Tibetan Modern Book Layout Detection RFDETR
Permissive (Apache-2.0) alternative in TiBLA (Tibetan Book Layout Analysis) — a lighter, PyTorch-native RF-DETR-L detector for the page layout of modern Tibetan books (headers, text area, footers, footnotes).
Model source
- Kind
- Other
- File size
- 132 MB
- Version
- vRevision 74d2991
- Updated
- Oct 10, 2026
- Downloads
- 0
- Source
- Hugging Face
Source description
TiBLA-RFDETR
Permissive (Apache-2.0) alternative in TiBLA (Tibetan Book Layout Analysis) — a lighter, PyTorch-native RF-DETR-L detector for the page layout of modern Tibetan books (headers, text area, footers, footnotes).
- Base model / provenance: RF-DETR-L
(Roboflow, DINOv2 backbone), fine-tuned on the leak-free v4
tam2colsplit of TiBLAD. - License: Apache-2.0.
- Dataset: BDRC/TiBLAD
- Paper: buda-base/papers (
papers/2026-tibetan-book-layout) — arXiv link forthcoming - Code: github.com/buda-base/tibla
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
# pip install rfdetr
from rfdetr import RFDETRLarge
model = RFDETRLarge.from_checkpoint("rfdetr_tibetan_book_layout.pth")
det = model.predict("page.jpg", threshold=0.47, shape=(1024, 1024))
# checkpoint class ids are offset by 1 (id 0 = background):
# 1 header, 2 text-area, 3 footnote, 4 footer
A ready-made infer.py (batch, YOLO-format output, per-class thresholds) is
included in this repo. Recommended global operating confidence: 0.47 (the
validation-selected best-mean-F1 point); the bundled infer.py also ships
per-class max-F1 thresholds (header 0.46, text-area 0.32, footnote 0.26,
footer 0.52).
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 [email protected] | 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.
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
@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}
}
Sources
1 source
Sources
1 sourceVerified Oct 10
Model artifacts
1 artifact
Model artifacts
1 artifactSource excerpts
3 excerpts
Source excerpts
3 excerpts--- license: apache-2.0 tags: - object-detection - document-layout-analysis - tibetan - rf-detr - tibla pipeline_tag: object-detection datasets: - BDRC/TiBLAD --- # TiBLA-RFDETR **Permissive (Apache-2.0) alternative in TiBLA (Tibetan Book Layout Analysis)** — a lighter, PyTorch-native RF-DETR-L detector for the page layout of modern Tibetan books (headers, text area, footers, footnotes). - **Base model / provenance:** [RF-DETR-L](https://github.com/roboflow/rf-detr) (Roboflow, DINOv2 backbone), fine-tuned on the leak-free **v4** `tam2col` split of TiBLAD. - **License:** Apache-2.0. - **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) ## 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 rfdetr from rfdetr import RFDETRLarge model = RFDETRLarge.from_checkpoint("rfdetr_tibetan_book_layout.pth") det = model.predict("page.jpg", threshold=0.47, shape=(1024, 1024)) # checkpoint class ids are offset by 1 (id 0 = background): # 1 header, 2 text-area, 3 footnote, 4 footer ``` A ready-made `infer.py` (batch, YOLO-format output, per-class thresholds) is included in this repo. Recommended global operating confidence: **0.47** (the validation-selected best-mean-F1 point); the bundled `infer.py` also ships per-class max-F1 thresholds (`header` 0.46, `text-area` 0.32, `footnote` 0.26, `footer` 0.52). ## 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-D...
--- license: apache-2.0 library_name: rfdetr pipeline_tag: object-detection language: - bo tags: - tibetan - document-layout-analysis - rf-detr - rfdetr - object-detection - bounding-box - BDRC datasets: - BDRC/TDLA-Training-Dataset-v2 metrics: - name: canonical mean AP50 (test) type: mAP value: 0.960 model-index: - name: Tibetan-Modern-Book-Layout-Detection-RFDETR results: - task: type: object-detection dataset: name: TDLA-Training-Dataset-v2 (test) type: BDRC/TDLA-Training-Dataset-v2 metrics: - type: mAP name: [email protected] (native, test) value: 0.996 - type: mAP name: [email protected]:0.95 (native, test) value: 0.813 --- # Tibetan Modern Book Layout Detection (RF-DETR-L) An **RF-DETR-L** ([Roboflow](https://github.com/roboflow/rf-detr), DINOv2 backbone) object detector that locates the four structural regions of a **modern Tibetan book** page — **header**, **text-area**, **footnote**, **footer** — as a preprocessing step for OCR and etext production. - **Training code, recipes & write-up:** [buda-base/tibetan-book-layout-analysis](https://github.com/buda-base/tibetan-book-layout-analysis) - **Dataset:** [BDRC/TDLA-Training-Dataset-v2](https://huggingface.co/datasets/BDRC/TDLA-Training-Dataset-v2) (gated, fair-use) > This is one of several architectures BDRC fine-tuned on the same labels and > recipe to test how much the choice of architecture matters (see the > [blog post](https://github.com/buda-base/tibetan-book-layout-analysis/blob/main/BLOGPOST.md)). > The primary, production release is the RT-DETR-l fine-tune at > [BDRC/Tibetan-Modern-Book-Layout-Detection-RTDETR](https://huggingface.co/BDRC/Tibetan-Modern-Book-Layout-Detection-RTDETR). > This RF-DETR-L checkpoint matches it almost exactly on every metric, and is > published as a **permissively-licensed (Apache-2.0) alternative** for anyone > who can't use the RT-DETR-l release's AGPL-derived training stack. ## Model description This is an RF-DETR-L fine-tuned with the **`tam2col`** labelling scheme: text-area boxes merged into one envelope per page, except on genuine two-column pages, where it keeps one box per column. Header and footer are kept as separate classes (they can be combined losslessly downstream). | Property | Value | | --- | --- | | Architecture | RF-DETR-L (via [`rfdetr`](https://github.com/roboflow/rf-detr), DINOv2-small windowed backbone) | | Task | Object detection | | Ba...
Source context: 0 downloads · 0 likes · Pipeline object-detection · Library rfdetr · Repo BDRC/Tibetan-Modern-Book-Layout-Detection-RFDETR
