Datasets:
annotations_creators:
- crowdsourced
language:
- zh
language_creators:
- found
license:
- unknown
multilinguality:
- monolingual
pretty_name: CGL-Dataset v2
size_categories:
- 10K<n<100K
source_datasets:
- CGL-Dataset
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: 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: annotation_id
dtype: int64
- 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
- name: category_id
dtype: int64
- name: image_id
dtype: int64
- name: iscrowd
dtype: bool
- name: segmentation
dtype: image
- name: text_annotations
struct:
- name: is_sample
dtype: bool
- name: image
dtype: string
- name: rotate
dtype: float32
- name: pin
dtype: string
- name: data
sequence:
- name: category_description
dtype: string
- name: points
sequence:
- name: x
dtype: int64
- name: 'y'
dtype: int64
- name: user_selected_value
struct:
- name: name
dtype: string
- name: product_detail_highlighted_word
sequence: string
- name: blc_text
sequence: string
- name: adv_sellpoint
sequence: string
- name: text_features
struct:
- name: num
dtype: int64
- name: pos
sequence:
sequence: int64
- name: feats
sequence:
sequence:
sequence: float32
splits:
- name: train
num_bytes: 6825941140.344
num_examples: 60548
- name: test
num_bytes: 261185824.48
num_examples: 1035
download_size: 7093932679
dataset_size: 7087126964.823999
- 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: annotation_id
dtype: int64
- 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
- name: category_id
dtype: int64
- name: image_id
dtype: int64
- name: iscrowd
dtype: bool
- name: segmentation
dtype: image
- name: text_annotations
struct:
- name: is_sample
dtype: bool
- name: image
dtype: string
- name: rotate
dtype: float32
- name: pin
dtype: string
- name: data
sequence:
- name: category_description
dtype: string
- name: points
sequence:
- name: x
dtype: int64
- name: 'y'
dtype: int64
- name: user_selected_value
struct:
- name: name
dtype: string
- name: product_detail_highlighted_word
sequence: string
- name: blc_text
sequence: string
- name: adv_sellpoint
sequence: string
- name: text_features
struct:
- name: num
dtype: int64
- name: pos
sequence:
sequence: int64
- name: feats
sequence:
sequence:
sequence: float32
splits:
- name: train
num_bytes: 29188440681.841053
num_examples: 48438
- name: validation
num_bytes: 3651199848.741473
num_examples: 6055
- name: test
num_bytes: 3656104138.376473
num_examples: 6055
- name: no_annotation
num_bytes: 307193567.355
num_examples: 1035
download_size: 37888671814
dataset_size: 36802938236.314
Dataset Card for CGL-Dataset v2
Dataset Description
- Homepage: https://github.com/liuan0803/RADM
- Repository: https://github.com/creative-graphic-design/huggingface-datasets/tree/main/datasets/CGLDatasetV2
- Hugging Face Dataset: https://huggingface.co/datasets/creative-graphic-design/CGL-Dataset-v2
- Paper (arXiv): https://arxiv.org/abs/2306.09086
- Paper (CIKM 2023): https://dl.acm.org/doi/10.1145/3583780.3615028
Dataset Summary
CGL-Dataset v2 is an advertising-poster layout dataset released with Relation-Aware Diffusion Model for Controllable Poster Layout Generation. The paper argues that poster layouts should account for both visual-textual relationships and geometry relationships between elements. This version extends CGL-Dataset with richer element annotations, text annotations, and text features for controllable poster layout generation.
Supported Tasks and Leaderboards
The dataset supports poster layout generation, layout understanding, and relation-aware controllable generation. No public leaderboard is bundled with this Hugging Face dataset.
Languages
Poster text and annotations are primarily Chinese (zh).
Dataset Structure
Data Fields
The default config contains poster images and COCO-style instance annotations with optional text_annotations and text_features.
The ralf-style config exposes the same data in a layout-generation format with original posters, inpainted posters, saliency maps, and annotations.
Data Splits
| Config | Split | Rows |
|---|---|---|
| default | train | 60,548 |
| default | test | 1,035 |
| ralf-style | train | 48,438 |
| ralf-style | validation | 6,055 |
| ralf-style | test | 6,055 |
| ralf-style | no_annotation | 1,035 |
Dataset Creation
The original release was created for relation-aware diffusion research on controllable poster layout generation. Posters are annotated with visual elements such as logos, text, underlays, embellishments, and highlighted text, and the dataset supports generation under user constraints.
Considerations for Using the Data
The dataset is focused on advertising posters and Chinese e-commerce-style visual content. Models trained on it may inherit the visual conventions and category distribution of the source data.
Additional Information
Licensing Information
The dataset license is not specified in the local loader metadata. Users should verify the upstream terms before redistribution or commercial use.
Citation Information
@inproceedings{li2023relation,
title={Relation-Aware Diffusion Model for Controllable Poster Layout Generation},
author={Li, Fengheng and Liu, An and Feng, Wei and Zhu, Honghe and Li, Yaoyu and Zhang, Zheng and Lv, Jingjing and Zhu, Xin and Shen, Junjie and Lin, Zhangang},
booktitle={Proceedings of the 32nd ACM international conference on information & knowledge management},
pages={1249--1258},
year={2023}
}
Contributions
Thanks to liuan0803 for creating the original dataset.