Datasets:
annotations_creators:
- crowdsourced
language:
- zh
language_creators:
- found
license:
- cc-by-nc-sa-4.0
multilinguality:
- monolingual
pretty_name: CGL-Dataset
size_categories:
- 10K<n<100K
source_datasets:
- original
tags:
- graphic-design
- poster
- layout-generation
task_categories:
- image-to-image
task_ids: []
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_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
Dataset Card for CGL-Dataset
Dataset Description
- Homepage: https://github.com/minzhouGithub/CGL-GAN
- Repository: https://github.com/creative-graphic-design/huggingface-datasets/tree/main/datasets/CGLDataset
- Hugging Face Dataset: https://huggingface.co/datasets/creative-graphic-design/CGL-Dataset
- Paper (arXiv): https://arxiv.org/abs/2205.00303
- Paper (IJCAI 2022): https://www.ijcai.org/proceedings/2022/692
Dataset Summary
CGL-Dataset is a poster layout dataset released with Composition-aware Graphic Layout GAN for Visual-Textual Presentation Designs. The paper studies layout generation for a given image, emphasizing that both global semantics and spatial image composition affect where graphic elements should be placed. The original dataset contains 60,548 advertising posters with annotated layout information.
Supported Tasks and Leaderboards
The dataset supports poster layout generation and layout-conditioned graphic design modeling. No public leaderboard is bundled with this Hugging Face dataset.
Languages
Poster text is primarily Chinese (zh).
Dataset Structure
Data Fields
The default config contains image_id, file_name, width, height, image, and COCO-style annotations.
The ralf-style config provides original posters, inpainted posters, saliency maps, and annotations for layout-generation pipelines.
Data Splits
| Config | Split | Rows |
|---|---|---|
| default | train | 54,546 |
| default | validation | 6,002 |
| default | test | 1,000 |
| ralf-style | train | 48,438 |
| ralf-style | validation | 6,055 |
| ralf-style | test | 6,055 |
| ralf-style | no_annotation | 1,000 |
Dataset Creation
The dataset was created for Composition-aware Graphic Layout GAN research. It provides visual element categories and positions for poster layout generation, enabling models to synthesize text and decorative layouts conditioned on image content rather than using template-only rules.
Considerations for Using the Data
The data focuses on advertising poster layouts and may reflect the visual conventions of the source domain.
Additional Information
Licensing Information
The dataset card uses the CC BY-NC-SA 4.0 metadata from the local loader.
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},
pages = {4995--5001},
year = {2022},
doi = {10.24963/ijcai.2022/692},
url = {https://doi.org/10.24963/ijcai.2022/692}
}
Contributions
Thanks to minzhouGithub for creating the original dataset.