|
--- |
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annotations_creators: |
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- machine-generated |
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language_creators: |
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- found |
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language: |
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- en |
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license: |
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- cdla-permissive-1.0 |
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multilinguality: |
|
- monolingual |
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size_categories: [] |
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source_datasets: |
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- original |
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task_categories: |
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- image-classification |
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- image-segmentation |
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- image-to-text |
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- question-answering |
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- other |
|
- multiple-choice |
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- token-classification |
|
- tabular-to-text |
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- object-detection |
|
- table-question-answering |
|
- text-classification |
|
- table-to-text |
|
task_ids: |
|
- multi-label-image-classification |
|
- multi-class-image-classification |
|
- semantic-segmentation |
|
- image-captioning |
|
- extractive-qa |
|
- closed-domain-qa |
|
- multiple-choice-qa |
|
- named-entity-recognition |
|
pretty_name: PubLayNet |
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tags: |
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- graphic design |
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- layout-generation |
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dataset_info: |
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features: |
|
- name: image_id |
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dtype: int32 |
|
- name: file_name |
|
dtype: string |
|
- name: width |
|
dtype: int32 |
|
- name: height |
|
dtype: int32 |
|
- name: image |
|
dtype: image |
|
- name: annotations |
|
sequence: |
|
- name: annotation_id |
|
dtype: int32 |
|
- name: area |
|
dtype: float32 |
|
- name: bbox |
|
sequence: float32 |
|
length: 4 |
|
- name: category |
|
struct: |
|
- name: category_id |
|
dtype: int32 |
|
- name: name |
|
dtype: |
|
class_label: |
|
names: |
|
'0': text |
|
'1': title |
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'2': list |
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'3': table |
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'4': figure |
|
- name: supercategory |
|
dtype: string |
|
- name: category_id |
|
dtype: int32 |
|
- name: image_id |
|
dtype: int32 |
|
- name: iscrowd |
|
dtype: bool |
|
- name: segmentation |
|
dtype: image |
|
splits: |
|
- name: train |
|
num_bytes: 99127922734.771 |
|
num_examples: 335703 |
|
- name: validation |
|
num_bytes: 3513203604.885 |
|
num_examples: 11245 |
|
- name: test |
|
num_bytes: 3406081626.495 |
|
num_examples: 11405 |
|
download_size: 107597638930 |
|
dataset_size: 106047207966.15099 |
|
configs: |
|
- config_name: default |
|
data_files: |
|
- split: train |
|
path: data/train-* |
|
- split: validation |
|
path: data/validation-* |
|
- split: test |
|
path: data/test-* |
|
--- |
|
|
|
# Dataset Card for PubLayNet |
|
|
|
[](https://github.com/shunk031/huggingface-datasets_PubLayNet/actions/workflows/ci.yaml) |
|
|
|
## Table of Contents |
|
- [Dataset Card Creation Guide](#dataset-card-creation-guide) |
|
- [Table of Contents](#table-of-contents) |
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- [Dataset Description](#dataset-description) |
|
- [Dataset Summary](#dataset-summary) |
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- [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards) |
|
- [Languages](#languages) |
|
- [Dataset Structure](#dataset-structure) |
|
- [Data Instances](#data-instances) |
|
- [Data Fields](#data-fields) |
|
- [Data Splits](#data-splits) |
|
- [Dataset Creation](#dataset-creation) |
|
- [Curation Rationale](#curation-rationale) |
|
- [Source Data](#source-data) |
|
- [Initial Data Collection and Normalization](#initial-data-collection-and-normalization) |
|
- [Who are the source language producers?](#who-are-the-source-language-producers) |
|
- [Annotations](#annotations) |
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- [Annotation process](#annotation-process) |
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- [Who are the annotators?](#who-are-the-annotators) |
|
- [Personal and Sensitive Information](#personal-and-sensitive-information) |
|
- [Considerations for Using the Data](#considerations-for-using-the-data) |
|
- [Social Impact of Dataset](#social-impact-of-dataset) |
|
- [Discussion of Biases](#discussion-of-biases) |
|
- [Other Known Limitations](#other-known-limitations) |
|
- [Additional Information](#additional-information) |
|
- [Dataset Curators](#dataset-curators) |
|
- [Licensing Information](#licensing-information) |
|
- [Citation Information](#citation-information) |
|
- [Contributions](#contributions) |
|
|
|
## Dataset Description |
|
|
|
- **Homepage:** https://developer.ibm.com/exchanges/data/all/publaynet/ |
|
- **Repository:** https://github.com/shunk031/huggingface-datasets_PubLayNet |
|
- **Paper (Preprint):** https://arxiv.org/abs/1908.07836 |
|
- **Paper (ICDAR2019):** https://ieeexplore.ieee.org/document/8977963 |
|
|
|
### Dataset Summary |
|
|
|
PubLayNet is a dataset for document layout analysis. It contains images of research papers and articles and annotations for various elements in a page such as "text", "list", "figure" etc in these research paper images. The dataset was obtained by automatically matching the XML representations and the content of over 1 million PDF articles that are publicly available on PubMed Central. |
|
|
|
### Supported Tasks and Leaderboards |
|
|
|
[More Information Needed] |
|
|
|
### Languages |
|
|
|
[More Information Needed] |
|
|
|
## Dataset Structure |
|
|
|
### Data Instances |
|
|
|
```python |
|
import datasets as ds |
|
|
|
dataset = ds.load_dataset( |
|
path="shunk031/PubLayNet", |
|
decode_rle=True, # True if Run-length Encoding (RLE) is to be decoded and converted to binary mask. |
|
) |
|
``` |
|
|
|
### 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 |
|
|
|
- [CDLA-Permissive](https://cdla.io/permissive-1-0/) |
|
|
|
### Citation Information |
|
|
|
|
|
```bibtex |
|
@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}, |
|
organization={IEEE} |
|
} |
|
``` |
|
|
|
### Contributions |
|
|
|
Thanks to [ibm-aur-nlp/PubLayNet](https://github.com/ibm-aur-nlp/PubLayNet) for creating this dataset. |
|
|
