Imadeddineibrahim/yolo_8_layout
Updated • 1
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End of preview. Expand in Data Studio
Dataset Card for PubLayNet
Dataset Summary
PubLayNet is a large document layout analysis dataset built by automatically matching XML representations and PDF content from more than one million PubMed Central Open Access articles. It contains more than 360,000 document images with COCO-style annotations for common layout elements such as text, title, list, table, and figure regions.
Supported Tasks and Leaderboards
The dataset supports document layout object detection and segmentation. No leaderboard is bundled with this Hugging Face packaging.
Languages
Document content is primarily English (en), but the task is visual document layout analysis.
Dataset Structure
Data Fields
Rows contain image_id, file_name, width, height, image, and COCO-style annotations.
Data Splits
| Split | Rows |
|---|---|
| train | 335,703 |
| validation | 11,245 |
| test | 11,405 |
Dataset Creation
PubLayNet was created from automatically parsed document layouts and released for large-scale document layout analysis.
Considerations for Using the Data
The dataset is document-centric and may not represent all document domains or non-English layout conventions.
Additional Information
Licensing Information
This dataset card uses the CDLA Permissive 1.0 license metadata from the local loader.
Citation Information
@inproceedings{zhong2019publaynet,
title={Publaynet: largest dataset ever for document layout analysis},
author={Zhong, Xu and Tang, Jianbin and Yepes, Antonio Jimeno},
booktitle={2019 International Conference on Document Analysis and Recognition (ICDAR)},
pages={1015--1022},
year={2019}
}
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Homepage:
developer.ibm.com
Repository:
github.com
Hugging Face Dataset:
huggingface.co
Paper (arXiv):
arxiv.org
Paper (ICDAR 2019):
ieeexplore.ieee.org
Number of rows:
358,353
Total file size:
108 GB
Models trained or fine-tuned on creative-graphic-design/PubLayNet