BDRC/Tibetan_Modern_Book_Layout_Detection_Model

TMBLD-YOLO26m — Tibetan Modern book layout dection

A fine-tuned YOLO26m object-detection model for Tibetan Modern book layout dection. The model detects four layout classes in Tibetan modern book page images: header, Text area, footnote, and footer.

Model Description

This model was fine-tuned from the Ultralytics YOLO26m pretrained checkpoint on the BDRC/TDLA-Training-Dataset, a YOLO-format bounding-box dataset of Tibetan document pages sourced from the Buddhist Digital Resource Center (BDRC) digital library.

Classes

Performance

Evaluated on the validation split of the TDLA Training Dataset.

Training Loss (final epoch)

Training Details

Dataset

• —Dataset: BDRC/TDLA-Training-Dataset
• —Train images: 2,692
• —Val images: 103
• —Test images: 313
• —Total annotations: 14,705
• —Train/Val split: Iterative multi-label stratification (seed 42, 80/20 ratio)

Hyperparameters

Loss Weights

Augmentation

Usage

Inference with Ultralytics

from ultralytics import YOLO

model = YOLO("Tibetan_modern_book_Layout_detection.pt")

results = model.predict("page_image.jpg", imgsz=640)

for result in results:
    boxes = result.boxes
    for box in boxes:
        cls_id = int(box.cls)
        conf = float(box.conf)
        xyxy = box.xyxy[0].tolist()
        print(f"Class: {cls_id}, Confidence: {conf:.3f}, Box: {xyxy}")

Batch Inference

from ultralytics import YOLO

model = YOLO("Tibetan_modern_book_Layout_detection.pt")

results = model.predict("path/to/images/", imgsz=640, conf=0.25)

Intended Use

This model is designed for automatic layout detection of modern Tibetan book pages. It can be used as a preprocessing step for:

• —OCR pipelines on Tibetan documents
• —Document digitization workflows
• —Structured text extraction from scanned Tibetan texts
• —Digital library cataloging and indexing

Limitations

• —Trained primarily on modern Tibetan book layouts; performance on historical manuscripts, woodblock prints, or non-standard layouts may vary.
• —Optimized for 640×640 input resolution; very high-resolution pages may benefit from tiling or higher imgsz values.
• —The footnote class has fewer training samples (456 annotations) compared to other classes, which may affect detection quality for that class.

License

This model is released under the CC0 1.0 Universal (Public Domain Dedication). You are free to copy, modify, and distribute the model, even for commercial purposes, without asking permission.

Acknowledgements

This dataset was developed by Dharmaduta from specifications provided by the Buddhist Digital Resource Center (BDRC) for the BDRC Etext Corpus, with funding from the Khyentse Foundation.

Citation

If you use this model, please cite the dataset:

@software{bdrc_tmbld_yolo26m_2026,
  title   = {tmbld-YOLO26m: Tibetan Modern book layout detection Model},
  author  = {Buddhist Digital Resource Center (BDRC)},
  year    = {2026},
  url     = {https://huggingface.co/BDRC/TDLA-YOLO26m},
  license = {CC0-1.0}
}