32
- name: top
sequence: float32
- name: width
sequence: float32
- name: height
sequence: float32
- name: opacity
sequence: float32
- name: color
sequence:
sequence: float32
length: 3
- name: image
sequence: image
- name: text
sequence: string
- name: font
sequence:
class_label:
names:
'0': ''
'1': Montserrat
'2': Bebas Neue
'3': Raleway
'4': Josefin Sans
'5': Cantarell
'6': Playfair Display
'7': Oswald
'8': Blogger
'9': Abril Fatface
'10': Prompt
'11': Comfortaa
'12': Rubik
'13': Open Sans
'14': Roboto
'15': Libre Baskerville
'16': Quicksand
'17': Dosis
'18': Podkova
'19': Lato
'20': Cormorant Infant
'21': Amatic Sc
'22': Fjalla One
'23': Playlist Script
'24': Arapey
'25': Baloo Tamma
'26': Graduate
'27': Titillium Web
'28': Kreon
'29': Nunito
'30': Rammetto One
'31': Anton
'32': Poiret One
'33': Alfa Slab One
'34': Righteous
'35': Play
'36': Space Mono
'37': Frank Ruhl Libre
'38': Yanone Kaffeesatz
'39': Pacifico
'40': Bangers
'41': Yellowtail
'42': Droid Serif
'43': Racing Sans One
'44': Merriweather
'45': Miriam Libre
'46': Crete Round
'47': Rubik One
'48': Bungee
'49': Sansita One
'50': Patua One
'51': Economica
'52': Caveat
'53': Philosopher
'54': Limelight
'55': Breathe
'56': Rokkitt
'57': Russo One
'58': Noticia Text
'59': Tinos
'60': Oleo Script
'61': Josefin Slab
'62': Arima Madurai
'63': Brusher Free Font
'64': Old Standard Tt
'65': Kalam
'66': Patrick Hand
'67': Playball
'68': Six Caps
'69': Bad Script
'70': Orbitron
'71': Contrail One
'72': Selima Script
'73': Gravitas One
'74': El Messiri
'75': Bubbler One
'76': Italiana
'77': Pompiere
'78': Lemon Tuesday
'79': Vast Shadow
'80': Sunday
'81': Cookie
'82': Exo 2
'83': Barrio
'84': Radley
'85': Mrs Sheppards
'86': Grand Hotel
'87': Great Vibes
'88': Maven Pro
'89': Knewave
'90': Damion
'91': Tulpen One
'92': Parisienne
'93': Superclarendon Regular
'94': Oxygen
'95': Nixie One
'96': Permanent Marker
'97': Medula One
'98': Cabin Sketch
'99': Vollkorn
'100': Yeseva One
'101': Montserrat Alternates
'102': Satisfy
'103': Sacramento
'104': Carter One
'105': Glass Antiqua
'106': Mr Dafoe
'107': Lauren
'108': Oranienbaum
'109': Scope One
'110': Mr De Haviland
'111': Pirou
'112': Rise
'113': Sensei
'114': Yesteryear
'115': Delius
'116': Sue Ellen Francisco
'117': Copse
'118': Kaushan Script
'119': Monda
'120': Pattaya
'121': Dancing Script
'122': Reem Kufi
'123': Playlist Caps
'124': Beacon
'125': Reenie Beanie
'126': Overlock
'127': Mrs Saint Delafield
'128': Open Sans Condensed
'129': Covered By Your Grace
'130': Varela Round
'131': Allura
'132': Buda
'133': Mikodacs
'134': Arkana Script
'135': Nothing You Could Do
'136': Rochester
'137': Fredericka The Great
'138': Port Lligat Slab
'139': Heebo
'140': Arimo
'141': Dawning Of A New Day
'142': Aldrich
'143': Neucha
'144': Source Serif Pro
'145': Shadows Into Light Two
'146': Armata
'147': Cutive Mono
'148': Merienda One
'149': Rissa Typeface
'150': Stalemate
'151': Assistant
'152': Pathway Gothic One
'153': Breathe Press
'154': Suez One
'155': Berkshire Swash
'156': Rakkas
'157': Pinyon Script
'158': Pt Sans
'159': Delius Swash Caps
'160': Kurale
'161': Offside
'162': Clicker Script
'163': Mate
'164': Bentham
'165': Rye
'166': Lalezar
'167': Julius Sans One
'168': Quattrocento
'169': V T323
'170': Finger Paint
'171': La Belle Aurore
'172': Inconsolata
'173': Press Start 2P
'174': Junge
'175': Iceberg
'176': Kelly Slab
'177': Handlee
'178': Rosario
'179': Gaegu
'180': Homemade Apple
'181': Londrina Shadow
'182': Meddon
'183': Elsie Swash Caps
'184': Share Tech Mono
'185': Black Ops One
'186': Fauna One
'187': Alice
'188': Arizonia
'189': Text Me One
'190': Nova Square
'191': Bungee Shade
'192': Just Me Again Down Here
'193': Jacques Francois Shadow
'194': Cousine
'195': Forum
'196': Architects Daughter
'197': Cedarville Cursive
'198': Elsie
'199': Sirin Stencil
'200': Vampiro One
'201': Dorsa
'202': Marcellus Sc
'203': Kumar One
'204': Allerta Stencil
'205': Courgette
'206': Rationale
'207': Gluk Znikomitno25
'208': Happy Monkey
'209': Stint Ultra Expanded
'210': Rock Salt
'211': Im Fell Dw Pica Sc
'212': Faster One
'213': Bellefair
'214': Wire One
'215': Geo
'216': Farsan
'217': League Script
'218': Chathura
'219': Euphoria Script
'220': Zeyada
'221': Jura
'222': Loved By The King
'223': Give You Glory
'224': Znikomitno24
'225': Gluk Glametrix
'226': Alegreya Sans
'227': Kristi
'228': Knewave Outline
'229': Pangolin
'230': Okolaks
'231': Seymour One
'232': Didact Gothic
'233': Kavivanar
'234': Underdog
'235': Alef
'236': Italianno
'237': Londrina Sketch
'238': Secular One
'239': Katibeh
'240': Caesar Dressing
'241': Lovers Quarrel
'242': Iceland
'243': Im Fell
'244': Waiting For The Sunrise
'245': David Libre
'246': Marck Script
'247': Kumar One Outline
'248': Znikomit
'249': Monsieur La Doulaise
'250': Gruppo
'251': Monofett
'252': Gfs Didot
'253': Petit Formal Script
'254': Dukomdesign Constantine
'255': Brusher
'256': Eb Garamond
'257': Ewert
'258': Bilbo
'259': Raleway Dots
'260': Gabriela
'261': Ruslan Display
- name: font_size
sequence: float32
- name: text_align
sequence:
class_label:
names:
'0': ''
'1': left
'2': center
'3': right
- name: angle
sequence: float32
- name: capitalize
sequence:
class_label:
names:
'0': 'false'
'1': 'true'
- name: line_height
sequence: float32
- name: letter_spacing
sequence: float32
- name: suitability
sequence:
class_label:
names:
'0': mobile
- name: keywords
sequence: string
- name: industries
sequence:
class_label:
names:
'0': marketingAds
'1': entertainmentLeisure
'2': services
'3': retail
'4': businessFinance
'5': educationTraining
'6': foodBeverages
'7': artCrafts
'8': fashionStyle
'9': healthWellness
'10': ecologyNature
'11': nonProfitCharity
'12': techGadgets
'13': beautyCosmetics
'14': homeLiving
'15': familyKids
'16': travelTourism
'17': sportFitness
'18': corporate
'19': petsAnimals
'20': realEstateConstruction
'21': transportDelivery
'22': religionFaith
'23': hrRecruitment
- name: preview
dtype: image
- name: cluster_index
dtype: int64
splits:
- name: train
num_bytes: 5058614277.34
num_examples: 19095
- name: validation
num_bytes: 538185754.149
num_examples: 1951
- name: test
num_bytes: 649876234.375
num_examples: 2375
download_size: 6188050025
dataset_size: 6246676265.864
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: validation
path: data/validation-*
- split: test
path: data/test-*
Dataset Card for Crello
Table of Contents
- Dataset Card for Crello
Dataset Description
- Homepage: CanvasVAE github
- Repository:
- Paper: CanvasVAE: Learning to Generate Vector Graphic Documents
- Leaderboard:
- Point of Contact: Kota Yamaguchi
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 were collected from crello.com (now create.vista.com) and converted to a low-resolution format suitable for machine learning analysis.
Usage
import datasets
dataset = datasets.load_dataset("cyberagent/crello")
Old revision is available via revision option.
import datasets
dataset = datasets.load_dataset("cyberagent/crello", revision="3.1")
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': '592d6c2c95a7a863ddcda140',
'length': 8,
'group': 4,
'format': 20,
'canvas_width': 3,
'canvas_height': 1,
'category': 0,
'title': 'Beauty Blog Ad Woman with Unusual Hairstyle',
'type': [1, 3, 3, 3, 3, 4, 4, 4],
'left': [0.0,
-0.0009259259095415473,
0.24444444477558136,
0.5712962746620178,
0.2657407522201538,
0.369228333234787,
0.2739444375038147,
0.44776931405067444],
'top': [0.0,
-0.0009259259095415473,
0.37037035822868347,
0.41296297311782837,
0.41296297311782837,
0.8946287035942078,
0.4549448788166046,
0.40591198205947876],
'width': [1.0,
1.0018517971038818,
0.510185182094574,
0.16296295821666718,
0.16296295821666718,
0.30000001192092896,
0.4990740716457367,
0.11388888955116272],
'height': [1.0,
1.0018517971038818,
0.25833332538604736,
0.004629629664123058,
0.004629629664123058,
0.016611294820904732,
0.12458471953868866,
0.02657807245850563],
'opacity': [1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0],
'text': ['', '', '', '', '', 'STAY WITH US', 'FOLLOW', 'PRESS'],
'font': [0, 0, 0, 0, 0, 152, 172, 152],
'font_size': [0.0, 0.0, 0.0, 0.0, 0.0, 18.0, 135.0, 30.0],
'text_align': [0, 0, 0, 0, 0, 2, 2, 2],
'angle': [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0],
'capitalize': [0, 0, 0, 0, 0, 0, 0, 0],
'line_height': [1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0],
'letter_spacing': [0.0, 0.0, 0.0, 0.0, 0.0, 14.0, 12.55813980102539, 3.0],
'suitability': [0],
'keywords': ['beautiful',
'beauty',
'blog',
'blogging',
'caucasian',
'cute',
'elegance',
'elegant',
'fashion',
'fashionable',
'femininity',
'glamour',
'hairstyle',
'luxury',
'model',
'stylish',
'vogue',
'website',
'woman',
'post',
'instagram',
'ig',
'insta',
'fashion',
'purple'],
'industries': [1, 8, 13],
'color': [[153.0, 118.0, 96.0],
[34.0, 23.0, 61.0],
[34.0, 23.0, 61.0],
[255.0, 255.0, 255.0],
[255.0, 255.0, 255.0],
[255.0, 255.0, 255.0],
[255.0, 255.0, 255.0],
[255.0, 255.0, 255.0]],
'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>]}
To get a label for categorical values, use the int2str method:
data = dataset['train'] # obtain the train set
key = "font"
example = data[0] # obtain first sample in train set
data.features[key].feature.int2str(example[key]) # obtain the text equivalent of the encoded values
Data Fields
In the following, categorical fields are shown as categorical type, but the actual storage is int64.
Canvas attributes
| Field | Type | Shape | Description |
|---|---|---|---|
| id | string | () | Template ID from crello.com |
| group | categorical | () | Broad design groups, such as social media posts or blog headers |
| format | categorical | () | Detailed design formats, such as Instagram post or postcard |
| category | categorical | () | Topic category of the design, such as holiday celebration |
| canvas_width | categorical | () | Canvas pixel width |
| canvas_height | categorical | () | Canvas pixel height |
| length | int64 | () | Length of elements |
| suitability | categorical | (None,) | List of display tags, only mobile tag exists |
| keywords | string | (None,) | List of keywords associated to this template |
| industries | categorical | (None,) | List of industry tags like marketingAds |
| preview | image | () | Preview image of the template for convenience; only for debugging |
| cluster_index | int64 | () | Cluster index used to split the dataset; only for debugging |
Element attributes
| Field | Type | Shape | Description |
|---|---|---|---|
| type | categorical | (None,) | Element type, such as vector shape, image, or text |
| left | float32 | (None,) | Element left position normalized to [0, 1] range w.r.t. canvas_width |
| top | float32 | (None,) | Element top position normalized to [0, 1] range w.r.t. canvas_height |
| width | float32 | (None,) | Element width normalized to [0, 1] range w.r.t. canvas_width |
| height | float32 | (None,) | Element height normalized to [0, 1] range w.r.t. canvas_height |
| color | int64 | (None, 3) | Extracted main RGB color of the element |
| opacity | float32 | (None,) | Opacity in [0, 1] range |
| image | image | (None,) | Pre-rendered 256x256 preview of the element encoded in PNG format |
| text | string | (None,) | Text content in UTF-8 encoding for text element |
| font | categorical | (None,) | Font family name for text element |
| font_size | float32 | (None,) | Font size (height) in pixels |
| text_align | categorical | (None,) | Horizontal text alignment, left, center, right for text element |
| angle | float32 | (None,) | Element rotation angle (radian) w.r.t. the center of the element |
| capitalize | categorical | (None,) | Binary flag to capitalize letters |
| line_height | float32 | (None,) | Scaling parameter to line height, default is 1.0 |
| letter_spacing | float32 | (None,) | Adjustment parameter for letter spacing, default is 0.0 |
Note that 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>
left and top can be negative because elements can be bigger than the canvas size.
Data Splits
The Crello dataset has 3 splits: train, validation, and test. The current split is generated based on appearance-based clustering.
| Split | Count |
|---|---|
| train | 19095 |
| validaton | 1951 |
| test | 2375 |
Visualization
Each example can be visualized in the following approach using skia-python. Note the following does not guarantee a similar appearance to the original template. Currently, the quality of text rendering is far from perfect.
import io
from typing import Any, Dict
import numpy as np
import skia
def render(features: datasets.Features, example: Dict[str, Any], max_size: float=512.) -> bytes:
"""Render parsed sequence example onto an image and return as PNG bytes."""
canvas_width = int(features["canvas_width"].int2str(example["canvas_width"]))
canvas_height = int(features["canvas_height"].int2str(example["canvas_height"]))
scale = min(1.0, max_size / canvas_width, max_size / canvas_height)
surface = skia.Surface(int(scale * canvas_width), int(scale * canvas_height))
with surface as canvas:
canvas.scale(scale, scale)
for index in range(example["length"]):
pil_image = example["image"][index]
image = skia.Image.frombytes(
pil_image.convert('RGBA').tobytes(),
pil_image.size,
skia.kRGBA_8888_ColorType)
left = example["left"][index] * canvas_width
top = example["top"][index] * canvas_height
width = example["width"][index] * canvas_width
height = example["height"][index] * canvas_height
rect = skia.Rect.MakeXYWH(left, top, width, height)
paint = skia.Paint(Alphaf=example["opacity"][index], AntiAlias=True)
angle = example["angle"][index]
with skia.AutoCanvasRestore(canvas):
if angle != 0:
degree = 180. * angle / np.pi
canvas.rotate(degree, left + width / 2., top + height / 2.)
canvas.drawImageRect(image, rect, paint=paint)
image = surface.makeImageSnapshot()
with io.BytesIO() as f:
image.save(f, skia.kPNG)
return f.getvalue()
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 the above format.
Who are the source language producers?
While create.vista.com owns those templates, the templates seem to be originally created by a specific 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 utilization might enable the 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 the 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}
}
Releases
4.0.0: v4 release (Dec 5, 2023)
- Change the dataset split based on the template appearance to avoid near-duplicates: no compatibility with v3.
- Class labels have been reordered: no compabilitity with v3.
- Small improvement to font rendering.
3.1: bugfix release (Feb 16, 2023)
- Fix a bug that ignores newline characters in some of the texts.
3.0: v3 release (Feb 13, 2023)
- Migrate to Hugging Face Hub.
- Fix various text rendering bugs.
- Change split generation criteria for avoiding near-duplicates: no compatibility with v2 splits.
- Incorporate a motion picture thumbnail in templates.
- Add
title,keywords,suitability, andindustriescanvas attributes. - Add
capitalize,line_height, andletter_spacingelement attributes.
2.0: v2 release (May 26, 2022)
- Add
text,font,font_size,text_align, andangleelement attributes. - Include rendered text element in
image_bytes.
1.0: v1 release (Aug 24, 2021)
Contributions
Thanks to @kyamagu for adding this dataset.