SommaireTOC Region Proposal — PP-DocLayout-L (gated)
Fine-tuned PaddlePaddle/PP-DocLayout-L for document region proposal on French Bulletin Officiel (BO) pages, used in the SommaireTOC pipeline (SOMMAIRE / TOC extraction).
Gated model — trained on proprietary/internal BO annotations. Inference export from run
sommaire_proposal_new_dataset_08-08-2026.
Model description
| Property | Value |
|---|---|
| Architecture | PP-DocLayout-L (DETR), Paddle inference |
| Base model | PaddlePaddle/PP-DocLayout-L |
| Task | Multi-class document layout / region proposal |
| Metric (selection) | COCO bbox mAP |
| Best checkpoint | Epoch 16, mAP 0.7039 |
| Input size | 640 × 640 |
| Framework | PaddlePaddle / PaddleOCR LayoutDetection |
Label taxonomy (23 PP-DocLayout classes)
paragraph_title, image, text, number, abstract, content, figure_title, formula, table, table_title, reference, doc_title, footnote, header, algorithm, footer, seal, chart_title, chart, formula_number, header_image, footer_image, aside_text
Training data
| Item | Value |
|---|---|
| Pages | 775 |
| Documents | 155 |
| Domain | French Bulletin Officiel (SOMMAIRE / front-matter heavy) |
| Run id | sommaire_proposal_new_dataset_08-08-2026 |
Training / checkpoint selection
| Item | Value |
|---|---|
| Best val COCO mAP | 0.703857 |
| Best epoch | 16 |
| Export | best_model/inference (inference.pdiparams + inference.json + inference.yml) |
Full training ledger: metrics/train_result.json
Best info: metrics/best_model.info.json
Evaluation results
Validation (training selection)
| Metric | Value |
|---|---|
| Best COCO mAP | 0.7039 |
| Best epoch | 16 |
Held-out page eval (n=120) vs pretrained baseline
From metrics/eval_metrics.json:
| Metric | Finetuned | Baseline |
|---|---|---|
| Macro F1 | 0.9865 | (see JSON) |
| Weighted F1 | 0.9874 | (see JSON) |
| Mean matched IoU | 0.8378 | (see JSON) |
Per-class F1 (finetuned, matched evaluation)
| Class | Precision | Recall | F1 | Support |
|---|---|---|---|---|
| header | 0.984 | 0.974 | 0.979 | 190 |
| paragraph_title | 0.991 | 0.997 | 0.994 | 921 |
| reference | 0.990 | 1.000 | 0.995 | 96 |
| text | 0.961 | 0.997 | 0.978 | 593 |
Plots
Sample detections
Example pages
Usage (PaddleOCR)
from huggingface_hub import snapshot_download
from paddleocr import LayoutDetection
model_dir = snapshot_download(
"AvoCahDoe/sommaire-pp-doclayout-l-proposal",
allow_patterns=["inference/*"],
)
# Hub layout keeps weights under inference/
det = LayoutDetection(
model_dir=f"{model_dir}/inference",
model_name="PP-DocLayout-L",
enable_mkldnn=False,
)
output = det.predict("page.png", layout_nms=True, threshold=0.5)
for res in output:
data = res.json if hasattr(res, "json") else {}
for box in data.get("res", data).get("boxes", []):
print(box.get("label"), box.get("score"), box.get("coordinate"))
Repository layout
inference/ # Exported Paddle inference model (use this at runtime)
metrics/ # train_result, best_model.info, eval_metrics
plots/ # training / validation curves
samples/ # detection overlay visualizations
examples/ # raw BO page examples
Intended use
- Region proposal stage of SommaireTOC on French BO PDFs
- Upstream of LayoutLMv3 segment classification (
AvoCahDoe/sommaire-layoutlmv3-classifier)
Limitations
- Tuned for Moroccan/French Bulletin Officiel layouts; other document types may need further finetuning
- Private weights; do not redistribute without authorization
- Stock PP-OCR det/rec are not included (use separately)
Citation
If you use this model in work derived from the SommaireTOC pipeline, please cite the Hub repo and base model PaddlePaddle/PP-DocLayout-L.