SIFQ — Plan & Experiment Tracker
Dùng file này để: ghi kế hoạch trước khi thay đổi code, track tiến độ, và revert khi cần.
Active Experiments
v32 — Done ❌
Goal: Fix two concept grounding failures in v31 via T42 concept map Key changes from v31: T42 concept map (2 changes):
dry_skin: [4, 2, 0] → [4, 0]— remove continuitynoise: [3] → [1, 3]— add clarity as co-target Script:scripts/run_train_v32.shResult: KS=0.1375, Pearson=0.7892 — Track 2 regression vs v31 (KS +0.022, Pearson -0.097) Track 4 regressions vs v31:- blur→clarity: +0.046 ✗ (was -0.305 ✓)
- noise→clarity: +0.114 ✗ (was -0.236 ✓)
- noise→noise_level: +0.121 ✗ (was -0.081 ✓)
- occlusion→minutiae: +0.121 ✗ (was -0.123 ✓)
- wet_press→minutiae: +0.676 ✗ (was -0.695 ✓)
Root cause: Adding clarity[1] to noise map caused clarity concept to absorb noise-degradation gradient, destroying its response to blur. Removing continuity[2] from dry_skin destabilised concept interactions across other degradation types.
Reverted: concept map → T39 (restored
noise:[3],dry_skin:[4,2,0]) Status: Failed — reverted to T39 concept map
v31 — Done ✅
Goal: Benchmark DINOv2-ViTS/14 as public teacher replacing unpublished MDGT
Key change from v28: --teacher dinov2 (frozen DINOv2-ViTS/14 CLS token [B,384])
Script: scripts/run_train_v31.sh
Result: KS=0.1152, Pearson=0.8858, q_std≥15 ✅ — best Track 2 so far
Concept issues: dry_skin→noise_level Spearman -0.758 (spurious, noise_level NOT in target);
blur→continuity +0.214 (wrong direction); noise→noise_level -0.081 (weak signal)
Status: Complete
v29 — Done ❌
Goal: Fix concept grounding failures in v28 — T41 concept map + anti-saturation losses Key changes from v28:
- T41 concept map: jpeg→[1], dry_skin→[4,0], noise→[1,3] (fix gradient conflicts + noise inversion)
--ortho-weight 3.0(vs 1.0 default) — push saturated concepts apart--concept-spread-weight 1.0— penalise concepts with batch std < 0.20 Script:scripts/run_train_v29.shResult: KS=0.2985, Pearson=0.1448 — catastrophic sensor invariance regression Root cause of failure:--concept-spread-weight 1.0forces per-batch concept diversity which amplifies sensor-specific texture features, destroying cross-sensor score alignment. G_roll_png and H_roll_png particularly affected (KS 0.86 and 0.77 vs other sensors). Status: Failed — reverted to v28 code base (train_sifq.py, degradation_ranking.py T39)
v28 — Done ✅
Goal: Validate matcher-free quality learning — L_mat disabled entirely (--no-mat)
Key change from v26: No MDGT teacher. Loss = L_sens + L_deg + L_ortho + L_spread only.
Script: scripts/run_train_v28.sh
Result: KS=0.1346, Pearson=0.7645, q_std~23.2 (60 epochs)
Concept issues found: continuity collapsed (mean=0.068), noise_level inverted (+0.56), orient_coh saturated (0.94), contrast_uni saturated (0.85), clarity flat (std=0.043). Only minutiae_rel discriminating.
Paper claim: ✅ "SIFQ quality is self-supervised — no external matcher needed" validated by KS close to v24
Status: Complete → concept grounding needs fix (next experiment TBD)
v26 — Đang train 🚀
Goal: Verify v25 regression root cause = gamma=1.5 (not T40 concept map)
Key change from v25: --concept-deg-gamma 2.0 (restored), DEGRADATION_CONCEPT_MAP reverted to T39
Script: scripts/run_train_v26.sh
Log: logs/train_v26.log
Expected: KS ≈ 0.126, Pearson ≈ 0.80 (match v24)
Eval: scripts/run_eval_v26.sh (auto-runs after training)
Status: Training in progress
v27 — Pending ⏳
Goal: Validate SpatialConceptHead (14×14 spatial tokens) improves concept grounding
Key change from v26: --spatial-concept-head flag — SpatialConceptHead replaces ConceptHead
Script: scripts/run_train_v27.sh
Expected: KS ≈ v26, Track 4 diagonal stronger (especially orientation_coherence, continuity, minutiae_reliability)
Blocker: Wait for v26 to confirm KS/Pearson target first
Status: Code ready, not launched
Code Change Log
2026-06-04 — T42 concept map: 2 changes for v32
Files changed:
src/losses/degradation_ranking.py— T42 DEGRADATION_CONCEPT_MAP:dry_skin: [4,2,0] → [4,0](removed continuity [2])noise: [3] → [1,3](added clarity [1] as co-target)
Root cause (dry_skin): v31 shows noise_level (c=3) Spearman -0.758 with dry_skin (NOT in target). Continuity [2] shares backbone patch-scale features (~16×16) with noise texture → cross-activation. Fix: use contrast[4] + orientation[0] (regional features at multi-patch scale) only.
Root cause (noise): v31 shows noise→noise_level Spearman only -0.081 (nearly no signal). TinyViT 16×16 patch embed averages out pixel Gaussian noise (σ=5–30) → concept[3] gradient ≈ 0. Adding clarity[1] anchors noise degradation to ridge-valley blur (detectable at patch scale).
Backward compatibility: ⚠️ Modifies shared DEGRADATION_CONCEPT_MAP.
Revert T42 → T39:
"noise": [3],
"dry_skin": [4, 2, 0],
2026-06-03 — DINOv2Teacher: replace MDGT with public DINOv2-ViTS/14
Files changed:
src/training/mdgt_teacher.py— AddedDINOv2Teacherclass: frozen DINOv2-ViTS/14 (viatorch.hub), handles grayscale→RGB channel repeat + ImageNet normalization internally, returns L2-normalized [B, 384] CLS embeddings.MDGTCheckpointTeacherunchanged.src/train.py— Added--teacher {dinov2,mdgt}arg (default=dinov2). ImportDINOv2Teacher. Instantiation inmain()dispatches onargs.teacher.--mdgt-checkpointarg remains but only used when--teacher=mdgt.
Root cause / motivation: MDGT is unpublished work → not reproducible by reviewers → academic integrity risk. DINOv2-ViTS/14 is public (Meta, ICLR 2024), cite-able, and torch.hub reproducible.
Expected Pearson impact: DINOv2 raw → expect Pearson ~0.55–0.70 vs 0.80 with MDGT. Run v31 to benchmark.
Backward compatibility: ✅ All existing scripts using --teacher mdgt --mdgt-checkpoint <path> unaffected.
Revert:
# train.py: change --teacher default back to "mdgt"
# or pass --teacher mdgt --mdgt-checkpoint <ckpt_path> explicitly
2026-06-02 — T41 concept map + anti-saturation losses (v29)
Files changed:
src/losses/degradation_ranking.py— T41 DEGRADATION_CONCEPT_MAP:jpeg: [2,1] → [1](remove continuity — JPEG artifacts wrong gradient direction in v28)dry_skin: [4,2,0] → [4,0](remove continuity — gradient conflict, Spearman+0.10 wrong)noise: [3] → [1,3](add clarity co-target — noise blurs ridges, anchors concept 3)
scripts/train_sifq.py— Added--ortho-weight(default 1.0) and--concept-spread-weight(default 0.0) args; l_orth now weighted; per-concept spread loss (std < 0.20 → penalty) added to total loss; logged asl_cspreadin running dict and epoch print
Root cause: v28 concept collapse/inversion diagnosed via inference stats:
- continuity std=0.076, mean=0.068 → dead concept (3 conflicting grad sources: blur✓, jpeg✗, dry_skin✗)
- noise_level Spearman +0.56 → inverted (Gaussian noise increases texture energy in TinyViT)
- orient_coh mean=0.942, contrast_uni mean=0.851 → L_ortho=1.0 too weak to break saturation
Backward compatibility: ✅ --ortho-weight default=1.0, --concept-spread-weight default=0.0 → all v16-v28 unaffected.
Revert T41 → T39:
# degradation_ranking.py DEGRADATION_CONCEPT_MAP:
"noise": [3],
"jpeg": [2, 1],
"dry_skin": [4, 2, 0],
2026-06-02 — --no-mat flag (v28 teacher-free experiment)
Files changed:
scripts/train_sifq.py— Added--no-matflag; MDGT teacher/emb_cache/prototypes wrapped inif not args.no_mat; training loop forcesl_mat = 0.0when flag is set
Backward compatibility: ✅ Default --no-mat=False — all existing versions unaffected.
Revert: Remove --no-mat block in parse_args() and restore unconditional MDGT instantiation in main().
2026-06-01 — SpatialConceptHead
Files changed:
src/models/concept_head.py— AddedSpatialConceptHeadclasssrc/models/sifq.py—SIFQ.forward()dispatches viaconcept_head.uses_spatialsrc/models/__init__.py— ExportSpatialConceptHeadscripts/train_sifq.py—--spatial-concept-headflag (default=False)src/train.py— same flagscripts/run_eval.py— auto-detect fromckpt["config"]["spatial_concept_head"]scripts/run_infer.py— same auto-detectrules/SIFQ_explained.md— Section 2.2 updated, diagram updated, File Map updated
Backward compatibility: ✅ All v16–v26 checkpoints load cleanly without flag.
2026-06-01 — T39 concept map revert + gamma restore (v26)
Problem: v25 used gamma=1.5 AND T40 concept map. Regression KS 0.126→0.213.
Root cause identified: gamma=1.5 too weak during S1→S2 ramp.
- v24 (gamma=2.0): spread stable ~0.003, q_std grows 18→22 monotonically
- v25 (gamma=1.5): spread spikes to 0.028, q_std collapses 22→10 (epochs 13–16)
Files changed:
src/losses/degradation_ranking.py— T40 reverted to T39 DEGRADATION_CONCEPT_MAPscripts/run_train_v26.sh—--concept-deg-gamma 2.0
DEGRADATION_CONCEPT_MAP T39 (current, correct):
"blur": [1, 2]
"noise": [3]
"jpeg": [2, 1]
"occlusion": [5]
"dry_skin": [4, 2, 0]
"wet_press": [1, 5, 0]
Revert target: T40 map (v25):
"blur": [1, 2, 0] # + orient_coh
"noise": [3, 4] # + contrast_u
"jpeg": [2, 1, 4] # + contrast_u
"occlusion": [5]
"dry_skin": [4, 2, 0]
"wet_press": [1, 5, 0]
Do NOT revert to T40 unless v26 confirms gamma=2.0 alone is insufficient and T39 concept grounding is weaker than expected.
Revert Cookbook
Revert concept map to previous version
# Check what the map looked like in a specific git commit:
git log --oneline src/losses/degradation_ranking.py
git show <commit>:sifq/src/losses/degradation_ranking.py | grep -A 30 "DEGRADATION_CONCEPT_MAP"
# Edit directly:
# src/losses/degradation_ranking.py — DEGRADATION_CONCEPT_MAP dict
Revert to v24 hyperparameters (known good baseline)
# Key v24 flags (from scripts/run_train_v24.sh):
--concept-deg-gamma 2.0
--spread-weight 3.0
--spread-mode uniform
--deg-every-n-steps 2
--no-mat-stats
--proto-max-batches 0
--k-cross 0
--batch-size 96
Load and inspect a checkpoint
import torch
ckpt = torch.load("checkpoints/v24/last.pt", map_location="cpu", weights_only=False)
print(ckpt["metrics"]) # KS, Pearson, q_std, etc.
print(ckpt["config"]) # all argparse flags used
print(ckpt["epoch"]) # which epoch
Compare two checkpoints' configs
import torch, json
c24 = torch.load("checkpoints/v24/last.pt", map_location="cpu", weights_only=False)["config"]
c25 = torch.load("checkpoints/v25/last.pt", map_location="cpu", weights_only=False)["config"]
for k in c24:
if c24.get(k) != c25.get(k):
print(f"{k}: v24={c24.get(k)} v25={c25.get(k)}")
Run eval manually on any checkpoint
cd /home/aiserver/works/fingerprint
# Step 1: Inference
python sifq/scripts/run_infer.py \
--checkpoint sifq/checkpoints/vXX/last.pt \
--root-302a dataset/302a/images/challengers \
--root-302b dataset/302b/images/baseline \
--root-302d dataset/nist_302d/images/auxiliary \
--exclude-sensor "R_1000_slap,R_500_slap,S_500_slap" \
--output sifq/eval_results/sifq_scores_vXX.jsonl
# Step 2: Track 2 + Track 4
python sifq/scripts/run_eval.py \
--sifq-scores sifq/eval_results/sifq_scores_vXX.jsonl \
--checkpoint sifq/checkpoints/vXX/last.pt \
--out-dir sifq/eval_results/vXX \
--exclude-sensor "R_1000_slap,R_500_slap,S_500_slap" \
--skip-track1
Smoke test (~2 min)
cd /home/aiserver/works/fingerprint
bash sifq/scripts/run_smoke.sh
Decision Log
Why not T40 concept map?
T40 added orient_coh to blur/noise/jpeg. Reverted in v26 because root cause of v25 regression was gamma=1.5, not concept map. T40 change had no confirmed benefit. Defer until v26 evaluation is done.
Why FVC-only L_deg (not SD302)?
SD302 images are all high quality by protocol → clean images satisfy L_rank at a single ~53 attractor → blocks quality discrimination signal from L_mat. FVC has genuine quality variation (8 impressions/subject) → MDGT cosine varies → L_mat gradient meaningful → transfers to SD302 at inference. (v20 root cause analysis)
Why --no-mat-stats?
Per-identity cosine stats (T31) created asymmetry: FVC has stable tanh targets, SD302 gets raw cosine ~0.85 constant. Backbone learned "FVC=quality-variable, SD302=fixed" → no L_mat gradient for SD302 → GRL destroyed SD302 features. (v17 root cause)
Why --k-cross 0?
k_cross>0 forces k guaranteed cross-sensor pairs per batch → over-constrains L_sens → backbone over-optimises sensor invariance → loses quality discrimination → Pearson collapses to ~0.09. (v21 root cause)
Why --proto-max-batches 0?
Partial prototypes (150 batches = 45% data) → SD302 identities have 2–3 sensor prototypes instead of full 19-sensor → cosine correlates with WHICH sensors in prototype window (sensor-biased) → no quality gradient for SD302. (v19/v20 root cause)
Why gamma must be ≥ 2.0?
During S1→S2 ramp, mat loss is introduced alongside existing deg loss. If deg loss too weak (gamma=1.5), mat loss dominates momentarily → spread spikes → model partially collapses and never fully recovers. gamma=2.0 keeps deg strong enough to maintain ordinal grounding through the ramp. (v25 root cause)
Pending Research Questions
- v26 eval: Does restoring gamma=2.0 fully recover v24 KS/Pearson? (expected: yes)
- v27 eval: Does SpatialConceptHead improve Track 4 diagonal ρ vs v26?
- noise→noise_level: v24 Track 4 shows +0.365 (wrong direction). Defer to v28.
- T40 revisit: Once v26/v27 stable, evaluate if blur/noise/jpeg → orient_coh improves orientation_coherence grounding.