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LibreYOLO/tabular-data-wf9uh

Tabular Data Wf9Uh This dataset is part of the Roboflow 100 benchmark, a diverse collection of 100 object detection datasets spanning 7 imagery domains. Dataset Statistics Split Images Train 3,251 Validation 409 Test 206 Total 3,866 Classes (12) bold_parent_row bold_row closure_row column direct_children non_bold_parent_row non_bold_row parent_column prime_parent sub_row table Usage With… See the full description on the dataset page: https://huggingface.co/datasets/LibreYOLO/tabular-data-wf9uh.

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Tabular Data Wf9Uh

This dataset is part of the Roboflow 100 benchmark, a diverse collection of 100 object detection datasets spanning 7 imagery domains.

Dataset Description

—Source: Roboflow 100
—Category: Documents
—License: CC-BY-4.0
—Format: YOLO (LibreYOLO compatible)
—Mirrored on: 2026-01-20

Dataset Statistics

Split	Images
Train	3,251
Validation	409
Test	206
Total	3,866

Classes (12)

—-
—boldparentrow
—bold_row
—closure_row
—column
—direct_children
—nonboldparent_row
—nonboldrow
—parent_column
—prime_parent
—sub_row
—table

Usage

With LibreYOLO

python

from libreyolo import LIBREYOLO

# Load a model
model = LIBREYOLO(model_path="libreyoloXnano.pt")

# Train on this dataset
model.train(data='path/to/data.yaml', epochs=100)

Download from HuggingFace

python

from huggingface_hub import snapshot_download

# Download the dataset
snapshot_download(
    repo_id="Libre-YOLO/tabular-data-wf9uh",
    repo_type="dataset",
    local_dir="./tabular-data-wf9uh"
)

Directory Structure

tabular-data-wf9uh/
├── data.yaml           # Dataset configuration
├── README.md           # This file
├── train/
│   ├── images/         # Training images
│   └── labels/         # Training labels (YOLO format)
├── valid/
│   ├── images/         # Validation images
│   └── labels/         # Validation labels
└── test/
    ├── images/         # Test images (if available)
    └── labels/         # Test labels

Label Format

Labels are in YOLO format (one .txt file per image):

<class_id> <x_center> <y_center> <width> <height>

All coordinates are normalized to [0, 1].

Citation

If you use this dataset, please cite the Roboflow 100 benchmark:

bibtex

@misc{rf100_2022,
    Author = {Floriana Ciaglia and Francesco Saverio Zuppichini and Paul Guerrie and Mark McQuade and Jacob Solawetz},
    Title = {Roboflow 100: A Rich, Multi-Domain Object Detection Benchmark},
    Year = {2022},
    Eprint = {arXiv:2211.13523},
}

License

This dataset is released under the CC-BY-4.0 license. Please check the original source for any additional terms.

Acknowledgments

—Original dataset from Roboflow Universe
—Part of the Roboflow 100 Benchmark
—Sponsored by Intel