Poster Generation Dataset
Collection
4 items • Updated • 1
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image_id int64 | file_name string | width int64 | height int64 | image image | annotations sequence | text_annotations dict | text_features dict |
|---|---|---|---|---|---|---|---|
2 | O1CN01HnK3zH1HoH7oxbsE5_!!3409010804-0-alimamazszw_mask002.jpg | 513 | 750 | {
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... | {
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6 | O1CN01MQMDp41wbMDu5CuGj_!!1125016326-0-alimamazszw_mask002.jpg | 513 | 750 | {
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7 | O1CN015AQ4xK2HQSiTNNktq_!!2097409145-0-alimamazszw_mask002.jpg | 513 | 750 | {
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10 | O1CN01jw3oTi22XaeV9w5oU_!!2270977130-0-alimamazszw_mask002.jpg | 513 | 750 | {
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12 | O1CN012D5Xga1JXRpa1F7T9_!!776151038-0-alimamazszw_mask002.jpg | 513 | 750 | {
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13 | O1CN01ixtZZv1YnDhuccLLj_!!2746493103-0-alimamazszw_mask002.jpg | 513 | 750 | {
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[
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14 | O1CN01RhC83e2LyaiAoaOZl_!!2965419761-0-alimamazszw_mask002.jpg | 513 | 750 | {
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... | null | null | |
15 | O1CN01K6rwcc1qSl5wUK4Eh_!!521385495-0-alimamazszw_mask002.jpg | 513 | 750 | {
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"pin": "jd_50e595b192af9",
"data": {
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{
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16 | O1CN01whMbZv1ITxBeIjmFt_!!3356060895-0-alimamazszw_mask002.jpg | 513 | 750 | {
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17 | O1CN01XjAvU82BmY7NKOSX8_!!1014248381-0-alimamazszw_mask002.jpg | 513 | 750 | {
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... | null | null |
End of preview.
Dataset Card for CGL-Dataset-v2
Dataset Summary
CGL-Dataset V2 is a dataset for the task of automatic graphic layout design of advertising posters, containing 60,548 training samples and 1035 testing samples. It is an extension of CGL-Dataset.
Supported Tasks and Leaderboards
[More Information Needed]
Languages
The language data in CGL-Dataset v2 is in Chinese (BCP-47 zh).
Dataset Structure
Data Instances
To use CGL-Dataset v2 dataset, you need to download RADM_dataset.tar.gz that includes the poster image, text and text features via JD Cloud or Google Drive.
Then place the downloaded files in the following structure and specify its path.
/path/to/datasets
└── RADM_dataset.tar.gz
import datasets as ds
dataset = ds.load_dataset(
path="shunk031/CGL-Dataset-v2",
data_dir="/path/to/datasets/RADM_dataset.tar.gz",
decode_rle=True, # True if Run-length Encoding (RLE) is to be decoded and converted to binary mask.
include_text_features=True, # True if RoBERTa-based text feature is to be loaded.
)
Data Fields
[More Information Needed]
Data Splits
[More Information Needed]
Dataset Creation
Curation Rationale
[More Information Needed]
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{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 this dataset.
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Homepage:
github.com
Repository:
github.com
Paper (Preprint):
arxiv.org
Paper (CIKM'23):
dl.acm.org
Total file size:
45 GB