README.md

metadata

dataset_info:
  features:
    - name: image_name
      dtype: string
    - name: image
      dtype: image
    - name: obb
      dtype: string
  splits:
    - name: train
      num_bytes: 446206006
      num_examples: 3053
    - name: validation
      num_bytes: 101618505
      num_examples: 709
  download_size: 547824511
  dataset_size: 547824511
configs:
  - config_name: default
    data_files:
      - split: train
        path: data/dataset_with_images_obb_train.parquet
      - split: validation
        path: data/dataset_with_images_obb_val.parquet
license: cc-by-4.0
task_categories:
  - object-detection
language:
  - km
size_categories:
  - 1K<n<10K

Table Dataset - Image & OBB Annotation (Train/Val Split)

Dataset Overview

Table detection dataset with OBB (Oriented Bounding Box) annotations in YOLO format, split into training and validation sets.

• Total examples: 3762 image-annotation pairs
  • Train: 3053 (81.2%)
  • Validation: 709 (18.8%)
• Total size: 522.45 MB
• Language: Khmer (km)
• Document types: Table documents
• Annotation format: YOLO OBB (class cx cy w h)

Dataset Statistics

Split Information

Split	Examples	Size (MB)
Train	3053	425.54
Validation	709	96.91
Total	3762	522.45

Train/Val Ratio

• Train: 81%
• Validation: 19%

Features

Feature	Type	Description
image_name	string	Document image filename (without extension)
image	image (bytes)	PNG image binary data
obb	string	OBB YOLO annotations (class cx cy w h per line)

Class Names

ID	Name
0	cell
1	column
2	header
3	row

Data Format

Image

PNG binary data — convert to PIL Image for processing:

from PIL import Image
from io import BytesIO

image_bytes = row['image']['bytes']   # HF datasets raw access
image = Image.open(BytesIO(image_bytes))

OBB TXT (string)

YOLO format: one detection per line — class_id cx cy w h (all normalised 0-1):

obb_text = row['obb']
for line in obb_text.strip().splitlines():
    parts = line.split()
    class_id = int(parts[0])
    cx, cy, w, h = map(float, parts[1:])

Usage Examples

Load with pandas

import pandas as pd

df_train = pd.read_parquet('data/dataset_with_images_obb_train.parquet')
df_val   = pd.read_parquet('data/dataset_with_images_obb_val.parquet')
print(f"Train: {len(df_train)}, Val: {len(df_val)}")

Load with Hugging Face Datasets

from datasets import load_dataset

dataset = load_dataset('parquet', data_files={
    'train':      'data/dataset_with_images_obb_train.parquet',
    'validation': 'data/dataset_with_images_obb_val.parquet',
})

Access a single row

from PIL import Image
from io import BytesIO

row = df_train.iloc[0]

image = Image.open(BytesIO(row['image']['bytes']))
print(image.size)          # (width, height)
print(row['image_name'])   # filename stem
print(row['obb'])          # raw YOLO OBB text

File Summary

File	Rows	Size (MB)
dataset_with_images_obb_train.parquet	3053	425.54
dataset_with_images_obb_val.parquet	709	96.91

Citation

@dataset{table_dataset_obb_2026,
  title={Table Dataset - Image & OBB Annotations (Train/Val Split)},
  year={2026},
  note={Table detection dataset with YOLO OBB annotations}
}

License

CC-BY-4.0

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Last Updated: 2026-06-01 Dataset Version: 1.0 Total Examples: 3762 Total Size: 522.45 MB