| --- |
| license: cc0-1.0 |
| task_categories: |
| - object-detection |
| language: |
| - bo |
| tags: |
| - yolo |
| - tibetan |
| - document-layout-analysis |
| - bounding-box |
| pretty_name: TDLA Training Dataset |
| size_categories: |
| - 1K<n<10K |
| --- |
| |
| # TDLA Training Dataset |
|
|
| YOLO-format object-detection dataset for **Tibetan Document Layout Analysis (TDLA)**. The dataset contains bounding-box annotations for four layout classes found in Tibetan document page images. It is split into training, validation, and test sets. The train/val split uses iterative multi-label stratification, while the test set is a hand-picked benchmarking set of the most unique page layouts. |
|
|
| ## Overview |
|
|
| | Property | Value | |
| |---|---| |
| | **Total annotations** | 14705 | |
| | **Number of classes** | 4 | |
| | **Image format** | JPEG (`.jpg`) | |
| | **Label format** | YOLO (`.txt`) | |
| | **Splits** | train / val / test | |
| | **Train/Val stratification** | Iterative multi-label stratification (seed 42) | |
|
|
| ## Image Source |
|
|
| All images in this dataset are sourced from the [Buddhist Digital Resource Center (BDRC)](https://www.bdrc.io/) digital library. |
|
|
| ## Classes |
|
|
| | ID | Name | Annotations | % of total annotations | |
| |---|---|---|---| |
| | 0 | header | 4550 | 30.9% | |
| | 1 | Text area | 5844 | 39.7% | |
| | 2 | footnote | 456 | 3.1% | |
| | 3 | footer | 3854 | 26.2% | |
|
|
| ## Annotation Process |
|
|
| Annotations were created on the [Ultralytics HUB](https://hub.ultralytics.com/) platform using the following two-stage workflow: |
|
|
| 1. **Annotation** -- Annotators drew bounding boxes for each of the four layout classes (header, Text area, footnote, footer) on every page image. |
| 2. **Quality Control** -- A dedicated reviewer inspected each annotated image, verifying label correctness, box tightness, and class assignment before the annotation was accepted into the dataset. |
|
|
| ## Split Methodology |
|
|
| ### Train / Val |
|
|
| The training and validation sets were split at an **80/20 ratio** using **iterative multi-label stratification** (seed = 42). This approach treats each image as a multi-label sample (an image may contain several classes simultaneously) and iteratively assigns images to splits so that per-class proportions stay as close to the target ratio as possible. The result is a near-uniform 80/20 distribution for every class, as shown in the tables below. |
|
|
| ### Test (Benchmarking Set) |
|
|
| The test set was curated **independently** from the train/val split. Pages exhibiting the most unique and diverse layouts were **manually selected** from the source collection to maximize layout variety. Each selected page was then **manually annotated** following the same annotation guidelines used for the rest of the dataset. This hand-picked set serves as the **benchmarking dataset** — a fixed, high-quality reference for evaluating model performance on challenging and atypical page layouts. |
|
|
| ## Split Statistics |
|
|
| | Split | Images | |
| |---|---| |
| | train | 2692 | |
| | val | 103 | |
| | test | 313 | |
|
|
| ## Annotation Distribution per Split |
|
|
| | Class | train | val | test | Total | |
| |---|---|---|---|---| |
| | header | 3424 | 856 | 270 | 4550 | |
| | Text area | 4425 | 1107 | 312 | 5844 | |
| | footnote | 299 | 75 | 82 | 456 | |
| | footer | 2912 | 728 | 214 | 3854 | |
|
|
| > **Note:** A single image can contain multiple annotations of the same class, so annotation counts may exceed image counts. |
|
|
| ## Directory Structure |
|
|
| ``` |
| TDLA_Training_dataset/ |
| ├── images/ |
| │ ├── train/ |
| │ ├── val/ |
| │ └── test/ |
| ├── labels/ |
| │ ├── train/ |
| │ ├── val/ |
| │ └── test/ |
| ├── train.txt |
| ├── val.txt |
| ├── test.txt |
| ├── data.yaml |
| └── README.md |
| ``` |
|
|
| ## Usage |
|
|
| Point your YOLO training config to `data.yaml` in this directory: |
|
|
| ```bash |
| yolo detect train data=TDLA_Training_dataset/data.yaml |
| ``` |
|
|
| The `train.txt`, `val.txt`, and `test.txt` files list relative image paths for each split. |
|
|
| ## Label Format |
|
|
| Each `.txt` label file uses the standard YOLO format — one row per bounding box: |
|
|
| ``` |
| <class_id> <x_center> <y_center> <width> <height> |
| ``` |
|
|
| All coordinates are normalized to `[0, 1]` relative to image dimensions. |
|
|
| ## License |
|
|
| This dataset is released under the [CC0 1.0 Universal (Public Domain Dedication)](https://creativecommons.org/publicdomain/zero/1.0/). You are free to copy, modify, and distribute the data, even for commercial purposes, without asking permission. |
|
|
| ## Acknowledgements |
|
|
| This dataset was developed by [Dharmaduta](https://dharmaduta.org/) from specifications provided by the [Buddhist Digital Resource Center (BDRC)](https://www.bdrc.io/) for the BDRC Etext Corpus, with funding from the [Khyentse Foundation](https://khyentsefoundation.org/). |
|
|
