Poster Generation Dataset
Collection
4 items
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Updated
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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 | {
"annotation_id": [
1
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"area": [
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],
"bbox": [
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],
"category": [
{
"category_id": 2,
"name": "文字",
"supercategory": "文字"
}
],
"category_id": [
2
],
"image_id": [
2
],
"iscrowd": [
false
],
"segmentation": [
{
"src": "https://datasets-server.huggingface.co/assets/creative-graphic-design/CGL-Dataset-v2/--/1b11bf53a6c6f9a811d464de15c8e9fbb3cf512a/--/default/train/0/annotations/image-45a90687.jpg?Expires=1724548182&Signature=sZMFzuCFUpA0F3X5XanVMI-YAldGKxMIF~qfRKJEnjgaxKygz0Enqt9BYQXtPswTb5kXL0QWnUvDzd2Jjjzeislq8bRy0t8IZo7TDjbnRwqW-T6b0MKBs-z1KVz0K5JKboGAkNXT6ZpikUW1ZrtXnQg67JZEp6Sch64Qla5K0YQaRZumjqc0VDsDfBEDyauHso~eoLKKx~mBu2FJvceLY7mtlRJI7tO4I1uhMvaJd3B0DJvb4Os-VBqNKwAnMN4rS3lpWvWMx7lQhSPq3LboRRBHpBXhzYElvRTD2svMBLQhOmN4cEM1jDmwFbdzZrtbTUIxH~80phi0CIuINY8V2A__&Key-Pair-Id=K3EI6M078Z3AC3",
"height": 750,
"width": 513
}
]
} | {
"is_sample": false,
"image": "ali_anno_1/0.png",
"rotate": 0,
"pin": "jd_50e595b192af9",
"data": {
"category_description": [
"新款尝鲜价"
],
"points": [
{
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}
],
"user_selected_value": [
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}
]
},
"product_detail_highlighted_word": null,
"blc_text": null,
"adv_sellpoint": null
} | {
"num": 1,
"pos": [
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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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