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>
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