Datasets:

Libre-YOLO
/

tabular-data-wf9uh

license: cc-by-4.0
task_categories:
  - object-detection
tags:
  - roboflow
  - roboflow-100
  - rf100
  - yolo
  - libreyolo
  - documents
  - computer-vision
  - bounding-box
pretty_name: Tabular Data Wf9Uh
size_categories:
  - 1K<n<10K
dataset_info:
  features:
    - name: image
      dtype: image
    - name: label
      dtype: string
  splits:
    - name: train
      num_examples: 3251
    - name: validation
      num_examples: 409
    - name: test
      num_examples: 206

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)

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 LibreYOLO

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

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:

@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