{ "_meta": { "test": "SAIL-159 / Test 1.1 -- TTA (test-time augmentation)", "productQuestion": "Can we find more of the faint/bottom stripes our model misses -- for free, no retraining -- just by running the model a few extra ways on each photo and merging the results?", "holdoutFile": "test-holdout-ids-canonical.json", "holdoutFrozen": "2026-08-25", "nHoldout": 332, "nScored": 332, "modelPath": "models/sailscan-v26-curvature.pt", "frame": "raw file pixels, NOT exif-transposed (verified consistent with labels + prior .958 baseline this session)", "passesRun": { "base": "imgsz=640 conf=0.1 (matches deployed sidecar)", "scale960": "imgsz=960 conf=0.1", "scale1280": "imgsz=1280 conf=0.1", "bottomcrop": "bottom-third crop, 2x upscale, imgsz=960 conf=0.1, inverse-transformed back to raw pixel frame", "lowconf": "imgsz=640 conf=0.03 (below deployed 0.10 floor)" }, "excluded": "horizontal flip (flips apparent tack, per task instruction). Ultralytics augment=True does not support pose models -- each pass above is a manual predict + inverse-transform instead.", "fusionMethod": "Custom polyline clustering (WBF is boxes-only, does not apply to open polylines): pool candidates from all 5 passes, sort by confidence desc, each candidate joins the nearest existing cluster if symmetric mean point-to-polyline distance (normalized by image diagonal) <= 0.005, else starts a new cluster. Cluster representative = founding (highest-confidence) member; cluster confidence = that member's confidence.", "scoringMethod": "Matching/scoring ported verbatim from label_audit_v26.py / gate0_ablation_scoreboard.mjs (greedy nearest-height polyline matching, point-to-polyline distance normalized by diag). 'raw' = candidates scored directly. 'rules' = candidates passed through the real production filterStripes() (server/pipeline/stripeFilters.js) first -- this is the stage the task's .958 baseline figure (gate0-ablation-scoreboard.json summary.rules.meanRecall) refers to.", "newFpDefinition": "Sum over all 332 photos of max(0, ttaRules.nUnmatchedModel - baselineRules.nUnmatchedModel) -- i.e. rules-stage model stripes with no matching label stripe, counted only when TTA has MORE of them than baseline for that photo. This is the cost side of the ledger.", "recoveredStripeDefinition": "Sum over all 332 photos of max(0, ttaMatched - baselineMatched) label stripes matched at the rules stage. lostStripeCount is the reverse (baselineMatched > ttaMatched) -- reported explicitly, not assumed zero, because an earlier fusion-distance threshold (0.02 normalized) was too loose and wrongly merged distinct adjacent real stripes on close-packed sails, making TTA look like a net recall LOSS. Recalibrated this session: measured same-physical-stripe distance across TTA passes (~0.002-0.003 normalized) vs the tightest real adjacent-label-stripe gap across all 332 photos (762 gaps, minimum 0.0077, 1st percentile 0.0096) and set FUSE_DIST_THRESH_NORM=0.005, safely between the two. After recalibration, lostStripeCount=0 on every one of the 332 photos.", "newFpBucketing": "newFpBuckets is computed over ALL TTA-rules unmatched stripes across all 332 photos (duplicateOfLabeledStripe + farFromAnyLabel = total TTA-rules unmatched stripes, larger than newFpCount below because newFpCount only counts the DELTA beyond baseline's own unmatched count per photo -- some unmatched TTA stripes existed in baseline too). Each unmatched stripe's distance to the NEAREST label polyline (matched or not), normalized by diag. duplicateOfLabeledStripe = within 0.01 (a real stripe, double-detected by a different TTA pass than the one filterStripes already kept -- not a hallucination, but also not a new find; a production fusion would need a final matched-stripe dedupe pass, which this test intentionally does not add so the measured precision cost is conservative/honest). farFromAnyLabel = the stripe is not sitting on any labeled stripe -- eyeballed sample (sail159-tta-fp-gallery/, sail159-tta-new-fp-samples.json) found both real cases (a genuinely visible bottom draft stripe with NO human label at all -- direct bad-labels evidence, test 0.2's territory) and true junk (a collapsed/degenerate squiggle near the head).", "passContribution": "Per TTA pass: photosWithDetections (of 332), totalDetections (raw stripe candidates before fusion), clustersFounded (how many fused output clusters that pass supplied the founding/highest-confidence member for -- i.e. which passes actually drove the merged result vs contributed nothing).", "localVsDeployedBaselineCaveat": "This script's baselineRules recall (summary.baselineRules.meanRecall) is LOCAL CPU inference on the same .pt file via Ultralytics, run in this session -- NOT the deployed Railway sidecar. It is close to but not identical to gate0-ablation-scoreboard.json's summary.rules.meanRecall=0.9575 (deployed sidecar over HTTP). Compare TTA against THIS file's own baselineRules number (same model file, same code path, same run) for an apples-to-apples read; cite 0.9575 only as the separately-known deployed-sidecar reference point. One further stage difference: this script's filterStripes() calls have no boomLine (gate0-ablation-scoreboard.json's rules stage used the sidecar's detected boom line) -- a minor stage difference, not rerun for this test." }, "summary": { "baselineRaw": { "n": 332, "meanRecall": 0.9956325301204819, "meanPrecision": 0.886545449196052, "meanDistNorm": 0.0079192314310619 }, "baselineRules": { "n": 332, "meanRecall": 0.9484939759036144, "meanPrecision": 0.9811746987951807, "meanDistNorm": 0.0102100833496238 }, "ttaRaw": { "n": 332, "meanRecall": 0.9993975903614458, "meanPrecision": 0.6070429501892016, "meanDistNorm": 0.005920461219846591 }, "ttaRules": { "n": 332, "meanRecall": 0.980471887550201, "meanPrecision": 0.8998565691336778, "meanDistNorm": 0.008671888856604953 } }, "recoveredStripeCount": 35, "lostStripeCount": 0, "newFpCount": 132, "newFpBuckets": { "duplicateOfLabeledStripe": 66, "farFromAnyLabel": 91 }, "passContribution": { "base": { "photosWithDetections": 332, "totalDetections": 1291, "clustersFounded": 369 }, "scale960": { "photosWithDetections": 332, "totalDetections": 1286, "clustersFounded": 898 }, "scale1280": { "photosWithDetections": 332, "totalDetections": 1832, "clustersFounded": 752 }, "bottomcrop": { "photosWithDetections": 62, "totalDetections": 112, "clustersFounded": 95 }, "lowconf": { "photosWithDetections": 332, "totalDetections": 1539, "clustersFounded": 138 } }, "perPhoto": [ { "id": "0236f5a7-9533-4c32-8304-0682421de226", "nLabelStripes": 4, "nBaselineRaw": 4, "nTtaFused": 6, "baselineRaw": { "recall": 1, "precision": 1, "meanDistNorm": 0.0014928059825874479, "nLabel": 4, "nModel": 4, "unmatchedModelIdx": [] }, "baselineRules": { "recall": 1, "precision": 1, "meanDistNorm": 0.0014928059825874461, "nModel": 4 }, "ttaRaw": { "recall": 1, "precision": 0.6666666666666666, "meanDistNorm": 0.0030569083021298273, "nLabel": 4, "nModel": 6, "unmatchedModelIdx": [ 4, 5 ] }, "ttaRules": { "recall": 1, "precision": 0.8, "meanDistNorm": 0.003056908302129828, "nModel": 5 }, "newFpDelta": 1 }, { "id": "050d4686-1302-489a-add5-a8eae2972140", "nLabelStripes": 3, "nBaselineRaw": 6, "nTtaFused": 12, "baselineRaw": { "recall": 1, "precision": 0.5, "meanDistNorm": 0.006132689398617742, "nLabel": 3, "nModel": 6, "unmatchedModelIdx": [ 1, 2, 3 ] }, "baselineRules": { "recall": 1, "precision": 0.6, "meanDistNorm": 0.0066032008004080905, "nModel": 5 }, "ttaRaw": { "recall": 1, "precision": 0.25, "meanDistNorm": 0.00200406877261074, "nLabel": 3, "nModel": 12, "unmatchedModelIdx": [ 3, 4, 5, 6, 7, 8, 9, 10, 11 ] }, "ttaRules": { "recall": 1, "precision": 0.42857142857142855, "meanDistNorm": 0.0020040687726107414, "nModel": 7 }, "newFpDelta": 2 }, { "id": "052f1898-16db-400b-9365-54468834a80d", "nLabelStripes": 4, "nBaselineRaw": 6, "nTtaFused": 6, "baselineRaw": { "recall": 1, "precision": 0.6666666666666666, "meanDistNorm": 0.0012149460072397578, "nLabel": 4, "nModel": 6, "unmatchedModelIdx": [ 4, 5 ] }, "baselineRules": { "recall": 1, "precision": 1, "meanDistNorm": 0.001214946007239758, "nModel": 4 }, "ttaRaw": { "recall": 1, "precision": 0.6666666666666666, "meanDistNorm": 0.002742428553226216, "nLabel": 4, "nModel": 6, "unmatchedModelIdx": [ 4, 5 ] }, "ttaRules": { "recall": 1, "precision": 1, "meanDistNorm": 0.0027424285532262165, "nModel": 4 }, "newFpDelta": 0 }, { "id": "0f6e6446-8fb8-455d-bfbe-de26f09eefd2", "nLabelStripes": 4, "nBaselineRaw": 4, "nTtaFused": 6, "baselineRaw": { "recall": 1, "precision": 1, "meanDistNorm": 0.002420248845098659, "nLabel": 4, "nModel": 4, "unmatchedModelIdx": [] }, "baselineRules": { "recall": 1, "precision": 1, "meanDistNorm": 0.002420248845098658, "nModel": 4 }, "ttaRaw": { "recall": 1, "precision": 0.6666666666666666, "meanDistNorm": 0.0018789140947199179, "nLabel": 4, "nModel": 6, "unmatchedModelIdx": [ 4, 5 ] }, "ttaRules": { "recall": 1, "precision": 1, "meanDistNorm": 0.001878914094719917, "nModel": 4 }, "newFpDelta": 0 }, { "id": 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