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SIFQ — Hướng dẫn chạy

Tất cả lệnh chạy từ thư mục gốc: /home/aiserver/works/fingerprint

Version	Status	Epochs	Highlights
v11	✅ Done	80	PolyU back in L_sens
v12	✅ Done	80	T17–T19 concept fixes
v13	✅ Done	80	Revert T17, per-dataset L_spread, occlusion fix
v14	✅ Done	60	Exclude non-segmented slap; KS=0.263 ✅, Pearson=0.294 ✅; dry_skin ↑↑
v15	✅ Done	60	T30: SD302 concept-only L_deg + gamma=2.0; KS=0.2758 (tệ), score collapse (24–56) ❌
v16	✅ Done	60	T31: per-identity cos stats + tanh; score collapse 46.9–50.5 ❌ (worse!); KS=0.527 ❌
v17	✅ Done	60	T32: FVC-only cos stats; score collapse ALL SD302 at 52.7, q_std=0.60 ❌; KS=0.616 ❌
v18	✅ Done ❌	60	T33: revert v14 loss + SD302-A; score collapse q_std≈0.01, KS=0.249⚠️(false positive), Pearson=0.007❌; concept head stuck near bounds
v19	✅ Done ❌	60	T34: true revert to v14 — spread-weight=3.0, deg-every-n-steps=2; score collapse ALL ~53.1x, q_std≈0.01 ❌
v20	✅ Done ❌	60	T35: full prototypes (proto-max=0) + L_deg on SD302; score collapse q_std≈0.007 ❌ — --deg-include-sd302 was wrong fix
v21	✅ Done ❌	60	T36: full prototypes + FVC-only L_deg; KS=0.312, Pearson=0.092 ❌ — CrossSensorBatchSampler k_cross=16 destroyed quality discrimination
v22	✅ Done	60	T37: fix k_cross=0 + single GPU; KS=0.278, Pearson=0.546
v24	✅ Done	60	Best: KS=0.126 ✅, Pearson=0.800 ✅, q_std~22
v25	❌ Done	60	gamma=1.5 too weak; KS=0.213, Pearson=0.620
v26	✅ Done	60	gamma=2.0 restored
v27	✅ Done	60	SpatialConceptHead
v28	✅ Done	60	No L_mat (--no-mat); KS=0.1346, Pearson=0.7645
v29	❌ Done	60	T41 concept map + ortho-weight 3.0 + concept-spread-weight 1.0; KS=0.2985, Pearson=0.1448
v31	✅ Done	60	DINOv2-ViTS/14 teacher (public); KS=0.1152, Pearson=0.8858 — best so far
v32	🚀 Next	60	T42: dry_skin[4,0] + noise[1,3] — fix noise_level crosstalk + weak noise signal

1. Smoke test (kiểm tra pipeline ~2 phút)

cd /home/aiserver/works/fingerprint
bash sifq/scripts/run_smoke.sh

Kết quả ở sifq/checkpoints_smoke/last.pt và metrics.jsonl.

2. Full training v21 — Active 🚀 (60 epochs, từ đầu)

v21 — T36: Full prototypes + FVC-only L_deg (remove --deg-include-sd302).

Root cause of v20 failure (new diagnosis):

v20 training showed q_std≈18.3 (healthy) but ALL SD302 inference scores collapsed to ~53.38 (q_std≈0.007). The --proto-max-batches 0 fix (T35a) was correct. But --deg-include-sd302 (T35b) was WRONG and caused the collapse:

L_rank on SD302 says: Q(clean) > Q(low_deg) + m > Q(high_deg) + m

The model satisfies this with a single ~53.38 for ALL clean SD302 images + lower for degraded

L_rank does NOT require different clean images to score differently → attractor at ~53.38

L_spread_ds (batch-level) + L_pair reinforce the collapse

Why v14 worked without --deg-include-sd302: FVC genuine quality variation → MDGT raw cosine genuinely varies (0.75→0.93) per impression → L_mat gradient shapes backbone to be quality-discriminative → transfers to SD302 at inference. L_rank on SD302 BLOCKS this transfer by creating the stable ~53.38 attractor.

T36 fix: Remove --deg-include-sd302. Keep --proto-max-batches 0. FVC-only L_deg: genuine quality signal trains backbone → SD302 scored by intrinsic quality.

v20 — T35: Full prototypes + L_deg on SD302. Fix root causes of ALL v15–v19 failures.

Root cause analysis (v19 failure = same collapse as v15–v18):

v19 was supposed to reproduce v14 exactly but STILL collapsed (all scores ~53.1x, q_std≈0.01). The "spread-weight/deg-every-n-steps mismatch" explanation was wrong. Two deeper root causes:

Root cause 1 — Truncated prototypes (T35a):

v14's code had NO --proto-max-batches cap → prototypes computed on full 42,683 images

When this param was added (default=150 batches = ~19,200 images = 45% of data), SD302 identities went from full 19-sensor multi-sensor prototypes to 2–3 sensor partial prototypes

Full prototype: raw cosine varies with image quality across sensors (quality-discriminative)

Partial prototype: raw cosine correlates with WHICH sensors are in the prototype window → sensor-biased, NOT quality-correlated → no per-image quality gradient for SD302 → collapse

Root cause 2 — FVC-only L_deg leaves SD302 without ordinal grounding (T35b):

T27 reverted T17 (L_deg on all images) because clean SD302 anchored at ~28

But T27 ITSELF introduced per-dataset L_spread_ds — which prevents anchoring

With L_spread_ds forcing SD302 to span [10,90], re-enabling full L_rank for SD302 is now safe

L_deg (L_rank + L_concept) on SD302 provides the only stable per-image quality signal for SD302 at inference (synthetic degradation response ≈ ridge quality proxy)

T35 fixes:

T35a — --proto-max-batches 0: Full dataset prototype computation. Cost: ~10–15 min overhead at epoch 0 (334 batches vs 150). Restores genuine per-image L_mat quality signal.

T35b — --deg-include-sd302: Apply full L_deg (L_rank + L_concept) to SD302 images. L_spread_ds prevents the T17 score anchoring issue. Per-image ordinal grounding for SD302.

All other settings from v19 kept unchanged:

--no-mat-stats, --spread-weight 3.0, --deg-every-n-steps 2

--concept-deg-gamma 0.5, --sd302-concept-weight 0.0

SD302-A included, --batch-size 128, --gpus 0

2. Full training v22 — 🚀 Next run (60 epochs, từ đầu)

v22 — T37: Fix ALL bugs from v15–v21. First correct run with full dataset.

Three bugs caused v15–v21 to fail:

BUG 1 — CrossSensorBatchSampler k_cross=16:

k_cross=16 forces 16 guaranteed pairs/batch = 8–16× more L_sens pressure

With full prototypes (nearly-constant L_mat targets), backbone over-optimises sensor invariance and loses quality discrimination → Pearson collapses to ~0.09 (v21)

Fix: --k-cross 0 (random batching)

BUG 2 — proto-max-batches default=150:

Only 45% of dataset used for prototypes → partial sensor prototypes → sensor-biased cosine

Fix: --proto-max-batches 0 (full dataset)

BUG 3 — DataParallel (--gpus 0,1) causes 4.3× slowdown for TinyViT-5M:

Fix: --gpus 0 (single GPU)

BONUS — Redundant teacher double-pass eliminated:

Now build_prototypes_from_cache() computes prototypes from emb_cache (O(N) CPU)

Note on high L_sens / pair loss in v22: v22 includes SD302-A (8 diverse roll sensors per finger). L_mat targets vary by sensor for the same finger → L_pair starts high (~12 at S2 onset) and decreases gradually. This is EXPECTED with a richer dataset — not a failure. q_std=21–22 (best quality discrimination so far). L_sens equilibrium is found by ep60.

cd /home/aiserver/works/fingerprint
version=v32
nohup bash sifq/scripts/run_train_$version.sh > sifq/logs/train_$version.log 2>&1 &
echo "PID: $!"
tail -f sifq/logs/train_$version.log

pkill -f train_sifq.py

Expected results (research design goals, 171 sensor pairs):

Metric	v21 ❌	v22 (ep51)	v22 goal
q_std (train)	~17.7	~22 ✅	≥18
epoch time	~970s ❌	~220s ✅	≤250s
Track 2 mean_KS	0.312	— (training)	≤0.30
Track 2 Pearson	+0.092 ❌	— (training)	≥0.20
Concept grounding	dry_skin+0.83 ❌	— (training)	all targets negative ρ

Dấu hiệu v22 đang train đúng:

Epoch	Dấu hiệu tốt
ep 0	Startup nhanh (~teacher 1 pass + cache); epoch ~220s
ep 0–10	q_std tăng nhanh 1→10→18; l_mat ~0.18–0.20; l_deg > 0
ep 12+	l_pair cao (~12) khi S2 bắt đầu — BÌNH THƯỜNG với SD302-A 8 sensors
ep 20–50	l_pair giảm dần (12→4→2); adv tăng về rand_ce; q_std ~20–22 ✅
ep 40–60	Stable: l_spread ≈0.002, q_mean ~52, q_std ~21–22

3. Full training v21 — Done ❌ (60 epochs)

| ep 20+ | train_l_pair → 0.01–0.05, q_std ổn định 12–18 |

Key difference from v20: train_q_std in v20 was also 18.3 (false positive driven by L_spread). In v21, watch for INFERENCE q_std > 5 at end — run a quick spot-check: python sifq/scripts/run_infer.py --checkpoint sifq/checkpoints/v21/last.pt --max-samples 200 --output /tmp/spot.jsonl && python -c "import json,statistics; s=[json.loads(l)['q_score'] for l in open('/tmp/spot.jsonl')]; print(f'std={statistics.stdev(s):.2f} range=[{min(s):.1f},{max(s):.1f}]')"

cd /home/aiserver/works/fingerprint
nohup bash sifq/scripts/run_train_v21.sh > sifq/logs/train_v21.log 2>&1 &
echo "PID: $!"
tail -f sifq/logs/train_v21.log

Checkpoint: sifq/checkpoints/v21/last.pt
Train từ đầu, LR: 1e-4 cosine → 5e-6, Batch: 128, GPU: 0
Auto-eval sau training: kết quả ở sifq/eval_results/v21/

2b. Eval v21 — Chạy thủ công nếu cần

cd /home/aiserver/works/fingerprint
nohup bash sifq/scripts/run_eval_v32.sh > sifq/eval_v32.log 2>&1 &
echo "PID: $!"

Kết quả ở sifq/eval_results/v21/.

2c. Full training v20 — Completed ❌ (score collapse)

v20 actual results: q_std≈0.007 (collapsed), mean_KS=0.4936 ❌, Pearson=0.0048 ❌. Root cause: --deg-include-sd302 applied L_rank to all clean SD302 → stable ~53.38 attractor. See v21 for fix.

2d. Eval v20 — Chạy thủ công nếu cần

cd /home/aiserver/works/fingerprint
nohup bash sifq/scripts/run_eval_v20.sh > sifq/eval_v20.log 2>&1 &
echo "PID: $!"

Kết quả ở sifq/eval_results/v20/.

2c. Full training v19 — Completed ❌ (score collapse — 60 epochs)

v19 — T34: True revert to v14 hyperparameters (FAILED — same collapse as v18). Root cause was NOT the spread/deg values (those were already correct in v19). Real root cause: truncated prototypes + FVC-only L_deg. See v20 analysis above.

2. Full training v18 — Completed ❌ (score collapse — 60 epochs)

v18 — T33: revert toward v14 + include SD302-A.

Root cause từ v17 eval:

T32 chỉ remove explicit L_mat vs L_pair conflict, nhưng SD302 VẪNN collapse → q_std=0.60 at inference.

FVC tanh targets (variable) vs SD302 raw cosine targets (constant) tạo asymmetry → model học shortcut "FVC=variable, SD302=52.7".

W_SPREAD=4.0 + gamma=2.0 + sd302_concept_weight=1.0 làm tệ thêm so với v14.

T33 fixes: revert tất cả additions từ v15–v17:

T33a — --no-mat-stats: Skip per-identity cosine stats hoàn toàn. Tất cả images (FVC + SD302) đều dùng raw cosine làm L_mat target → không còn FVC/SD302 quality signal asymmetry.

T33b — W_SPREAD = 2.0 (revert từ 4.0 — v14 level)

T33c — concept_deg_gamma = 0.5 (revert từ 2.0 — v14 default)

T33d — sd302_concept_weight = 0.0 (revert T30b — v14 default)

T33e — deg-every-n-steps = 4 (revert từ 2 — v14 default)

NEW — SD302-A included: 13,630 images, 8 sensors (A-H) trong training (v14 chỉ dùng B+D)

cd /home/aiserver/works/fingerprint
nohup bash sifq/scripts/run_train_v18.sh > sifq/logs/train_v18.log 2>&1 &
echo "PID: $!"
tail -f sifq/logs/train_v18.log

Checkpoint: sifq/checkpoints/v18/last.pt
Metrics: sifq/checkpoints/v18/metrics.jsonl
Train từ đầu (không resume)
LR: 1e-4 cosine → 5e-6
Batch: 128
Auto-eval sau training: kết quả ở sifq/eval_results/v18/

Kết quả v18 thực tế (FAILED — score collapse):

Metric	v14 ✅	v17 ❌	v18 actual ❌	Target
q_std (inference)	~15	0.60	0.01 ❌❌	>12
Score range	10–90	18–53	54.62–54.65 ❌	10–90
Track 2 mean_KS	0.263	0.616	0.249 ⚠️	≤0.27
Track 2 Pearson	+0.294	-0.002	0.007 ❌	≥0.25
n_sensor_pairs	55	171	171	≥100

⚠️ v18 KS=0.249 là false positive: Model không phân biệt được images — tất cả sensors cho cùng score ≈54.64. KS thấp vì các phân bố trivially giống nhau (đều là Dirac delta tại 54.64). Root cause mới: raw cosine L_mat hằng số (~0.85) không tạo per-image quality gradient → model collapse về mean. Cần T34: tìm cách tạo per-image quality signal mà không gây FVC/SD302 asymmetry.

Dấu hiệu v18 đang train đúng:

Epoch	Dấu hiệu tốt
ep 0–10	`train_q_std` tăng > 15, `train_l_mat` giảm (~0.25–0.35)
ep 11–20	`train_l_pair` giảm (GRL kick-in), q_std giảm tạm (~12-15)
ep 20+	`train_l_pair` → 0.01–0.05, q_std ổn định 12–18

2a. Eval v18 — Chạy thủ công nếu cần

cd /home/aiserver/works/fingerprint
nohup bash sifq/scripts/run_eval_v18.sh > sifq/eval_v18.log 2>&1 &
echo "PID: $!"

Kết quả ở sifq/eval_results/v18/.

2. Full training v19 — Active 🚀 (60 epochs, từ đầu)

v19 — T34: True revert to v14 hyperparameters.

Root cause từ v18 failure:

v18 comments nói "revert to v14" nhưng dùng sai giá trị:

--spread-weight 2.0 (v14 thực tế: 3.0 — sai 50%)

--deg-every-n-steps 4 (v14 thực tế: 2 — sai 2×)

Hai sai số này làm L_spread + L_deg yếu hơn v14 → backbone không học được quality-discriminative features → inference collapse (q_std≈0.01).

Xác nhận bằng diff run_train_v14.sh run_train_v18.sh.

T34 fixes:

T34a — --spread-weight 3.0: Restore v14 actual value. Gradient L_spread mạnh hơn buộc backbone phân biệt quality.

T34b — --deg-every-n-steps 2: Restore v14 actual value. 2× tần suất L_deg → ordinal grounding mạnh hơn.

Giữ nguyên từ v18:

--no-mat-stats: cần thiết để reproduce v14 pre-T31 behavior với code hiện tại

--concept-deg-gamma 0.5, --sd302-concept-weight 0.0 (v14 defaults)

SD302-A included (v18 addition, giữ lại)

--batch-size 128, --gpus 0

cd /home/aiserver/works/fingerprint
nohup bash sifq/scripts/run_train_v19.sh > sifq/logs/train_v19.log 2>&1 &
echo "PID: $!"
tail -f sifq/logs/train_v19.log

Checkpoint: sifq/checkpoints/v19/last.pt
Metrics: sifq/checkpoints/v19/metrics.jsonl
Train từ đầu (không resume)
LR: 1e-4 cosine → 5e-6
Batch: 128, GPU: 0
Auto-eval sau training: kết quả ở sifq/eval_results/v19/

Expected results (target = v14 baseline):

Metric	v14 ✅	v18 ❌	v19 target
q_std (inference)	~15.5	0.01	>12
Score range	10–90	54.62–54.65	10–90
Track 2 mean_KS	0.263	0.249⚠️	≤0.27 (real)
Track 2 Pearson	+0.294	0.007	≥0.25

Dấu hiệu v19 đang train đúng:

Epoch	Dấu hiệu tốt
ep 0–10	`train_q_std` > 15, `train_l_mat` giảm (~0.25–0.35)
ep 11–20	`train_l_pair` giảm (GRL kick-in), q_std giảm tạm (~12–15)
ep 20+	`train_l_pair` → 0.01–0.05, q_std ổn định 12–18

2b. Eval v19 — Chạy thủ công nếu cần

cd /home/aiserver/works/fingerprint
nohup bash sifq/scripts/run_eval_v19.sh > sifq/eval_v19.log 2>&1 &
echo "PID: $!"

Kết quả ở sifq/eval_results/v19/.

3. Full training v17 — Completed (60 epochs) ✅ (score collapse ❌)

v17 thất bại: Score collapse nghiêm trọng — tất cả SD302 images output ~52.7 (q_std=0.60 at inference). Training q_std=16.59 vẫn ổn (driven by FVC). KS=0.616 ❌.

Root cause v17 (sâu hơn T32 đã fix):

T32 fix (FVC-only stats, SD302 raw cosine fallback) đã loại bỏ L_mat vs L_pair CONFLICT nhưng không fix SCORE COLLAPSE.

SD302 raw cosine ≈ 0.85 là CONSTANT cho tất cả images → L_mat không phân biệt được SD302 identities với nhau → không có per-image quality signal cho SD302.

GRL + L_pair triệt tiêu discriminative features từ SD302 backbone: GRL xóa sensor-correlated info (bao gồm quality-correlated-with-sensor). L_pair ép same-identity same-score.

L_spread_ds tạo relative constraints per-batch nhưng KHÔNG CONSISTENT qua các batches → average score cho mỗi SD302 image settle về ~52.7.

FVC images: có stable per-image tanh targets → model học được quality function. SD302: chỉ có inconsistent batch-relative spread signals → feature collapse.

v16 root cause (T32 mục tiêu):

v17 là training hiện tại. Root cause từ v16 eval:

Score collapse nghiêm trọng: inference q_std=0.33, range 46.9–50.5 (tệ hơn v15!). Training q_std=15.6 trông ổn nhưng driven bởi FVC images. Inference chỉ chạy SD302 → collapse.

T32 — Root cause: T31's per-identity stats conflict trực tiếp với L_pair.

L_mat (T31b): q_mat = tanh((cos−μᵢ)/σᵢ) → muốn within-identity variation (ảnh khác nhau của cùng ngón tay nên score khác nhau)

L_pair: |Q(s1)−Q(s2)| ≤ 0.05 → muốn within-identity uniformity (cùng ngón tay, khác sensor, phải score bằng nhau)

Hai loss conflict → model giải quyết bằng output ~50 cho tất cả SD302. FVC không có L_pair → spread được → training q_std=15.6 driven by FVC only.

v17 fixes (T32): FVC-only per-identity stats

T32a — compute_identity_cos_stats(fvc_only=True): Chỉ tính stats cho FVC identities. FVC không có cross-sensor pairs → L_pair=0 → không conflict với L_mat.

T32b — _compute_q_mat fallback to raw cosine: SD302 identities không có trong stats → dùng raw cosine (~0.85, hằng số) giống v14. Không còn conflict với L_pair.

Giữ nguyên: W_SPREAD=4.0, concept_deg_gamma=2.0, sd302_concept_weight=1.0, deg-every=2.

cd /home/aiserver/works/fingerprint
nohup bash sifq/scripts/run_train_v17.sh > sifq/logs/train_v17.log 2>&1 &
echo "PID: $!"
tail -f sifq/logs/train_v17.log

Checkpoint: sifq/checkpoints/v17/last.pt
Metrics: sifq/checkpoints/v17/metrics.jsonl
Train từ đầu (không resume)
LR: 1e-4 cosine → 5e-6
Batch: 128
Auto-eval sau training: kết quả ở sifq/eval_results/v17/

Kết quả v17 (thực tế so với target):

Metric	v14	v16	Target v17	Thực tế v17
Score range (eval, SD302)	—	46.9–50.5 ❌	0–100	18.1–53.0 (all ~52.7) ❌
q_std (inference)	—	0.33 ❌	>10	0.60 ❌ (worse collapse)
q_std (train, final)	~15.5	15.6	~15–20	16.59 ✅
Track 2 mean_KS	0.263 ✅	0.527 ❌	≤0.27	0.616 ❌
Track 2 Pearson	0.294 ✅	0.046 ❌	≥0.25	-0.002 ❌

Dấu hiệu v17 đang train đúng (watch trong log):

Epoch	Dấu hiệu tốt
ep 0–5	`train_l_mat` bắt đầu giảm (~0.15–0.25) — FVC quality signal hoạt động
ep 0–10	`train_q_std` tăng lên > 15 (L_spread có tác dụng trên mixed batch)
ep 11–15	`train_l_pair` giảm nhanh (GRL kick-in), q_std giảm tạm thời
ep 20+	`train_l_pair` → 0.01–0.05, `train_q_std` ≥ 12, ổn định

2a. Eval v17 — Chạy thủ công nếu cần

Auto-eval đã được tích hợp vào run_train_v17.sh. Nếu cần chạy lại riêng:

cd /home/aiserver/works/fingerprint
nohup bash sifq/scripts/run_eval_v17.sh > sifq/eval_v17.log 2>&1 &
echo "PID: $!"

Kết quả ở sifq/eval_results/v17/.

2b. Full training v16 — Completed (60 epochs) ✅ (score collapse ❌)

v16 thất bại: score collapse 46.9–50.5 (tệ hơn v15!), KS=0.527. Root cause: T31 conflict với L_pair → T32 fix.

# Xem kết quả v16 eval
cat sifq/eval_results/v16/eval_summary.json

Checkpoint: sifq/checkpoints/v16/last.pt
Eval kết quả: sifq/eval_results/v16/

2c. Full training v15 — Completed (60 epochs) ✅ (score collapse ❌)

v15 thất bại: score range 24–56 (score collapse), KS=0.2758 (tệ hơn v14). Root cause: T31.

# Chỉ xem logs — không cần chạy lại
cat sifq/eval_v15.log
cat sifq/checkpoints_full_v15/metrics.jsonl | tail -5

2c. Full training v14 — Completed (60 epochs, từ đầu) ✅

nohup bash sifq/scripts/run_eval_v15.sh > sifq/eval_v15.log 2>&1 &
echo "PID: $!"

Kết quả ở sifq/eval_results/v15/.

2c. Full training v14 — Completed (60 epochs, từ đầu) ✅

v14 đã train xong. Nguyên nhân gốc từ v13 eval:

T28 — Non-segmented slap std≈0: R_1000_slap, R_500_slap, S_500_slap là ảnh 4 ngón tay → backbone cho score ~21.2 cho mọi người (std=0). Kéo mean_KS từ 0.51 (v12) lên 0.634 (v13 — tệ hơn). Loại khỏi training và eval.

T29 — Train từ đầu (không resume): backbone TinyViT từ IN22k pretrained, concept head + aggregator ngẫu nhiên → cần 60 epochs để converge đầy đủ.

cd /home/aiserver/works/fingerprint
nohup bash sifq/scripts/run_train_v14.sh > sifq/train_v14.log 2>&1 &
echo "PID: $!"
tail -f sifq/train_v14.log

Checkpoint: sifq/checkpoints_full_v14/last.pt
Metrics: sifq/checkpoints_full_v14/metrics.jsonl
Train từ đầu (không resume)
Sensors bị loại: R_1000_slap, R_500_slap, S_500_slap (~1,700 records)
Dataset: 42,683 records, 29 sensors

Kết quả training v14 (60 epochs):

Metric	v13	v14 thực tế	Trend
`q_mean` (train)	~33	52.6	✅ Centered tốt hơn hẳn
`q_std` (train)	12.5	~15.5	✅ Spread ổn định
`l_pair` (ep59)	—	0.006	✅ Sensor invariance xuất sắc
`l_mat` (ep59)	—	0.270	✅ Converged tốt
`l_spread` (ep59)	—	0.025	✅ Ổn định
Eval (KS/Pearson)	0.634 / 0.224	0.263 / 0.294 ✅	Target ✅

3-phase training dynamics (quan sát được lần đầu ở v14 do train from scratch):

Phase	Epochs	q_std	l_pair	Mô tả
S1 — Spread	0–10	1 → 22.6	0.47 → 25	GRL λ warm-up, L_spread mở rộng phân bố
Transition	11–15	22.6 → 14	24 → 0.13	β kick-in, GRL triệt tiêu sensor signature
Equilibrium	16–59	~15.5	→ 0.006	l_pair/l_mat converged, l_spread stable

Kết quả eval v14 (Track 2 + Track 4):

Track	Metric	v12	v13	v14
Track 2	mean_KS	0.510	0.634 ❌	0.263 ✅
Track 2	Pearson	-0.003	+0.224	+0.294 ✅
Track 2	n_sensor_pairs	91	91	55 (slap loại)
Track 4	dry_skin → contrast	—	-0.376	-0.623 ✅⬆
Track 4	dry_skin → continuity	—	+0.009	-0.563 ✅⬆
Track 4	blur → clarity	—	-0.490 ✅	-0.187 ⚠️ (yếu hơn)
Track 4	noise → noise_level	—	-0.533 ✅	-0.019 ⚠️ (regression)
Track 4	wet_press → minutiae	—	+0.326 ❌	+0.565 ❌ (tệ hơn)

Expected improvements v14 — đã đạt:

Track	Metric	v13	Target v14
Score	Sensor clusters	3 cụm (~21, ~50, ~55)	2 cụm hoặc phẳng (slap đã loại)
Track 2	mean_KS	0.634 ❌	<0.35 ✅
Track 2	Pearson	0.224	≥0.25
Train	Epochs	80	60 (from scratch)

2d. Eval v14 — Chạy sau khi training xong ⭐

Bước tiếp theo bắt buộc sau khi v14 training completed.

# Bước 1: Inference — sinh Q scores
python sifq/scripts/run_infer.py \
  --checkpoint sifq/checkpoints_full_v14/last.pt \
  --exclude-sensor "R_1000_slap,R_500_slap,S_500_slap" \
  --output     sifq/eval_results/sifq_scores_v14.jsonl

# Bước 2: Eval — Track 2 + Track 4
python sifq/scripts/run_eval.py \
  --sifq-scores sifq/eval_results/sifq_scores_v14.jsonl \
  --checkpoint  sifq/checkpoints_full_v14/last.pt \
  --out-dir     sifq/eval_results/v14 \
  --skip-track1

# Bước 3: Visual score milestones
mkdir -p sifq/eval_results/v14
python sifq/visualize_score_milestones.py \
  --scores  sifq/eval_results/sifq_scores_v14.jsonl \
  --output  sifq/eval_results/v14/milestone_samples.png \
  --n_buckets 8

2b. Eval v13 (chính xác, exclude non-segmented slap)

bash sifq/scripts/run_eval_v13.sh

Kết quả ở sifq/eval_results/v13_seg/ (exclude R/S_*_slap).

2c. Full training v13 — Completed (80 epochs)

v13 là training hiện tại. Phân tích sau v12 eval cho thấy T17 fix (L_deg on all datasets) là sai về nguyên tắc:

T20 — SD302 anchored tại ~28: Synthetic degradation trên SD302 kéo clean images về gần degraded floor (~28). SD302 toàn ảnh chất lượng cao → synthetic degradation không phản ánh quality variation thực → anchoring sai.

T21 — Non-segmented slap std=0: Full-hand slap images visually homogeneous → model gán cùng score (behavior đúng, không cần fix).

T22 — Noise concept regression: noise_level: +0.188 (v11) → -0.168 (v12). Old c_idx==3 special case gây conflict với L_rank → model invert noise_level direction.

T23 — Occlusion vẫn dead: 40% coverage chưa đủ + continuity concept không được supervise.

v13 fixes:

T27: Revert T17 (FVC-only L_deg) + per-dataset L_spread cho SD302 subset riêng (prevents SD302 collapse mà không cần synthetic degradation anchoring).

T25: Remove c_idx==3 special case. Tất cả concepts giảm với degradation (high=better).

T26: Occlusion: add continuity (c_idx=2) to DEGRADATION_CONCEPT_MAP + coverage 40%→55%.

cd /home/aiserver/works/fingerprint
nohup bash sifq/scripts/run_train_v13.sh > sifq/train_v13.log 2>&1 &
echo "PID: $!"
tail -f sifq/train_v13.log

Checkpoint: sifq/checkpoints_full_v13/last.pt
Metrics: sifq/checkpoints_full_v13/metrics.jsonl
Resume từ: sifq/checkpoints_full_v12/last.pt

Expected improvements v13:

Track	Metric	v11	v12	Target v13
Score	q_std (inference)	6.47	12.53	>18 (spread đều, bao gồm SD302 roll/flat)
Score	mean	59.81	33.63	45–65 (SD302 anchored đúng chỗ)
Track 2	mean_KS	0.5566	0.5100	<0.20 (sensor type bias vẫn còn nhưng giảm)
Track 4	wet_press → clarity	+0.219 ❌	-0.647 ✅	giữ ✅
Track 4	noise → noise_level	+0.188 ✅	-0.168 ❌	positive ✅ (T25 fix)
Track 4	occlusion → continuity	-0.029 ❌	+0.023 ❌	negative ✅ (T26 fix)
Track 4	occlusion → minutiae	-0.032 ❌	+0.075 ❌	negative ✅ (T26 fix)

2b. Full training v11 — Completed (80 epochs)

v11 đã train xong. Checkpoint tại sifq/checkpoints_full_v11/last.pt.

PolyU trở lại L_sens — contact vs contactless pairs là tín hiệu L_sens mạnh nhất (2,612 anchor groups thay vì 2,276)

PolyU bị mask khỏi L_mat — không compute prototype / teacher loss cho PolyU

PolyU bị mask khỏi L_deg — không apply degradation cho PolyU images

Schedule mới: β/α = 1.0 ngay từ S2 (ep20) — β ramp nhanh hơn α trong ep10–19

S2: α=0.30 β=0.30 γ=0.60 (β/α=1.0) thay vì α=0.40 β=0.20 của v9

S3: α=0.25 β=0.35 γ=0.50 (β/α=1.4)

Root cause của v9 oscillation: β/α=0.50 trong S2 quá yếu — GRL không đủ override L_mat với PolyU cross-modality pairs

cd /home/aiserver/works/fingerprint
nohup bash sifq/scripts/run_train_v14.sh > sifq/train_v14.log 2>&1 &
echo "PID: $!"

# Theo dõi log
tail -f sifq/train_v11.log

Dữ liệu: SD302-A/B/D + FVC2002 + FVC2004 + PolyU = 43,559 ảnh, 24 sensors
L_sens anchors: SD302 (~2,276 groups) + PolyU (336 cross-modal groups) = 2,612 total
L_mat: SD302 + FVC (PolyU masked)
L_deg: FVC only (SD302 + PolyU masked)
Checkpoint: sifq/checkpoints_full_v11/last.pt
Metrics: sifq/checkpoints_full_v11/metrics.jsonl
Thời gian ước tính: ~9 giờ (2× RTX A4000, DataParallel, 453 steps/epoch)

Kiểm tra nhanh training đang chạy:

# Xem loss mới nhất
tail -5 sifq/checkpoints_full_v11/metrics.jsonl | python3 -c "
import sys, json
for l in sys.stdin: r=json.loads(l); print(f'ep{r[\"epoch\"]:02d} deg={r[\"train_l_deg\"]:.4f} sens={r[\"train_l_sens\"]:.4f} spread={r[\"train_l_spread\"]:.4f} lr={r[\"lr\"]:.2e} total={r[\"train_total\"]:.4f}')
"

Expected trends v11:

Loss	S1 (ep0-9)	S2 (ep20-34)	S3 (ep40+)
`L_deg`	0.10 → <0.05	giữ <0.05	giữ <0.05 (γ=0.50)
`L_sens`	tracked only	<1.5 (β/α=1.0 → GRL cân bằng)	<1.0
`spread`	giảm rõ (uniform mode)	tiếp tục giảm	→ ~0
`L_mat`	flat ~0.3	giảm (có FVC signal)	flat ~0.01

3. Inference — sinh Q scores từ model đã train

cd /home/aiserver/works/fingerprint
source .venv/bin/activate

python sifq/scripts/run_infer.py \
  --checkpoint sifq/checkpoints_full_v11/last.pt \
  --output     sifq/eval_results/sifq_scores_v11.jsonl

Output: sifq/eval_results/sifq_scores_v11.jsonl — mỗi dòng là 1 ảnh với Q score + 6 concepts.

4. Evaluation — verify SOTA

4a. Nhanh: chỉ Track 2 (sensor invariance) + Track 4 (concept grounding)

python sifq/scripts/run_eval.py \
  --sifq-scores sifq/eval_results/sifq_scores_v11.jsonl \
  --checkpoint  sifq/checkpoints_full_v11/last.pt \
  --out-dir     sifq/eval_results \
  --skip-track1

4b. Đầy đủ: Track 1 (ERC) + Track 2 + Track 4

python sifq/scripts/run_eval.py \
  --sifq-scores sifq/eval_results/sifq_scores_v11.jsonl \
  --checkpoint  sifq/checkpoints_full_v11/last.pt \
  --out-dir     sifq/eval_results \
  --max-pairs   3000

4c. Có NFIQ2 baseline (so sánh trực tiếp với SOTA)

# NFIQ2 is not on PyPI — generate proxy scores using image quality heuristics:
python sifq/scripts/gen_nfiq2_proxy_scores.py \
  --sifq-scores sifq/eval_results/sifq_scores_v11.jsonl \
  --output      /tmp/nfiq2_scores.jsonl
python sifq/scripts/run_eval.py \
  --sifq-scores  sifq/eval_results/sifq_scores_v11.jsonl \
  --checkpoint   sifq/checkpoints_full_v11/last.pt \
  --out-dir      sifq/eval_results \
  --nfiq2-scores /tmp/nfiq2_scores.jsonl

5. Xem kết quả

# Bảng số
cat sifq/eval_results/eval_summary.json

# Plots (mở file)
ls sifq/eval_results/*.png

File	Nội dung
`eval_summary.json`	Tất cả metrics (AUC_ERC, KS statistic, Pearson)
`plot_erc.png`	Track 1 — ERC curve, AUC thấp hơn là tốt hơn
`plot_sensor_hist.png`	Track 2 — Q distribution per sensor
`plot_sensor_scatter.png`	Track 2 — scatter Q_s1 vs Q_s2
`plot_crosstalk.png`	Track 4 — concept grounding heatmap

6. Tiêu chí SOTA rank 1

Track	Metric	v5	v7 nofvc	Target v11
Track 1 (ERC)	AUC_ERC	0.8884	—	< NFIQ2 proxy
Track 2 (Sensor)	mean_KS	0.326	0.326 (collapse)	< 0.10
Track 2 (Sensor)	Pearson cross-sensor	0.179	—	> 0.70
Track 4 (Concepts)	blur → clarity rho	+0.483 ❌	—	negative
Track 4 (Concepts)	occlusion → minutiae rho	-0.013 ❌	—	negative

Checkpoint paths (hiện tại)

Thành phần	Path
MDGT teacher	`pad/TRAM-downstream/checkpoint/checkpoints_dinov2_tram/best_eer.pt`
NIST SD302-A	`dataset/302a/images/challengers`
NIST SD302-B	`dataset/302b/images/baseline`
NIST SD302-D	`dataset/nist_302d/images/auxiliary`
FVC2002	`dataset/FVC_Dataset/FVC2002`
FVC2004	`dataset/FVC_Dataset/FVC2004`
PolyU	`dataset/PolyU`
SIFQ v18 (done, score collapse q_std≈0.01, Pearson=0.007 ❌)	`sifq/checkpoints/v18/last.pt`
SIFQ v17 (done, score collapse 18.1–53.0, q_std=0.60 ❌)	`sifq/checkpoints/v17/last.pt`
SIFQ v16 (done, score collapse 46.9–50.5 ❌)	`sifq/checkpoints/v16/last.pt`
SIFQ v15 (done, score collapse 24–56 ❌)	`sifq/checkpoints_full_v15/last.pt`
SIFQ v14 (eval done, KS=0.263 ✅)	`sifq/checkpoints_full_v14/last.pt`
SIFQ v13 (completed)	`sifq/checkpoints_full_v13/last.pt`
SIFQ v12 (completed)	`sifq/checkpoints_full_v12/last.pt`
SIFQ v11 (completed, score collapse ~58.3)	`sifq/checkpoints_full_v11/last.pt`
SIFQ v10 (stopped, PolyU excluded from L_sens)	`sifq/checkpoints_full_v10/last.pt`
SIFQ v9 (stopped ep63, β/α=0.50 too weak)	`sifq/checkpoints_full_v9/last.pt`
SIFQ v8 (stopped ep22, sens stuck 3-3.5)	`sifq/checkpoints_full_v8/last.pt`
SIFQ v7 nofvc (ref)	`sifq/checkpoints_full_v7_nofvc/last.pt`

Lịch sử checkpoint

Version	Epochs	Vấn đề	Trạng thái
`checkpoints_full/`	21	beta=0 bug → GRL tắt	Lỗi
`checkpoints_full_v2/`	50	spread loss bimodal	Lỗi
`checkpoints_full_v3/`	30	Q collapse, deg dead	Lỗi
`checkpoints_full_v5/`	25	Chỉ noise degradation	l_deg≈0
`checkpoints_full_v6/`	80	Full deg pipeline	Ref
`checkpoints_full_v7_nofvc/`	80	Score collapse (spread=0.010), L_sens explosion ep17-18, γ=0.40 làm L_deg plateau 0.104	Ref
`checkpoints_full_v8/`	22	Uniform spread, β ramp, γ=0.50, cosine LR, FVC — dừng ep22 vì α/γ step change tại ep15 gây sens oscillate 3–3.5	Stopped
`checkpoints_full_v9/`	63	Ramp ALL THREE (α/β/γ) ep10–19 + PolyU — dừng ep63 vì β/α=0.50 trong S2 quá yếu, sens oscillate 2–3 suốt S2/S3	Stopped
`checkpoints_full_v10/`	early	PolyU excluded từ L_sens — dừng sớm, sai thiết kế	Stopped
`checkpoints_full_v11/`	80	Score collapse: 95% ảnh tại ~58.3, wet_press concept sai chiều	Completed
`checkpoints_full_v12/`	80	T17+T18+T19: L_deg all datasets, fix wet_press erode, gamma=1.0	Completed
`checkpoints_full_v13/`	80	Revert T17, per-dataset L_spread, T25 noise fix, T26 occlusion fix	Completed
`checkpoints_full_v14/`	60	Train from scratch; exclude non-seg slap; KS=0.263; Pearson=0.294	Completed
`checkpoints_full_v15/`	60	T30: SD302 concept-only L_deg, gamma=2.0; score collapse 24–56 ❌	Completed
`checkpoints/v16/`	60	T31: per-identity stats ALL datasets; score collapse 46.9–50.5 ❌; L_mat vs L_pair conflict	Completed
`checkpoints/v17/`	60	T32: FVC-only stats; score collapse 18.1–53.0 (q_std=0.60 ❌); GRL suppresses SD302 quality	Completed
`checkpoints/v18/`	60	T33: --no-mat-stats + W_SPREAD=2.0 + gamma=0.5 + SD302-A; revert to v14 design; score collapse q_std≈0.01, KS=0.249⚠️(false positive) ❌	Completed ❌