Datasets:

pierreguillou
/

DocLayNet-base

Tasks:

Object Detection

Image Segmentation

Token Classification

Modalities:

Sub-tasks:

instance-segmentation

Languages:

Size:

ArXiv:

Tags:

Financial-Reports

Libraries:

License:

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pierreguillou commited on Jan 28, 2023

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@@ -80,8 +80,8 @@ For all these reasons, I decided to process the DocLayNet dataset:
 - into 3 datasets of different sizes:
   - [DocLayNet small](https://huggingface.co/datasets/pierreguillou/DocLayNet-small) (about 1% of DocLayNet) < 1.000k document images (691 train, 64 val, 49 test)
   - [DocLayNet base](https://huggingface.co/datasets/pierreguillou/DocLayNet-base) (about 10% of DocLayNet) < 10.000k document images (6910 train, 648 val, 499 test)
-  - DocLayNet large with full dataset (to be done)
-- with associated texts,
 - and in a format facilitating their use by HF notebooks.
 *Note: the layout HF notebooks will greatly help participants of the IBM [ICDAR 2023 Competition on Robust Layout Segmentation in Corporate Documents](https://ds4sd.github.io/icdar23-doclaynet/)!*

 - into 3 datasets of different sizes:
   - [DocLayNet small](https://huggingface.co/datasets/pierreguillou/DocLayNet-small) (about 1% of DocLayNet) < 1.000k document images (691 train, 64 val, 49 test)
   - [DocLayNet base](https://huggingface.co/datasets/pierreguillou/DocLayNet-base) (about 10% of DocLayNet) < 10.000k document images (6910 train, 648 val, 499 test)
+  - [DocLayNet large](https://huggingface.co/datasets/pierreguillou/DocLayNet-large) (about 100% of DocLayNet) < 100.000k document images (69.103 train, 6.480 val, 4.994 test)
+- with associated texts and PDFs (base64 format),
 - and in a format facilitating their use by HF notebooks.
 *Note: the layout HF notebooks will greatly help participants of the IBM [ICDAR 2023 Competition on Robust Layout Segmentation in Corporate Documents](https://ds4sd.github.io/icdar23-doclaynet/)!*