---
license: agpl-3.0
tags:
  - object-detection
  - document-layout-analysis
  - tibetan
  - rt-detr
  - tibla
library_name: ultralytics
pipeline_tag: object-detection
datasets:
  - BDRC/TiBLAD
---

# TiBLA-RTDETR

**Primary checkpoint of TiBLA (Tibetan Book Layout Analysis)** — an RT-DETR-l
detector for the page layout of modern Tibetan books (headers, text area,
footers, footnotes).

- **Base model / provenance:** [RT-DETR-l](https://docs.ultralytics.com/models/rtdetr/)
  (Ultralytics), fine-tuned on the leak-free **v4** `tam2col` split of TiBLAD.
- **License:** AGPL-3.0 (inherited from the Ultralytics RT-DETR weights).
- **Dataset:** [BDRC/TiBLAD](https://huggingface.co/datasets/BDRC/TiBLAD)
- **Paper:** [buda-base/papers](https://github.com/buda-base/papers) (`papers/2026-tibetan-book-layout`) — *arXiv link forthcoming*
- **Code:** [github.com/buda-base/tibla](https://github.com/buda-base/tibla)

> This checkpoint is **seed 0**. Across five training seeds the paper reports
> mean canonical F1 **0.961 ± 0.009** (unified scorer, per-seed operating point);
> at the validation-selected operating point used in the table below this seed
> scores **0.952**.

## Task

A **4-class** detector — `header`, `text-area`, `footer`, `footnote` — kept as
four classes at training time. Evaluation folds them into a **3-class canonical
scheme**: `header`+`footer` are combined into one `header-footer` class (matched
individually, merged losslessly afterwards), `text-area` is merged to a single
page/column envelope as a post-processing step (two boxes only on genuine
two-column pages), and `footnote` is left as-is. All numbers below are in that
canonical space, on the leak-free TiBLAD **v4** 833-page test set, unified scorer
(pycocotools bbox mAP 0.50:0.05:0.95; F1 by greedy IoU≥0.5 at the best-mean-F1
operating point).

## Inference

```python
# pip install ultralytics
from ultralytics import RTDETR

model = RTDETR("tibetan_book_layout.pt")
# recommended per-class confidence thresholds (see below); predict at the floor
res = model.predict("page.jpg", imgsz=1024, conf=0.25)[0]
TH = {0: 0.60, 1: 0.55, 2: 0.25, 3: 0.60}  # header / text-area / footnote / footer
for cls, conf, xywhn in zip(res.boxes.cls.tolist(), res.boxes.conf.tolist(),
                            res.boxes.xywhn.tolist()):
    if conf >= TH[int(cls)]:
        print(res.names[int(cls)], round(conf, 3), [round(v, 4) for v in xywhn])
```

A ready-made `infer.py` (batch, YOLO-format output) is included in this repo.

**Recommended confidence thresholds** (per-class max-F1 operating points):
`header`/`footer` ≈ **0.60**, `text-area` ≈ **0.55**, `footnote` ≈ **0.25**.
Footnote is deliberately kept low (recall-safe): the v4 test has only 38 footnote
GT boxes, so a low threshold keeps recall near 1.0. Raising `header`/`footer` from
0.25 to 0.60 lifts precision +0.028 for a −0.014 recall cost; raising `text-area`
from 0.25 to 0.55 (native) lifts precision +0.007 at no recall cost. If you prefer
one global knob, the single validation-selected best-mean-F1 confidence is **0.64**
(costs ≈0.008 mean F1 vs per-class tuning).

## Evaluation (TiBLAD v4, 833-page test)

| metric | **TiBLA-RTDETR** | TiBLA-PP-DocLayout-L | TiBLA-RFDETR |
|---|---|---|---|
| license | **AGPL-3.0** | Apache-2.0 | Apache-2.0 |
| base model | **RT-DETR-l (Ultralytics)** | PP-DocLayout-L (PaddleOCR, RT-DETR-L) | RF-DETR-L (Roboflow) |
| mean F1 (canonical 3-class) | **0.952** | 0.955 | 0.921 |
|   header-footer F1 | **0.954** | 0.953 | 0.947 |
|   text-area F1 | **0.999** | 0.998 | 0.996 |
|   footnote F1 | **0.902** | 0.914 | 0.821 |
| mean AP@0.50 | **0.974** | 0.959 | 0.925 |
| mean AP@[0.50:0.95] | **0.786** | 0.781 | 0.667 |
| shared-class mAP@[.50:.95] (DocLayNet-aligned) | **0.650** | 0.641 | 0.604 |
| Hidden Trespass — header/footer | **0.009** | 0.004 | 0.021 |
| Hidden Trespass — footnote | **0.043** | 0.037 | 0.178 |
| COTe (Trespass) | **0.975 (0.001)** | 0.978 (0.000) | 0.974 (0.002) |
| operating confidence | **0.64** | 0.61 | 0.47 |

*"operating confidence" is the single global best-mean-F1 confidence, selected on
the leak-free validation split and frozen for test (no test-set tuning). COCO AP
rows are threshold-free (all detections above the fixed 0.05 floor).*

**Hidden Trespass** = peripheral (header/footer/footnote) ground-truth **area**
that survives in the *actual OCR body crop* `C = E \ P`, where `E` is the predicted
`text-area` envelope and `P` is the union of the predicted peripheral boxes the
pipeline subtracts; area-based, micro-averaged over the test set. Lower is better
(less peripheral text bled into the OCR region). Formal definition in the
[paper](https://github.com/buda-base/papers).

## Which checkpoint to pick

| checkpoint | license | mean F1 | shared mAP | footnote HT |
|---|---|---|---|---|
| **TiBLA-RTDETR** (primary) | AGPL-3.0 | 0.952 | 0.650 | 0.043 |
| TiBLA-PP-DocLayout-L | Apache-2.0 | 0.955 | 0.641 | 0.037 |
| TiBLA-RFDETR | Apache-2.0 | 0.921 | 0.604 | 0.178 |

RT-DETR-l leads on mAP, shared-class mAP and the 5-seed mean F1 (0.961 ± 0.009),
but its weights are AGPL-3.0 (Ultralytics). If you need a permissive license,
PP-DocLayout-L is an Apache-2.0 match (statistically on par on F1); RF-DETR is a
lighter PyTorch-native Apache-2.0 option.

## Citation

```bibtex
@misc{tibla2026,
  title        = {TiBLA: Tibetan Book Layout Analysis},
  author       = {Buddhist Digital Resource Center (BDRC)},
  year         = {2026},
  howpublished = {\url{https://github.com/buda-base/tibla}},
  note         = {arXiv link forthcoming}
}
```