forked from vnguyen/product-recognition-demo

History

kiyo9w 0376a74418 docs(labels): the README described the pre-correction label set Three claims were stale: it said l12 has no facing on the shelf (facings #46-48 now carry it), that the file uses sku_l01-sku_l11, and that the set is 61 in-catalog / 48 unknown across 11 SKUs. It is 64 / 45 across 12. The #46-48 note it pointed at as an open question is now answered, so the note reference goes with it.	2026-09-29 03:33:16 +09:00
..
image6.json	fix(labels): the tare facings are sku_l12, not unknown	2026-09-29 03:09:15 +09:00
README.md	docs(labels): the README described the pre-correction label set	2026-09-29 03:33:16 +09:00

kiyo9w 0376a74418 docs(labels): the README described the pre-correction label set

Three claims were stale: it said l12 has no facing on the shelf (facings #46-48
now carry it), that the file uses sku_l01-sku_l11, and that the set is 61
in-catalog / 48 unknown across 11 SKUs. It is 64 / 45 across 12.

The #46-48 note it pointed at as an open question is now answered, so the note
reference goes with it.

2026-09-29 03:33:16 +09:00

image6.json

fix(labels): the tare facings are sku_l12, not unknown

2026-09-29 03:09:15 +09:00

README.md

docs(labels): the README described the pre-correction label set

2026-09-29 03:33:16 +09:00

README.md

Shelf label files

Ground truth for the identity evaluators (code/eval_identity.py, code/eval_live.py) and for the evidence scripts in docs/research/2026-09-28-recognition-architecture/evidence/.

{"image": "add/image6.jpg", "version": 2, "facings": [ ... ]}

image is the photo the boxes index into. facings is one row per detected product facing, in that photo's own pixel coordinates.

Row schema

Field	Required	Meaning
`index`	yes	Row identity. Stable across label revisions, so a run's per-facing log joins to truth by `index`, never by array position.
`box`	yes	`[x1, y1, x2, y2]` in `image` pixels.
`label`	yes	Canonical SKU id (`sku_l01`), `unknown` for a facing that is not in the catalog, or `exclude` for a facing no lane can be scored on.
`source`	yes	How the label was decided. `v1-confirmed-visual` = the v1 label was checked against the package and kept; `visual-audit-2026-09-28` = the label was re-read from the package in the 2026-09-28 audit.
`v1_label`	yes	The label this row carried in v1. Kept so claims computed on v1 stay reproducible and the 26 corrections stay auditable.
`note`	when `label != v1_label`	Why the label changed. Also on a few unchanged rows that need a remark.
`match`	no	v1 provenance only: the field DECISION.md §3 reads as a match IoU, and as evidence that v1 labels were propagated from tag or box matching rather than read off packages. Values here are 0.0, 0.911, 1.0. No scorer reads it; it is carried through unchanged for traceability.

label takes a canonical SKU id, unknown, or exclude. This file uses sku_l01-sku_l12. exclude rows stay in the file so the facing inventory is complete; every scorer drops them before computing a rate. The evaluators read index, box, and label; the provenance fields are additive.

image6.json is the corrected v2 set: 111 rows, 109 scored (2 exclude), 64 in-catalog across 12 SKUs, 45 unknown. 26 labels changed from v1, and three more changed on 2026-09-29: facings #46-48 were unknown with the note "possibly what stock.csv calls l12", and the l12 reconciliation settled it, so they now carry sku_l12. It is a copy of docs/research/2026-09-28-recognition-architecture/evidence/image6_v2.json; edit the evidence copy and re-copy, so the two cannot drift.

Schema check

DECISION.md §7 sets the rule for every label file:

Every label row carries source, confidence, and labeler. A label file without them fails a schema check.

The current file does not meet it. All 111 rows carry source and v1_label, but none carries confidence or labeler: the file records where each label came from and what it replaced, not a per-row certainty or a labeler name. Inventing confidence values after the fact would be worse than the gap, so they are absent rather than guessed.

Tier-2 labels must add both. confidence is the labeler's own certainty for that row; labeler names the person or pass that produced it, because Tier 2 is double-labelled and a disagreement has to be traceable to a pass. Tier 1 (image6_v2.json) is dev-only and its numbers never ship, so it stays as it is.