---
dataset_info:
  features:
  - name: id
    dtype: string
  - name: image
    dtype: image
  - name: annotations
    list:
      struct:
      - name: category_id
        dtype: int64
      - name: bbox
        list:
          dtype: float32
      - name: area
        dtype: float32
      - name: iscrowd
        dtype: int64
      - name: id
        dtype: int64
      - name: image_id
        dtype: int64
      - name: segmentation
        list:
          list:
            dtype: float32
  splits:
  - name: train
    num_bytes: 155800000
    num_examples: 500
  download_size: 155800000
  dataset_size: 155800000
configs:
- config_name: default
  data_files:
  - split: train
    path: publaynet_mini.parquet
language:
- en
license: mit
task_categories:
- object-detection
task_ids:
- object-detection
pretty_name: PubLayNet Mini
size_categories:
- n<1K
tags:
- document-layout-analysis
- document-understanding
- layout-detection
- academic-papers
- research
---

# PubLayNet_mini Dataset

A diverse mini subset of the PubLayNet dataset with 500 samples for document layout analysis evaluation.

## Dataset Details

- **Total Samples**: 500 document images
- **Source**: PubLayNet training set (146,874 total samples)
- **Task**: Document Layout Analysis
- **Format**: Parquet with embedded images and annotations
- **Image Size**: 612×792 pixels (RGB)
- **Categories**: 5 layout element types

## Categories

The dataset contains annotations for 5 categories of document layout elements:

1. **Text** (1): Regular text blocks and paragraphs
2. **Title** (2): Document titles and headings  
3. **List** (3): Bulleted or numbered lists
4. **Table** (4): Tabular data structures
5. **Figure** (5): Images, charts, and diagrams

## Features

Each sample contains:
- `id`: Unique document identifier
- `image`: Document image (PIL Image) - automatically loaded from embedded bytes
- `annotations`: List of layout element annotations with:
  - `category_id`: Element type (1-5)
  - `bbox`: Bounding box coordinates [x, y, width, height]
  - `area`: Area of the bounding box
  - `iscrowd`: Whether the annotation is for a crowd of objects
  - `id`: Unique annotation identifier
  - `image_id`: Reference to the document image
  - `segmentation`: Polygon segmentation mask

## Data Storage

Images are stored as embedded bytes in the parquet file and automatically converted to PIL Images when loaded. This ensures:
- Self-contained dataset (no external image dependencies)
- Fast loading and processing
- Compatibility with HuggingFace datasets library

## Category Distribution

This subset maintains diverse representation across categories:
- Text: ~3,676 annotations
- Title: ~1,000 annotations
- List: ~73 annotations
- Table: ~128 annotations
- Figure: ~172 annotations

## Usage

```python
from datasets import load_dataset

# Load the dataset
dataset = load_dataset("kenza-ily/publaynet-mini")

# Each sample contains:
for sample in dataset['train']:
    print(f"Document ID: {sample['id']}")
    print(f"Number of layout elements: {len(sample['annotations'])}")
    
    # Access the image (automatically converted to PIL Image)
    image = sample['image']  # PIL Image object
    print(f"Image size: {image.size}")
    
    # Access annotations
    for ann in sample['annotations']:
        category = ann['category_id']
        bbox = ann['bbox']
        segmentation = ann['segmentation']
        print(f"Element {category}: bbox={bbox}")
```

## Loading from Parquet

You can also load the data directly from the parquet file:

```python
import pyarrow.parquet as pq
import pandas as pd
from PIL import Image as PILImage
import io

# Read parquet file
table = pq.read_table("publaynet_mini.parquet")
df = table.to_pandas()

# Convert images from bytes to PIL Images
def convert_image(img_data):
    if isinstance(img_data, dict) and 'bytes' in img_data:
        img_bytes = img_data['bytes']
        return PILImage.open(io.BytesIO(img_bytes))
    return img_data

df['image'] = df['image'].apply(convert_image)

# Access data
for idx, row in df.iterrows():
    image = row['image']  # PIL Image
    annotations = row['annotations']  # List of dicts
```

## Citation

Please cite the original PubLayNet paper if you use this subset:

@article{zhong2019publaynet,
  title={PubLayNet: largest dataset ever for document AI},
  author={Zhong, Xu and Tang, Jianbin and Yepes, Antonio Jimeno},
  journal={arXiv preprint arXiv:1908.07836},
  year={2019}
}

## License

This subset follows the original PubLayNet dataset license.