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4b00323ae2
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10 changed files with 10988 additions and 2 deletions
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@ -0,0 +1,40 @@
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{
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"send": {
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"coverage": [
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0.63,
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0.704,
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0.667
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],
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"coverage_median": 0.667,
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"precision": [
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0.773,
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0.884,
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0.973
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],
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"precision_median": 0.884,
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"wrong": [
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0,
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0,
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0
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]
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},
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"result": {
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"coverage": [
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0.63,
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0.63,
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0.704
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],
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"coverage_median": 0.63,
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"precision": [
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0.857,
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0.825,
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0.9
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],
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"precision_median": 0.857,
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"wrong": [
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0,
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0,
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0
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]
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}
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}
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@ -233,7 +233,8 @@ frame / native crop
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│
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│
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├─ text lane: OCR (upsampled ×2 when crop height < 200 px)
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├─ text lane: OCR (upsampled ×2 when crop height < 200 px)
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│ ─► bigram similarity to every catalog row's text signature
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│ ─► bigram similarity to every catalog row's text signature
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│ (name + OCR of its packshot), including rows with no image
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│ (the row's name only, including rows with no image; adding
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│ the packshot's own OCR was tried and rejected - see below)
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│
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│
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▼
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▼
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decision: P(correct | local, text) from a small calibrated model
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decision: P(correct | local, text) from a small calibrated model
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@ -691,6 +692,19 @@ The 12 s numbers are within run-to-run noise; the short clip is where the
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pacing sensitivity lived, and it is now flat across pacings. Precision stays
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pacing sensitivity lived, and it is now flat across pacings. Precision stays
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100% and 0 unknowns are accepted at every pacing.
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100% and 0 unknowns are accepted at every pacing.
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Re-measured 2026-09-29 as medians of three 12 s runs per pace, through the
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shipped server with the calibrated accept lines
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(`data/evaluations/2026-09-29-session/pace-medians/`):
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pace coverage (3 runs) median precision (3 runs) median wrong
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send 63 / 70 / 67 % 67 % 77 / 88 / 97 % 88 % 0
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result 63 / 63 / 70 % 63 % 86 / 83 / 90 % 86 % 0
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The two paces land within 4 pp of each other on coverage and 2 pp on precision,
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with 0 wrong labels in all six runs. The pacing sensitivity the audit measured
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is gone, and the spread across runs is now the run-to-run noise the audit said
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the 12 s numbers were inside.
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**The stream path now has a G1 number.** `code/score_stream.py` scores a
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**The stream path now has a G1 number.** `code/score_stream.py` scores a
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recorded clip's labels against the ground truth, matching each track's
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recorded clip's labels against the ground truth, matching each track's
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`label_box` to the labelled facings. Three defects in the first version of
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`label_box` to the labelled facings. Three defects in the first version of
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@ -820,6 +834,14 @@ with text, 55% at 81% without (2 false accepts against 8). The lane still adds
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11 pp of coverage while raising precision by 14 pp, so the answer is unchanged
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11 pp of coverage while raising precision by 14 pp, so the answer is unchanged
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and the margin is wider than the audit's.
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and the margin is wider than the audit's.
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**Name-only signatures, and why.** The architecture sketch above originally
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said the text signature was "name + OCR of its packshot". That was tried and
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rejected: it is how l09 used to win l10's facings. l09's packshot prints
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"マヨネーズ", which is l10's actual name, so a row with a photographed packshot
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could explain a sibling's text better than the sibling's own name could. Every
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row in the catalog has a name, so the packshot adds nothing but that confusion,
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and `TextLane.scores_from_text` scores against the name alone.
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**E-2 answered.** `LocalLane` is RootSIFT-only; ORB and BRISK survive only in
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**E-2 answered.** `LocalLane` is RootSIFT-only; ORB and BRISK survive only in
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the `matcher` baseline lane, so there is nothing to drop from the shipped path.
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the `matcher` baseline lane, so there is nothing to drop from the shipped path.
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@ -879,8 +901,10 @@ out of "210g"), and percentages are excluded because "カロリー 50% カット
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claim about the contents. Measured on the corrected labels, full resolution:
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claim about the contents. Measured on the corrected labels, full resolution:
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precision 86% -> 91%, false accepts 8 -> 5, coverage unchanged at 79%.
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precision 86% -> 91%, false accepts 8 -> 5, coverage unchanged at 79%.
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### Far unknowns: the accept lines were tuned on a shelf that has no unknowns
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### Far unknowns: the accept lines were tuned on a shelf that has no unknowns
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Every threshold in this document was fitted on image6, where each crop is a
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facing of a product the shelf actually holds. That is a closed-set tuning set,
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and it hid the far-unknown rate completely: the shipped lane accepted 82 of
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and it hid the far-unknown rate completely: the shipped lane accepted 82 of
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2650 crops from 60 Wikimedia Commons supermarket photos of milk, soy and snack
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2650 crops from 60 Wikimedia Commons supermarket photos of milk, soy and snack
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shelves - 3.09 %, against G3's 1 %.
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shelves - 3.09 %, against G3's 1 %.
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