dfine-det-large-baseline-hebrew-samaritan-stage1

Stage-1 product fine-tune of dfine-det — D-FINE Large (HGNetv2-B4) predicting text-line baselines as polylines (B-spline control points) for medieval Hebrew and Samaritan manuscripts (PAGE-XML).

Hub repo: johnlockejrr/dfine-det-large-baseline-hebrew-samaritan-stage1
Primary file: best_cbad_f1.safetensors (~116 MB)
SHA256: ef7c32254905d72f1768ba2043cf092b5b040e5643fba83fba2bbbc13b70643d

Init chain: Peterande/D-FINE dfine_l_obj2coco → johnlockejrr/dfine-det-large-baseline-stage0 (cbad_f1_max ≈ 0.893) → this Stage-1.

Model summary

Detection is set prediction of scored polylines. Polygonal line environments (BLLA) are applied only at PAGE/ALTO serialize time, not in the training objective.

Intended use

Use for

• Baseline detection on Hebrew and Samaritan manuscript pages (PAGE-XML workflows)
• Downstream HTR / layout pipelines that need reliable text-line geometry
• Research on Stage-0 → Stage-1 transfer for script-specific corpora

Not for

• Official ICDAR cBAD 2019 bake-offs (wrong track / domain)
• Production OCR transcription (detects lines; does not recognize text)
• Replacing layout region detectors (paragraphs, tables, etc.)

Training pipeline

Peterande D-FINE-L (Objects365→COCO)
        │
        ▼
Stage-0 multiscript polyline pretrain
  (~46.5k train / 1.9k val; cbad_f1_max ≈ 0.893 @ ~ep39)
  Hub: johnlockejrr/dfine-det-large-baseline-stage0
        │
        ▼
Stage-1 Hebrew + Samaritan fine-tune  ← this release
  (1758 train / 261 val; monitor peak cbad_f1_max ≈ 0.9599 @ ep109)

Config: configs/hebrew_samaritan_stage1.yaml.

Fine-tuning data (Stage-1)

PAGE-XML baselines from:

Split policy (seed 42; see configs/hebrew_samaritan_combined_split.json):

• Samaritan: manuscript-level holdout (~12% pages target) — 43 train MSS / 7 val MSS (no page from a val MS appears in train).
• Hebrew: page-level split within Hebrew_Medieval-seg.

Compiled to Arrow (simplify_eps=0.01 → B-spline (K=8)) under outputs/hebrew_samaritan_combined/arrow/.

Training recipe (Stage-1)

Evaluation

Metric: cBAD-style line F1 — Hungarian matching of densified polylines with mean bidirectional Chamfer cost; TP if cost ≤ dist_thresh (20 px).

Operating point (recommended)

Lock conf = 0.50 using the unseen Hebrew holdout (not the Stage-1 val peak). On Stage-1 val, 0.50 is within ~0.0015 F1 of the val-best conf (0.40).

1) Unseen Hebrew holdout (primary report)

Source: /opt/DATA/DATASETS/multi-export-hebrew (674 PAGE pages).
Excluded any page already in Stage-1 train/val (exact basename) or Stage-0 lists (exact / post-__ stem) → 377 pages.
List: configs/multi_export_hebrew_unseen_test.lst.

dfine-det --config configs/hebrew_samaritan_stage1.yaml -d cuda:0 test \
  --weights best_cbad_f1.safetensors \
  --val-arrow outputs/hebrew_samaritan_stage1/arrow_unseen_test/val.arrow \
  --conf 0.50

Headline (unseen @ 0.50): F1 0.898 · P 0.932 · R 0.866

2) Stage-1 in-domain val (early-stop split)

261 pages (hebrew_samaritan_combined val Arrow). Optimistic vs true holdout.

Train monitor cbad_f1_max ≈ 0.9599 @ ep109 (best across the same conf grid during training).

Caveats

• Unseen set is Hebrew multi-export pages not in Stage-0/1 lists; it is not a Samaritan-only test and not manuscript-disjoint vs every possible Hebrew source.
• Stage-1 val F1 is not interchangeable with unseen F1 (~0.95 vs ~0.90).
• Fixed conf 0.1 (train log cbad_f1) is far below operating F1 — always sweep or use 0.50.

How to use

Install

pip install -e ".[dev]"   # from the dfine-det repo; see QUICKSTART for torch/CUDA notes

Inference (PAGE XML)

dfine-det -d cuda:0 infer \
  --weights best_cbad_f1.safetensors \
  --image page.jpg \
  -o page.xml \
  --format page \
  --conf 0.5 \
  --reading-order rtl

Evaluate

dfine-det --config configs/hebrew_samaritan_stage1.yaml -d cuda:0 test \
  --weights best_cbad_f1.safetensors \
  --val-arrow path/to/val.arrow \
  --conf 0.50

Files in this release

Upload the safetensors (+ this README) for Hub users; Lightning .ckpt is optional.

Limitations

• Domain gap remains between Stage-1 val (~0.95 F1) and unseen Hebrew (~0.90 F1).
• (K=8) control points underfit strongly curved / damaged lines.
• Vertical “ghost” doubles can still appear on some layouts; cost_y / loss_height mitigate but do not eliminate them.
• Private / institutional manuscript images used in training are not redistributed with the weights; respect each corpus license.
• Do not cite these scores as cBAD 2019 / Orli bake-off results.

Citation & credits

D-FINE (architecture & COCO/Objects365 pretrain)

@inproceedings{peng2025dfine,
  title     = {D-FINE: Redefine Regression Task in DETRs as Fine-grained Distribution Refinement},
  author    = {Peng, Yansong and Li, Hebei and Wu, Peixi and Zhang, Yueyi and Sun, Xiaoyan and Wu, Feng},
  booktitle = {The Thirteenth International Conference on Learning Representations},
  year      = {2025},
  url       = {https://arxiv.org/abs/2410.13842}
}

• Code: github.com/Peterande/D-FINE
• Weights: huggingface.co/Peterande/D-FINE

Stage-0 polyline pretrain

@software{dfine_det_stage0_large,
  title  = {dfine-det Large Stage-0: Multiscript Polyline Baseline Pretrain},
  author = {John Locke Jrr, johnlockejrr},
  year   = {2026},
  url    = {https://huggingface.co/johnlockejrr/dfine-det-large-baseline-stage0}
}

This Stage-1 Hebrew/Samaritan fine-tune

@software{dfine_det_hebsam_stage1,
  title  = {dfine-det Large Stage-1: Hebrew and Samaritan Polyline Baseline Detection},
  author = {John Locke Jrr, johnlockejrr},
  year   = {2026},
  note   = {Fine-tuned from johnlockejrr/dfine-det-large-baseline-stage0; operating conf 0.50},
  url    = {https://huggingface.co/johnlockejrr/dfine-det-large-baseline-hebrew-samaritan-stage1}
}

Additional notices

• Vendored D-FINE modules: see THIRD_PARTY_NOTICES.md (Apache-2.0, © 2024 The D-FINE Authors).
• PAGE polygonalization helpers derive from Kraken/BLLA (Apache-2.0, © Benjamin Kiessling) — used at export only.
• Training corpora: follow each dataset’s original license / institutional terms.

License

Apache License 2.0 for the dfine-det code and these weights, consistent with D-FINE’s Apache-2.0 release. Downstream users must also comply with licenses of any datasets used in further fine-tuning.