datasets
Training and evaluation data, with the modality, task and licence stated up front. Listed live from the Hugging Face Hub.
DocLayNet-v1.2
Dataset Card for DocLayNet v1.2
Dataset Summary
This dataset is an extention of the original DocLayNet dataset which embeds the PDF files of the document images inside a binary column.
DocLayNet provides page-by-page layout segmentation ground-truth using bounding-boxes for 11 distinct class labels on 80863 unique pages from 6 document categories. It provides several unique features compared to related work such as PubLayNet or DocBank:
Human Annotation: DocLayNet is… See the full description on the dataset page: https://huggingface.co/datasets/docling-project/DocLayNet-v1.2.DocLayNet-v1.1
Dataset Card for DocLayNet v1.1
Dataset Summary
DocLayNet provides page-by-page layout segmentation ground-truth using bounding-boxes for 11 distinct class labels on 80863 unique pages from 6 document categories. It provides several unique features compared to related work such as PubLayNet or DocBank:
Human Annotation: DocLayNet is hand-annotated by well-trained experts, providing a gold-standard in layout segmentation through human recognition and interpretation of… See the full description on the dataset page: https://huggingface.co/datasets/docling-project/DocLayNet-v1.1.doclaynet_processed
Dataset Card for "doclaynet_processed"
Clean version of DocLayNet ready for finetuning.
doclaynet_benchDocLayNet-baseAccurate document layout analysis is a key requirement for high-quality PDF document conversion. With the recent availability of public, large ground-truth datasets such as PubLayNet and DocBank, deep-learning models have proven to be very effective at layout detection and segmentation. While these datasets are of adequate size to train such models, they severely lack in layout variability since they are sourced from scientific article repositories such as PubMed and arXiv only. Consequently, the accuracy of the layout segmentation drops significantly when these models are applied on more challenging and diverse layouts. In this paper, we present \textit{DocLayNet}, a new, publicly available, document-layout annotation dataset in COCO format. It contains 80863 manually annotated pages from diverse data sources to represent a wide variability in layouts. For each PDF page, the layout annotations provide labelled bounding-boxes with a choice of 11 distinct classes. DocLayNet also provides a subset of double- and triple-annotated pages to determine the inter-annotator agreement. In multiple experiments, we provide smallline accuracy scores (in mAP) for a set of popular object detection models. We also demonstrate that these models fall approximately 10\% behind the inter-annotator agreement. Furthermore, we provide evidence that DocLayNet is of sufficient size. Lastly, we compare models trained on PubLayNet, DocBank and DocLayNet, showing that layout predictions of the DocLayNet-trained models are more robust and thus the preferred choice for general-purpose document-layout analysis.DocLayNet-smallAccurate document layout analysis is a key requirement for high-quality PDF document conversion. With the recent availability of public, large ground-truth datasets such as PubLayNet and DocBank, deep-learning models have proven to be very effective at layout detection and segmentation. While these datasets are of adequate size to train such models, they severely lack in layout variability since they are sourced from scientific article repositories such as PubMed and arXiv only. Consequently, the accuracy of the layout segmentation drops significantly when these models are applied on more challenging and diverse layouts. In this paper, we present \textit{DocLayNet}, a new, publicly available, document-layout annotation dataset in COCO format. It contains 80863 manually annotated pages from diverse data sources to represent a wide variability in layouts. For each PDF page, the layout annotations provide labelled bounding-boxes with a choice of 11 distinct classes. DocLayNet also provides a subset of double- and triple-annotated pages to determine the inter-annotator agreement. In multiple experiments, we provide smallline accuracy scores (in mAP) for a set of popular object detection models. We also demonstrate that these models fall approximately 10\% behind the inter-annotator agreement. Furthermore, we provide evidence that DocLayNet is of sufficient size. Lastly, we compare models trained on PubLayNet, DocBank and DocLayNet, showing that layout predictions of the DocLayNet-trained models are more robust and thus the preferred choice for general-purpose document-layout analysis.DocLayNet_rankdoclaynet-pt-enriched-formuladoclaynet-grpodoclaynet-document-level
DocLayNet Document-Level Reconstruction and 8K Expansion
This dataset is a normalized, one-row-per-document view over the page-level
DocLayNet v1.1
dataset. Pages are grouped using DocLayNet's source metadata and ordered by their original
page number.
Dataset summary
2,944 logical documents
80,863 observed pages
896 complete document groups
2,048 partial document groups
Train: 2,355 documents / 60,810 pages
Validation: 294 documents / 7,964 pages
Test: 295… See the full description on the dataset page: https://huggingface.co/datasets/operant-ai/doclaynet-document-level.arocrbench_doclaynetPlease see paper & code for more information:
https://github.com/mbzuai-oryx/KITAB-Bench
https://arxiv.org/abs/2502.14949
Doclaynet-Full
NOtice: The category Ids are not mapped btw 0-10 doclaynet classes, rather they are 2-12 Use the following Classes map.
{'caption': 2,
'footnote': 3,
'formula': 4,
'list_item': 5,
'page_footer': 6,
'page_header': 7,
'picture': 8,
'section_header': 9,
'table': 10,
'text': 11,
'title': 12}
dataset_info:
config_name: all
features:
name: image, dtype: image
name: category_ids, sequence: int32
name: image_id, dtype: int32
name: boxes, sequence:… See the full description on the dataset page: https://huggingface.co/datasets/thewalnutaisg/Doclaynet-Full.DocLayout_YOLO_DocLayNet_iccv1968small-DocLayNet-v1.1doclaynet_train_cleaned
doclaynet_train_cleaned
The doclaynet_train family of the ElliotVL supervised-fine-tuning pool, after VLM cleaning.
images
54,199
QA turns
145,370
answers rewritten by the cleaning pass
0
QA created by the cleaning pass (new_qa)
not measured for this family
shards
43
How this was cleaned
A vision-language model read each image together with its QA and judged the item. The pass is
not a filter that only removes rows — it rewrites answers… See the full description on the dataset page: https://huggingface.co/datasets/Elliot-Data/doclaynet_train_cleaned.doclaynet-full
Category IDs
1 - Caption
2 - Footnote
3 - Formula
4 - List Item
5 - Page Footer
6 - Page Header
7 - Picture
8 - Section Header
9 - Table
10 - Text
11 - Title
DoCLayNet-large-wt-image
Dataset Card for DocLayNet large without image
About this card (02/14/2024)
Property and license
All information from this page but the content of this paragraph "About this card (02/14/2025)" has been copied/pasted from Dataset Card for DocLayNet.
DocLayNet is a dataset created by Deep Search (IBM Research) published under license CDLA-Permissive-1.0.
I do not claim any rights to the data taken from this dataset and published on this page.
Dataset… See the full description on the dataset page: https://huggingface.co/datasets/agomberto/DoCLayNet-large-wt-image.DocLayNet-Instruct-v1-preprocessedDocLayNet-tiny
Dataset Card for "DocLayNet-tiny"
Tiny set for unit tests based on https://huggingface.co/datasets/pierreguillou/DocLayNet-small.
Total ~0.1% of DocLayNet.
doclaynet-for-yoloUsed by https://github.com/ppaanngggg/yolo-doclaynet
DocLayNet-base-lawdoclaynet10classesdoclaynet-smalldoclaynetTextClassDataset
Dataset Card for "doclaynetTextClassDataset"
More Information needed
arocrbench_doclaynetv2doclaynet_classlabel
Dataset Card for "doclaynet_classlabel"
More Information needed
DocLayNet-Instruct-v1DocLayNet-base-law_paragraphs_encoded_ml512doclaynet3classesdoclaynet_math