STER code
Scripts run against the official 3dSAGER repo (BarGenossar/3dSAGER) checkout.
| file | purpose |
|---|---|
ster_repro.py |
Reproduce official matching pipeline (Bagging F1=0.946 on Hague-small). |
ster_diagnostic.py |
Headroom map: few-shot / long-tail / discrepancy strata. |
ster_fewshot.py |
Few-shot baseline landscape across 8 classifiers (bar-setting). |
crawl_3dbag_multilod.py |
Crawl 3DBAG multi-LoD (LoD1.2/1.3/2.2) via api.3dbag.nl. |
ster_crosslod.py |
Cross-LoD matching + zero-shot difficulty (run after crawl). |
ster_export_vectors.py |
Compute per-building 25-D vectors (multi-LoD + Hague) -> data/ster_wm_vectors.npz. |
ster_wm.py |
STER-WM: InfoNCE self-supervised identity encoder + few-shot fine-tune + hybrid (GPU). |
ster_fusion.py |
Rank-fusion of WM cosine + raw Bagging across K (GPU). |
ster_proto.py |
Few-shot prototype/metric head on WM relation vectors (GPU). |
ster_combo.py |
Sample-efficient linear head on [raw-ratio ⊕ WM-relation] + fusion (GPU). |
ster_zeroshot.py |
Zero-shot: train only on multi-LoD (0 target labels), transfer to Hague (GPU). |
ster_noisy.py |
Cross-LoD hard-negative matching (noisy regime) (GPU). |
ster_flow_v2.py |
Flow-Matching zero-shot (scale-invariant direction cosine); confirmed non-transfer (GPU). |
clip.py |
Stub so import clip succeeds (ViT baseline unused in matching). |
Dependencies: numpy, pandas, scikit-learn, scipy, xgboost, shapely, pyproj, faiss-cpu, torch. Pod (CPU) computes properties; GPU (torch 2.8+cu128) trains WM/Flow.