feat: a store's fine-tuned head, and the two-stage gate it needs

The head is now part of a store rather than a script: `finetune_identity.py --stock
stock-lopia` trains on that store's enrolled views (27 views over 7 products, 12
augmented copies each) and writes `<stock>/identity_head.pt`, which ProductGallery
loads automatically.

The first attempt exposed a trap worth recording. A discriminative head has no "none
of the above": trained with an angular margin to separate seven products, it maps
everything confidently onto those seven, including cartons it has never seen.

  raw features, threshold 0.55   home shelf 78% accepted   foreign shelves  5% accepted
  trained head, threshold 0.40   home shelf 58% accepted   foreign shelves 44% accepted

Rejection - the gallery's whole point - disappears. The fix needs no retraining: the
two mechanisms fail in opposite directions, so raw features decide *whether* to answer
(they generalise) and the head decides *what* to answer (it discriminates). Measured:
47% accepted on the home shelf, 5% on foreign ones. Lower coverage than raw-only,
which is the honest trade.

The head also needs its own threshold (0.40 against the raw 0.55): a trained head's
similarity scale is its own, and reusing the raw line rejected almost everything it
said.

175 tests pass.

Co-Authored-By: Devin <158243242+devin-ai-integration[bot]@users.noreply.github.com>

recognization/docs/EXTERNAL-EVAL.md

@@ -232,3 +232,54 @@ What is settled is the mechanism: **a head trained on labels the pipeline can pr
 itself** - tag-named facings, plus operator confirmations - moves fine-grained
 identity from useless to usable, and a rejection threshold turns it into something a
 store can act on.
+
+
+---
+
+# Part 4: making it usable, and the trap that came with it
+
+The head is now part of a store: `python code/finetune_identity.py --stock stock-lopia`
+trains on that store's enrolled views and writes `<stock>/identity_head.pt`, which
+`ProductGallery` loads automatically. The first attempt exposed a trap worth writing
+down.
+
+## A trained head destroys rejection
+
+Measured on the same two sets (the home shelf it was enrolled from, and 20 shelves
+whose products are absent):
+
+| | home shelf accepted | foreign shelves accepted |
+| --- | --- | --- |
+| raw features, threshold 0.55 | 78 % | **5 %** |
+| trained head, threshold 0.40 | 58 % | **44 %** |
+
+A discriminative head has no "none of the above": trained with an angular margin to
+separate seven products, it maps *everything* confidently onto its seven prototypes,
+including cartons it has never seen. Rejection, which was the gallery's whole point,
+disappears.
+
+## The fix is two stages, and it needs no retraining
+
+The two mechanisms fail in opposite directions, so each does what it is good at:
+
+* **raw features decide whether to answer** (they generalise: 5 % false accepts on
+  strangers),
+* **the head decides what to answer** (it discriminates: 73 % balanced on
+  fine-grained products against 1 % raw).
+
+| | home shelf accepted | foreign shelves accepted |
+| --- | --- | --- |
+| **raw gate, then head ranking** | 47 % | **5 %** |
+
+The home-shelf acceptance is lower than raw-only because the gate is strict, which is
+the honest trade: fewer claims, and the claims are worth more.
+
+## Still open
+
+* A joint accuracy number on the LOPIA store needs held-out labels there; its
+  enrolment views came from the facings the tags named, so scoring on those would be
+  scoring on the training set.
+* Training the head *with* out-of-distribution negatives (crops from other stores)
+  should let it keep rejecting on its own, which would remove the need for the gate.
+* Everything above is still one photograph per store. The second visit remains the
+  measurement that settles it.