product-recognition-demo/recognization/docs/research/2026-09-28-recognition-architecture/evidence

Evidence scripts (2026-09-28 audit)

Read-only against product code: every script imports from code/ and writes only into this folder. Run from recognization/ with .venv/bin/python.

eval/labels/image6.json is the corrected image6 label set (26 of 111 v1 labels changed; every row carries v1_label, source, and a note where changed). image6-v1-labels-overlay.jpg shows the v1 labels on the shelf; green = catalog label, red = unknown. sheet.py regenerates per-SKU contact sheets.

Step	Command (prefix E=docs/research/2026-09-28-recognition-architecture/evidence)	Produces
Original matcher, both catalogs, full/live/live-with-unscaled-boxes	.venv/bin/python $E/diag.py --stock stock-lopia-clean --labels $E/../../../eval/labels/image6.json [--live] [--bug] --out $E/run_stock-lopia-clean_<full|live|livebug>.pkl (repeat with --stock stock-lopia)	run_*.pkl
Score them under v1 or v2 labels	.venv/bin/python $E/analyze.py <labels.json> $E/run_*.pkl	identity top-1, argmax, current gate
Matcher variants	.venv/bin/python $E/variants.py --stock stock-lopia-clean --labels $E/../../../eval/labels/image6.json [--live] --scales 200,400 --root --geom affine --out $E/v_ms_root_aff_<full|live>.pkl (also --tmpl-h 250/400, no flags = raw)	v_*.pkl
Variant ranking + open-set frontier	.venv/bin/python $E/an2.py $E $(basename -a $E/v_*.pkl) (LABELS=image6_v2_lfo.json for the leave-facing-out runs)	correct at 100/95/90 % precision
Leave-facing-out catalog	.venv/bin/python $E/lfo_setup.py, then variants.py --stock $E/cat_<pack|full> --labels $E/image6_v2_lfo.json --scales 200,400 --root --geom affine [--live] --out $E/lfo_cat_<pack|full>_<full|live>.pkl	lfo_*.pkl
Unrelated-shelf distractors	YOLO_OFFLINE=1 .venv/bin/python $E/distract.py stock-lopia-clean $E/cat_full	distract.pkl
Catalog growth, exhaustive vs descriptor index	YOLO_OFFLINE=1 .venv/bin/python $E/scale.py	stdout
DINO retrieval under growth	YOLO_OFFLINE=1 .venv/bin/python $E/dino_scale.py	stdout
OCR read-rate	.venv/bin/python $E/ocr_exp.py	ocr.pkl
OCR signatures, then fusion	.venv/bin/python $E/ocr_fuse.py && .venv/bin/python $E/fuse_eval.py	sig.pkl, stdout
Per-SKU contact sheets + the labelled shelf	.venv/bin/python $E/sheet.py <outdir>	gt_<label>.jpg, shelf_labels.jpg

Timings in DECISION.md come from scale.py (single process). The other scripts ran up to ten processes at once, so their ms figures are relative only.

Verified 2026-09-28

Every command above was run from recognization/ and produced its listed output. Two rows were wrong as written:

• The table said python; the python on this machine's PATH has no cv2, so every row failed on import. It is .venv/bin/python.
• an2.py takes the folder and then file names inside it, so v_*.pkl expanded in the working directory, where those files do not live. It is $(basename -a $E/v_*.pkl).

sheet.py was missing from the table. It takes its output directory as a positional argument, not a flag: sheet.py --help reads --help as a directory name, writes nothing, and still exits 0. analyze.py and an2.py with no arguments raise IndexError; neither validates its own usage.

v1 labels are no longer a file on disk — eval/labels/image6.json is the v2 set. To score a run under v1, rebuild it from the v1_label field:

.venv/bin/python -c "import json;d=json.load(open('$E/../../../eval/labels/image6.json'));\
[f.update(label=f.pop('v1_label')) for f in d['facings']];\
json.dump(d,open('/tmp/image6_v1.json','w'),ensure_ascii=False)"

That reconstruction reproduces DECISION.md's v1 numbers exactly: analyze.py gives ungated top-1 26/35 at both resolutions and the current gate 24/35 full, 23/35 live (§2 row 15, F-01).