| # v20 training — T35: full prototypes + L_deg on SD302. | |
| # | |
| # Root cause analysis (v19 failure — same collapse as v15–v18): | |
| # | |
| # v19 was supposed to reproduce v14 exactly but STILL collapsed (q_std≈0.01 | |
| # at inference, all scores 53.1x). This means the "v14 hyperparameter diff" | |
| # explanation (spread-weight and deg-every-n-steps) was wrong. Two deeper root | |
| # causes identified by analysing every code change since v14: | |
| # | |
| # Root cause 1 — Truncated prototypes (T35a): | |
| # v14's code had NO --proto-max-batches cap; prototypes were computed on the | |
| # full dataset (42,683 images, ~334 batches). When this parameter was added | |
| # (default 150 batches = ~19,200 images, 45% of data), SD302 identities went | |
| # from having full 19-sensor multi-sensor prototypes to 2–3 sensor partial | |
| # prototypes. | |
| # Full prototype → raw cosine varies with image quality across all sensors | |
| # (quality-discriminative, sensor-invariant). | |
| # Partial proto → raw cosine correlated with which sensors are in the | |
| # prototype window (sensor-biased, quality-agnostic) → | |
| # no per-image quality gradient for SD302 → collapse. | |
| # | |
| # Root cause 2 — FVC-only L_deg (T27, unchanged since v13): | |
| # T27 reverted T17 (L_deg on all images) because clean SD302 images anchored | |
| # at ~28 when there was no per-dataset spread. T27 itself introduced | |
| # per-dataset L_spread_ds. Now that L_spread_ds forces SD302 to span [10,90], | |
| # re-enabling full L_rank for SD302 is safe: L_spread_ds prevents the | |
| # anchoring, and L_rank orders SD302 images by their synthetic degradation | |
| # response (a proxy for ridge clarity / quality). This gives the ONLY stable | |
| # per-image quality signal for SD302 at inference. | |
| # | |
| # v20 fixes (T35): | |
| # T35a — --proto-max-batches 0: Compute prototypes on the full training set. | |
| # Full multi-sensor prototypes restore per-image L_mat quality signal | |
| # for SD302 (raw cosine varies with quality, not sensor). | |
| # Cost: ~10–15 extra minutes at epoch 0 for prototype computation. | |
| # | |
| # T35b — --deg-include-sd302: Apply full L_deg (L_rank + L_concept) to SD302 | |
| # images in addition to FVC. L_spread_ds (present since v13) prevents | |
| # score anchoring at ~28. Provides robust per-image ordinal quality | |
| # grounding for SD302 independent of root cause 1. | |
| # | |
| # All other settings kept from v19 (which correctly reproduced v14 hyperparams): | |
| # --spread-weight 3.0 --deg-every-n-steps 2 --no-mat-stats | |
| # --concept-deg-gamma 0.5 --sd302-concept-weight 0.0 | |
| # --batch-size 128 --gpus 0 --epochs 60 | |
| # | |
| # Expected improvements vs v19: | |
| # q_std (inference) : >12 (v19: ~0.01) | |
| # Score range : 10–90 (v19: 53.10–53.16) | |
| # Track 2 mean_KS : ≤0.27 real (v19: 0.3706 with collapsed distributions) | |
| # Track 2 Pearson : ≥0.25 (v19: -0.0123) | |
| set -euo pipefail | |
| REPO_ROOT="$(cd "$(dirname "$0")/../.." && pwd)" | |
| export PATH="/home/aiserver/miniconda3/bin:$PATH" | |
| VERSION="v20" | |
| SAVE_DIR="${REPO_ROOT}/sifq/checkpoints/${VERSION}" | |
| LOG_FILE="${REPO_ROOT}/sifq/logs/train_${VERSION}.log" | |
| EVAL_SCRIPT="${REPO_ROOT}/sifq/scripts/run_eval_${VERSION}.sh" | |
| mkdir -p "${REPO_ROOT}/sifq/logs" | |
| python "${REPO_ROOT}/sifq/scripts/train_sifq.py" \ | |
| --root-302a "${REPO_ROOT}/dataset/302a/images/challengers" \ | |
| --root-302b "${REPO_ROOT}/dataset/302b/images/baseline" \ | |
| --root-302d "${REPO_ROOT}/dataset/nist_302d/images/auxiliary" \ | |
| --root-fvc2002 "${REPO_ROOT}/dataset/FVC_Dataset/FVC2002" \ | |
| --root-fvc2004 "${REPO_ROOT}/dataset/FVC_Dataset/FVC2004" \ | |
| --root-polyu "${REPO_ROOT}/dataset/PolyU" \ | |
| --exclude-sensor "R_1000_slap,R_500_slap,S_500_slap" \ | |
| --mdgt-checkpoint "${REPO_ROOT}/pad/TRAM-downstream/checkpoint/checkpoints_dinov2_tram/best_eer.pt" \ | |
| --epochs 60 \ | |
| --batch-size 128 \ | |
| --image-size 224 \ | |
| --lr 1e-4 \ | |
| --spread-mode uniform \ | |
| --spread-weight 3.0 \ | |
| --concept-deg-gamma 0.5 \ | |
| --sd302-concept-weight 0.0 \ | |
| --deg-every-n-steps 2 \ | |
| --deg-include-sd302 \ | |
| --no-mat-stats \ | |
| --proto-max-batches 0 \ | |
| --max-train-samples -1 \ | |
| --num-workers 8 \ | |
| --gpus 0 \ | |
| --save-dir "${SAVE_DIR}" | |
| echo "[auto-eval] Training done. Starting eval ${VERSION}..." | |
| bash "${EVAL_SCRIPT}" >> "${LOG_FILE}" 2>&1 | |