BEYOND_95 Implementation Status
5 quick-win strategy için kod + config + scaffolding tamamlandı. Hepsi opt-in, bağımsız, config-driven.
Implementation özet
| # | Strategy | Status | Files |
|---|---|---|---|
| 1 | End-to-end seq2seq paradigm | ✅ Code + 83 tablet pair | src/seq2seq/ + configs/seq2seq_hitit.yaml |
| 2 | 3D illumination augmentation | ✅ Code + test (5 tablet × 4 angle) | src/illum_aug/relighting.py |
| 3 | TLHdig photo-transliteration | ✅ 19.5K tablet ID + email template | src/tlhdig_integration/ |
| 4 | Image retrieval + exemplar | ✅ Code + template | src/retrieval/exemplar_matching.py |
| 5 | Active learning loop | ✅ BUAL + Label Studio/Prodigy | src/active_learning/al_loop.py |
Kritik kazanç noktaları
1. Seq2seq paradigm (en büyük potansiyel +%3-7)
Sonuç: 83 Hitit tablet için image-transliteration pair hazır.
datasets/processed/seq2seq_pairs.jsonl- Avg 344 sign/tablet, toplam 28,556 sign
- ViT encoder + ByT5 decoder model template:
src/seq2seq/model.py - Config:
configs/seq2seq_hitit.yaml
Next:
- Training SLURM job (GPU gerekli)
python3 src/seq2seq/train.py --config configs/seq2seq_hitit.yaml- SumTablets baseline chrF 0.9755
2. 3D Illumination augmentation (+%2-4 paper-validated)
Sonuç: 2D Lambertian relighting pipeline (3D mesh gerekmeden).
- Depth-from-shading proxy + Sobel normal map
- Per tablet 8 azimuth angle → 8× augment
- Test edildi: 5 tablet × 4 angle = 20 image
- Tam run: 307 × 8 = 2456 augmented image
Next:
python3 src/illum_aug/relighting.py --n-angles 8(background)
3. TLHdig entegrasyon
Sonuç: 19,549 unique Hitit tablet ID tespit edildi.
datasets/processed/tlhdig_tablet_ids.jsonl- Top tabletler: Privat 25, KUB 5.1+, StBoTB 1, Bo 86_299
- Email template:
datasets/processed/hpm_request_template.json
Next (kurumsal):
- Schwemer (Würzburg) ile contact
- CC-BY lisansı anlaşması
- HPM foto download pipeline aktivasyonu
- Tahmini 5K-20K tablet foto
4. Image retrieval + exemplar
Sonuç: FAISS-based ExemplarRetriever class.
- Gallery: hitit_local 21K crop
- Query → top-k neighbors + soft vote
- Leakage protection: same-tablet exemplars filtrelenir
- λ_exemplar=0.4 classifier fusion
Next:
- DINOv3-L ile 21K embedding (GPU job)
- Build gallery index
- Fuse with 198-class classifier at inference
5. Active learning loop
Sonuç: BUAL-based weekly iteration pipeline.
- BUAL score = αentropy + βdisagreement + γ*open_score
- Diverse sampling (KMeans cluster-wise top)
- Prodigy + Label Studio export formats
- 50 sample/hafta → 6 ayda 2000+ yeni
Next (kurumsal):
- Label Studio setup (ücretsiz) veya Prodigy (600€)
- Paleograph engagement (1 saat/hafta)
- Schedule weekly retrain
Cumulative gain projection (dürüst)
v4 baseline (full enhancements): 93-95%
+ Seq2seq paradigm shift: +3-7% → 96-98% ⭐ en büyük
+ 3D illumination augment: +2-4% → 97-98%
+ Exemplar retrieval (rare class): +1-3% → 97.5-98.5%
+ Active learning (6 ay birikimli): +1-3% → 98-99%
+ TLHdig photo integration: +1-3% → 98-99%
Theoretical ceiling: %98-99 (198 class Hitit). %99+ için: yıllar boyunca sustained data collection + expert loop.
Aktivasyon sıralaması
Hemen başlatılabilir (bugün)
# Illumination augmentation (CPU, 30-60 dk)
./hitit_ocr/scripts/beyond95_orchestration.sh
GPU job (SLURM)
# Exemplar gallery build + seq2seq training
sbatch hitit_ocr/scripts/pipeline/train_seq2seq.slurm # TODO
sbatch hitit_ocr/scripts/pipeline/build_exemplar_gallery.slurm # TODO
Kurumsal aksiyon (haftalar/aylar)
- Schwemer'e email (hpm_request_template.json) → TLHdig photo access
- Label Studio kurulum → active learning
- Paleograph angajman (ODTÜ/Ankara Üniversitesi)
- T.C. Kültür Bakanlığı başvurusu → müze envanter
Dosya haritası
hitit_ocr/
├── BEYOND_95.md # Strateji (13 yol)
├── BEYOND_95_IMPLEMENTATION.md # BU — status
├── configs/
│ └── seq2seq_hitit.yaml # Paradigm shift config
├── src/
│ ├── seq2seq/
│ │ ├── model.py # ViT + ByT5
│ │ └── build_pairs.py # image-transliteration extractor
│ ├── illum_aug/
│ │ └── relighting.py # 2D Lambertian multi-azimuth
│ ├── tlhdig_integration/
│ │ ├── scrape_photos.py # XML scanner
│ │ └── hpm_web_scrape.py # HPM URL generator
│ ├── retrieval/
│ │ └── exemplar_matching.py # FAISS gallery
│ └── active_learning/
│ └── al_loop.py # BUAL + export
└── scripts/
└── beyond95_orchestration.sh # Master runner
datasets/processed/
├── seq2seq_pairs.jsonl # 83 tablet
├── tlhdig_tablet_ids.jsonl # 19,549 Hittite tablet ID
├── tlhdig_photo_pairs.jsonl # 0 (TLHdig 0.2 yok)
├── illum_aug_map.json # augment log
├── hpm_request_template.json # Schwemer contact
├── hpm_scrape_targets.jsonl # (requires permission)
├── active_learning_recipe.json
└── tlhdig_download_recipe.json
Bilinen sınırlar
- TLHdig 0.2 fotoğraf yok: TLHdig 1.0 (Kasım 2025) veya HPM web scrape gerek
- Seq2seq training: 83 Hitit tablet yeter değil, pretraining (SumTablets 91K) lazım
- Exemplar retrieval: DINOv3 embedding çıkarımı GPU job gerek
- Active learning: Paleograph angajman organizasyonu
Referanslar
- SumTablets ACL ML4AL 2024 (chrF 97.55)
- Stötzner ICCVW 2023 (+%4 illumination aug)
- Kriege ICFHR 2021 (embedded attributes retrieval)
- Deng BUAL arXiv:2402.15198 (2024)
- Hethitologie-Portal Mainz (Schwemer, Würzburg)