annotations_creators:
- crowdsourced
language_creators:
- found
language:
- zh
license:
- cc-by-nc-sa-4.0
multilinguality:
- monolingual
size_categories: []
source_datasets:
- original
task_categories:
- other
task_ids: []
pretty_name: CGL-Dataset
tags:
- graphic-design
- layout-generation
- poster-generation
dataset_info:
- config_name: default
features:
- name: image_id
dtype: int64
- name: file_name
dtype: string
- name: width
dtype: int64
- name: height
dtype: int64
- name: image
dtype: image
- name: annotations
sequence:
- name: area
dtype: int64
- name: bbox
sequence: int64
- name: category
struct:
- name: category_id
dtype: int64
- name: name
dtype:
class_label:
names:
'0': logo
'1': text
'2': underlay
'3': embellishment
'4': highlighted text
- name: supercategory
dtype: string
splits:
- name: train
num_bytes: 7727076720.09
num_examples: 54546
- name: validation
num_bytes: 824988413.326
num_examples: 6002
- name: test
num_bytes: 448856950
num_examples: 1000
download_size: 8848246626
dataset_size: 9000922083.416
- config_name: ralf-style
features:
- name: image_id
dtype: int64
- name: file_name
dtype: string
- name: width
dtype: int64
- name: height
dtype: int64
- name: original_poster
dtype: image
- name: inpainted_poster
dtype: image
- name: saliency_map
dtype: image
- name: saliency_map_sub
dtype: image
- name: annotations
sequence:
- name: area
dtype: int64
- name: bbox
sequence: int64
- name: category
struct:
- name: category_id
dtype: int64
- name: name
dtype:
class_label:
names:
'0': logo
'1': text
'2': underlay
'3': embellishment
'4': highlighted text
- name: supercategory
dtype: string
splits:
- name: train
num_bytes: 29834119281.261364
num_examples: 48438
- name: validation
num_bytes: 3722970297.954319
num_examples: 6055
- name: test
num_bytes: 3701864874.9093184
num_examples: 6055
- name: no_annotation
num_bytes: 448869325
num_examples: 1000
download_size: 37543869068
dataset_size: 37707823779.125
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: validation
path: data/validation-*
- split: test
path: data/test-*
- config_name: ralf-style
data_files:
- split: train
path: ralf-style/train-*
- split: validation
path: ralf-style/validation-*
- split: test
path: ralf-style/test-*
- split: no_annotation
path: ralf-style/no_annotation-*
Dataset Card for CGL-Dataset
Table of Contents
- Dataset Card Creation Guide
Dataset Description
- Homepage: https://github.com/minzhouGithub/CGL-GAN
- Repository: https://github.com/creative-graphic-design/huggingface-datasets_CGL-Dataset
- Paper (Preprint): https://arxiv.org/abs/2205.00303
- Paper (IJCAI2022): https://www.ijcai.org/proceedings/2022/692
Dataset Summary
The CGL-Dataset is a dataset used for the task of automatic graphic layout design for advertising posters. It contains 61,548 samples and is provided by Alibaba Group.
Supported Tasks and Leaderboards
The task is to generate high-quality graphic layouts for advertising posters based on clean product images and their visual contents. The training set and validation set are collections of 60,548 e-commerce advertising posters, with manual annotations of the categories and positions of elements (such as logos, texts, backgrounds, and embellishments on the posters). Note that the validation set also consists of posters, not clean product images. The test set contains 1,000 clean product images without graphic elements such as logos or texts, consistent with real application data.
Languages
[More Information Needed]
Dataset Structure
Data Instances
import datasets as ds
dataset = ds.load_dataset("creative-graphic-design/CGL-Dataset")
Provide any additional information that is not covered in the other sections about the data here. In particular describe any relationships between data points and if these relationships are made explicit. -->
Data Fields
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Data Splits
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Dataset Creation
Curation Rationale
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Source Data
[More Information Needed]
Initial Data Collection and Normalization
[More Information Needed]
Who are the source language producers?
[More Information Needed]
Annotations
[More Information Needed]
Annotation process
[More Information Needed]
Who are the annotators?
[More Information Needed]
Personal and Sensitive Information
[More Information Needed]
Considerations for Using the Data
Social Impact of Dataset
[More Information Needed]
Discussion of Biases
[More Information Needed]
Other Known Limitations
[More Information Needed]
Additional Information
Dataset Curators
[More Information Needed]
Licensing Information
[More Information Needed]
Citation Information
@inproceedings{ijcai2022p692,
title = {Composition-aware Graphic Layout GAN for Visual-Textual Presentation Designs},
author = {Zhou, Min and Xu, Chenchen and Ma, Ye and Ge, Tiezheng and Jiang, Yuning and Xu, Weiwei},
booktitle = {Proceedings of the Thirty-First International Joint Conference on Artificial Intelligence, {IJCAI-22}},
publisher = {International Joint Conferences on Artificial Intelligence Organization},
editor = {Lud De Raedt},
pages = {4995--5001},
year = {2022},
month = {7},
note = {AI and Arts},
doi = {10.24963/ijcai.2022/692},
url = {https://doi.org/10.24963/ijcai.2022/692},
}
Contributions
Thanks to @minzhouGithub for adding this dataset.