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docs(decision): correct the record against the re-measured evidence

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The audit's own numbers were right; several claims built on them were not, and
the advisors found each one:

- 'wins both axes at both resolutions' - false at live, where the lane trades
  59% coverage for 93% precision against Van's 66% at 39%. Stated as the trade
  it is.
- G1's bootstrap now exists: the local lane's correct set is a strict superset
  of Van's on the stream, so the 95% lower bound is a hard +0.0 pp - a floor of
  that pairing, not a margin. tracks_final.json is level with Van and does not
  clear it.
- The far-unknown rate is 0.066% on 4580 crops at the shipped configuration,
  not 0.30% on 2650 at the old one.
- The E-3 ablation compared text-presence *and* a different argmax; both arms
  now go through the same DecisionPolicy ordering. Text adds 9-10 pp of
  coverage at full resolution while raising precision.
- The sweep table swept one axis while pinning the other; replaced with the
  joint sweep.
- The two Re-measured tables are dated snapshots, labeled as such.
This commit is contained in:
kiyo9w 2026-09-29 09:28:19 +09:00
parent 730a5e782b
commit 7736465bee
2 changed files with 132 additions and 43 deletions
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57
recognization/code/test_engine.py
View file

@ -178,6 +178,63 @@ class TextLaneSignatureTests(unittest.TestCase):
"sku_l08",
)
def test_one_shared_pair_is_refused_by_the_floor_alone(self):
"""Everything but the evidence floor says answer, and it says no.
"アンース" holds one pair the catalog knows - "ース" - so the row scores
a perfect 1.0 with the whole margin over an empty runner-up, and the
read carries three pairs, so `carries_identity` passes as well. Only
`TEXT_MIN_SHARED` stands between a coincidental pair and a named
product, which is why the test asserts the score is 1.0: it pins the
floor as the only thing doing the refusing.
"""
from identity_lanes import DecisionPolicy, TextLane
lane = TextLane.from_catalog(
[{"sku_id": "sku_x", "product_name": "オイスターソース"}],
"no-such-dir",
ocr_engine=None,
)
read = "アンース"
self.assertEqual(lane.scores_from_text(read)["sku_x"], 1.0)
self.assertIsNone(
DecisionPolicy(signatures=lane.signatures).decide(
[], [("sku_x", 1.0)], text_read=read
)["best_sku"],
"one shared pair is not enough to name a row",
)
def test_a_thin_read_still_vetoes_even_though_it_cannot_name(self):
"""The floor is a floor on naming, not on contradicting.
"北海道塩" shares three bigrams with sku_b - one short of the floor -
so it cannot name that row. It is still the row the text prefers over
the local answer, and the local lane clears its own line at 5.0 against
a line of 4.0, so if the floor also sat under the veto this would come
back as sku_a: a three-bigram read would have rescued a match the
geometry earned.
"""
from identity_lanes import DecisionPolicy, TextLane
lane = TextLane.from_catalog(
[
{"sku_id": "sku_a", "product_name": "キューピー ハーフマヨネーズ 400g"},
{"sku_id": "sku_b", "product_name": "北海道塩"},
],
"no-such-dir",
ocr_engine=None,
)
read = "北海道塩"
ranked = sorted(lane.scores_from_text(read).items(),
key=lambda kv: kv[1], reverse=True)
self.assertEqual((ranked[0][0], ranked[0][1]), ("sku_b", 1.0))
self.assertIsNone(
DecisionPolicy(signatures=lane.signatures).decide(
[("sku_a", 5.0)], ranked, text_read=read
)["best_sku"],
"a read too thin to name a row still refuses the local answer",
)
class FakeDetector:
"""Records concurrency and returns fixed boxes."""

118
recognization/docs/research/2026-09-28-recognition-architecture/DECISION.md
View file

@ -59,8 +59,8 @@ store-scoped, enrolled catalog, with live-video evidence accumulation**.
becomes retrieval plus verification (§6).
- **Open set is the dominant difficulty, and it comes in two kinds.** *Far*
unknowns (other categories) are rejected easily **once the accept lines are
calibrated against them**: 3.09 % at the old lines, 0.30 % at the shipped
ones, on 2650 crops from 60 unrelated Wikimedia supermarket photos, at a
calibrated against them**: 3.09 % at the old lines, 0.066 % at the shipped
ones, on 4580 crops from 64 unrelated Wikimedia supermarket photos, at a
threshold that keeps 42 of 48 correct image6 labels. The audit's 0.2-0.7 %
was measured on 602 crops from the `add/` photos with the audit's own variant
matcher; the shipped lane at the shipped lines was 3.09 % until 2026-09-29
@ -399,14 +399,17 @@ with always-argmax, run on that same catalog.
| G8 runtime | server identity latency p95 per crop; server memory | ≤ 60 ms; within the demo host's budget |
Status 2026-09-29, offline on image6 plus the Wikimedia distractor sets:
**G1 passes on the stream** (70-74 % coverage against Van's 63 % on the same
stream and catalog, 0 wrong against Van's 7). **G2 passes** (95 % precision
full, 97 % live). **G3's far-unknown half passes** (0.30 % on 2650 unrelated
crops); its near-unknown half is measured on image6 only, where the size gate
rejects the documented 450 g case. **G8 passes** (identity p95 well inside
60 ms). **G4, G5, G6, G7 are not yet measured on held-out data** - Tier 2 does
not exist, and G4/G5 need a recorded walk. Nothing here is a device-path
result.
**G1's point estimate passes on the stream** (70-74 % coverage against Van's
63 % on the same stream and catalog, 0 wrong against Van's 7), and the paired
bootstrap is now computed: the shipped lane's correct set is a strict superset
of Van's on 27 facings, so the 95 % lower bound is a hard +0.0 pp - a floor of
this pairing, not a margin, and it clears the -2 pp half. **G2 passes** (95 %
precision full, 97 % live). **G3's far-unknown half passes** (0.066 % on 4580
unrelated crops at the shipped configuration); its near-unknown half is
measured on image6 only, where the size gate rejects the documented 450 g case.
**G8 passes** (identity p95 well inside 60 ms). **G4, G5, G6, G7 are not yet
measured on held-out data** - Tier 2 does not exist, and G4/G5 need a recorded
walk. Nothing here is a device-path result.
Not complete until G1–G7 pass on the device path (Tier 3), not just offline.
@ -647,12 +650,12 @@ scores, always answered. Every "vs Van" claim in this record uses that row.
matcher 83% 79% 74% 61% 54% 33
local 83% 85% 60% 59% 93% 3
Against Van's real contract the local lane wins both axes at both resolutions:
75% at 91% against 64% at 38% (full), and 59% at 93% against 66% at 39%
(live). At live resolution Van answers more facings correctly - 66% against
59% - and is wrong on 67 of them to the local lane's 3. That is the trade the
decision makes deliberately: coverage is a gate (G1) and precision is not
negotiable, so the lane abstains where Van guesses.
Against Van's real contract the local lane wins both axes at full resolution:
75% at 91% against 64% at 38%. At live resolution it trades coverage for
precision - 59% at 93% against Van's 66% at 39%. Van answers more facings
correctly there and is wrong on 67 of them to the local lane's 3. That is the
trade the decision makes deliberately: coverage is a gate (G1) and precision is
not negotiable, so the lane abstains where Van guesses.
**One accept line serves both resolutions.** An earlier draft of this section
claimed full resolution needed a line near 6 to reach 95%, leaving 28/61
@ -674,8 +677,8 @@ audit measured, the 4.0 row is the shipped line:
6.0 59% 97% 1
The line is still provisional - it is tuned on Tier 1 and needs Tier-2
calibration - but it does not need to differ by input path, and the design
beats Van on both axes at both resolutions today.
calibration - but it does not need to differ by input path, and at full
resolution the design beats Van on both axes today.
The live count was pacing-dependent, and the cause was measurable: OCR ran on
every crop at every attempt, taking a crop from 5 ms to 25 ms and throughput
@ -738,6 +741,14 @@ Van names fewer facings than the local lane and is wrong on 7 of the 53 it
does name, plus 6 accepts of products the catalog does not hold. G1 asks the
new system to be no worse than Van on coverage; it is better on both axes.
The paired bootstrap (`code/score_stream.py --van ...`) grades both runs on the
same 27 facings: the local lane's correct set is a strict superset of Van's, so
the difference is +7.4 to +11.1 pp and the 95 % lower bound is a hard +0.0 pp.
That clears G1's -2 pp half, but read it as a floor of this pairing rather than
a margin - no resample of a superset can go negative. One of the five recorded
runs (`tracks_final.json`) is 17/27, level with Van, and does not clear the
bound; the other four do.
Two live-path bugs surfaced while measuring this, both fixed:
- **Inherited labels carried frame id 0.** `_inherit_labels` set `label_box`
@ -807,6 +818,11 @@ unanswerable before and after.
### Re-measured 2026-09-29
This is the state after the text lane's evidence floor landed and before the
accept lines were calibrated against unrelated shelves; the current numbers are
in § The baseline is Van's contract above (local 75% at 91% full, 59% at 93%
live). Kept as the dated snapshot that shows what the floor alone changed.
The text lane's evidence floor (shared bigrams between the read and the winning
row's name) and the local lane's signature pass-through landed, and the
decision ceiling was corrected: it divided a numerator that counted unenrolled
@ -823,16 +839,20 @@ facings by the enrolled count, so it read 117%.
local 83% 91% 64% 62% 93% 3
**E-3 answered.** `--no-text` runs the same crops and catalog without the text
lane, which is the only way this question is answerable. Full resolution:
end-to-end 77% at 91% precision with text, 61% at 66% without (5 false accepts
against 20). Live: 62% at 93% against 56% at 84% (3 against 7). The lane adds
16 pp and 6 pp of coverage while *raising* precision, against E-3's +5 pp bar.
It is not a veto-only lane.
lane, which is the only way this question is answerable. Re-measured 2026-09-29
after the ablation was corrected: the first version compared text-presence
*and* a different argmax, because it re-sorted candidates by confidence value
where the lane ranks by its own key. Both arms now go through the same
`DecisionPolicy` ordering, so text is the only variable.
Re-measured 2026-09-29 with the calibrated accept lines: 66% at 95% precision
with text, 55% at 81% without (2 false accepts against 8). The lane still adds
11 pp of coverage while raising precision by 14 pp, so the answer is unchanged
and the margin is wider than the audit's.
Full resolution: end-to-end 66% at 95% precision with text, 56% at 86% without
(2 false accepts against 6). Live: 56% at 97% against 48% at 100% (1 against
0). The lane adds 9-10 pp of coverage at full resolution while raising precision
by 10 pp, against E-3's +5 pp bar. At live resolution it adds 8 pp of coverage
and costs 3 pp of precision - one false accept - which is the trade the accept
lines already price. It is not a veto-only lane.
Reproduce: `code/eval_identity.py --lanes local --live [--no-text]`.
**Name-only signatures, and why.** The architecture sketch above originally
said the text signature was "name + OCR of its packshot". That was tried and
@ -907,7 +927,9 @@ Every threshold in this document was fitted on image6, where each crop is a
facing of a product the shelf actually holds. That is a closed-set tuning set,
and it hid the far-unknown rate completely: the shipped lane accepted 82 of
2650 crops from 60 Wikimedia Commons supermarket photos of milk, soy and snack
shelves - 3.09 %, against G3's 1 %.
shelves - 3.09 %, against G3's 1 %. (The crop count is the old full-resolution,
`detect_conf=0.25` measurement; at the shipped configuration the same photos
yield 4580 crops and the shipped lines accept 3 of them - 0.066 %.)
Those photos share nothing with the LOPIA catalog, so every accept is a false
accept by construction. Two mechanisms produced them:
@ -919,23 +941,33 @@ accept by construction. Two mechanisms produced them:
- **The local lane accepted pure geometry.** The residual accepts were matches
with no text overlap at all, scoring 2.26-4.81 against an accept line of 2.0.
Both lines moved: `TEXT_MIN_SHARED` 2 -> 4, `LOCAL_ACCEPT` 2.0 -> 4.0. Measured
on 2650 distractor crops and 109 image6 facings:
Both lines moved: `TEXT_MIN_SHARED` 2 -> 4, `LOCAL_ACCEPT` 2.0 -> 4.0. The
joint sweep below re-measures both axes together through the shipped
`DecisionPolicy` (`code/sweep_accept_lines.py`), on 4580 distractor crops from
64 unrelated Wikimedia shelf photos at the shipped configuration (1280-long-side
frames, `LiveSettings.conf=0.1`) and 109 image6 facings. An earlier version of
this table swept only `TEXT_MIN_SHARED` while pinning the accept line at 4.0, so
its "2.0" rows were not the old-line baseline and its "4.0 | 4" row duplicated
"2.0 | 4"; the numbers below replace it.
| `LOCAL_ACCEPT` | `TEXT_MIN_SHARED` | image6 shown / correct | precision | false accepts |
|---|---|---|---|---|
| 2.0 | 2 | 46 / 43 | 93.5 % | 82 (3.09 %) |
| 2.0 | 4 | 44 / 42 | 95.5 % | 8 (0.30 %) |
| **4.0** | **4** | **44 / 42** | **95.5 %** | **8 (0.30 %)** |
| 5.0 | 6 | 39 / 38 | 97.4 % | 3 (0.11 %) |
| `LOCAL_ACCEPT` | `TEXT_MIN_SHARED` | image6 full shown / correct | precision | coverage | image6 live precision | false accepts |
|---|---|---|---|---|---|---|
| 2.0 | 2 | 54 / 49 | 90.7 % | 76.6 % | 93.0 % | 60 (1.31 %) |
| 2.0 | 4 | 52 / 48 | 92.3 % | 75.0 % | 97.4 % | 8 (0.17 %) |
| 4.0 | 2 | 46 / 43 | 93.5 % | 67.2 % | 92.7 % | 55 (1.20 %) |
| **4.0** | **4** | **44 / 42** | **95.5 %** | **65.6 %** | **97.3 %** | **3 (0.07 %)** |
| 4.0 | 6 | 39 / 38 | 97.4 % | 59.4 % | 100 % | 0 (0.00 %) |
| 5.0 | 6 | 36 / 36 | 100 % | 56.2 % | 100 % | 0 (0.00 %) |
4.0/4 is the only region passing both G2 (>= 95 % precision) and G3 (<= 1 %
far-unknown) with coverage left. On the shipped evaluator that is 66 %
end-to-end at 95 % precision with 2 false accepts, against 75 % / 92 % / 4 at
the old 2.0 line - the coverage is what the precision costs, and G2 is the gate
that says to pay it. At live resolution the same lines give 56 % end-to-end at
97 % precision with 1 false accept, so the calibration holds on the path the
phone actually uses and not only at full resolution.
The shipped 4.0/4 row is the last one that passes G2 (>= 95 % precision) with
coverage left; 4.0/6 and 5.0/6 also pass both gates but give up 6-9 points of
coverage for precision the gate does not ask for. The old 2.0/2 line is 11
points of coverage richer and fails G3 outright at 1.31 %. On the shipped
evaluator 4.0/4 is 66 % end-to-end at 95 % precision with 2 false accepts,
against 75 % / 92 % / 4 at the old 2.0 line - the coverage is what the precision
costs, and G2 is the gate that says to pay it. At live resolution the same lines
give 56 % end-to-end at 97 % precision with 1 false accept, so the calibration
holds on the path the phone actually uses and not only at full resolution.
Reproduce: `.venv/bin/python code/eval_distractors.py --stock stock-lopia-clean
--write data/external-shelves/wikimedia-commons data/external-shelves/wikimedia-commons-2`.