feat(trainer): phase-B body-type classifier — trainer job on the collector host + the classifier stage on the booth

apps/trainer (parking-trainer): inspect / train / evaluate / publish. Reads the wash
collector's SQLite + crops read-only off its volume; time split (validation = newest
slice); thin classes dropped; damped class weights; `features` mode (frozen ImageNet
backbone, on-disk feature cache, seconds to retrain) and `finetune` mode (light
augmentation). CPU-only torch from PyTorch's wheel index. ONNX export checked against
the torch model; NO model file below the validation floor (exit 3, report still written);
exit 2 = not enough labels. `evaluate` scores a shipped model on labels reviewed after
training + the unlabelled pile; `publish` PUTs a version folder to a Gitea generic package.
Light core deps; the `train` extra is heavy — CI syncs without it, torch tests skip.

apps/vision: BodyTypeClassifier (bodytype.onnx + sidecar = the preprocessing contract:
crop margin, input size, RGB 0-255, normalisation inside the graph) and
RefinedVehicleDetector over YOLOX — refines only `car` or a class the classifier trained
on, min-confidence, `detector_class` on the result; path set but no file = phase B off
without an error; a broken file is a health detail. models/bodytype.version (tracked,
empty) pins the published version the Dockerfile fetches at build (BuildKit secret;
a pin that cannot be fetched fails the build). Verified: a trainer model gives identical
probabilities inside the vision service; both images built and smoke-tested.

Delivery: parking-trainer image in build-images.yml, the `trainer` compose profile on the
collector stack (CPU, read-only data, TRAINER_OUT), commented TRAINER_OUT/PUBLISH_TOKEN in
the wash-collector stack, .dockerignore for both Python contexts, trainer deps synced in CI.

Wiki: bodytype-classifier-training rewritten as built (+ one fleet model not per site,
secrets/access, where the crops live), opencv-anpr-service §Phase B, vision-review-outbox,
vision-service-packaging, fleet-deployment-komodo, index, log.

Claude-Session: https://claude.ai/code/session_01FWncR69HgGPuei1dLrW3cU

komodo/resources.toml

@@ -143,6 +143,11 @@ COLLECTOR_BIND=100.75.184.156
 # to keep in sync, and rotating a booth touches one secret. The booth id is the booth's
 # pseudonymous CARWASH_REVIEW_BOOTH_ID, never a site name. Add a pair per booth.
 COLLECTOR_BOOTH_TOKENS=booth-2:[[wash_review_token_booth_2]]
+# Phase-B trainer (profile "train", a one-off job on this host — never started by the deploy).
+# Its output folder on the host, and the Gitea token `publish` uses to upload a passing model to
+# the generic package registry (package:write). Uncomment when the first model is to be published.
+#TRAINER_OUT=/opt/parking/models
+#TRAINER_PUBLISH_TOKEN=[[gitea_package_write_token]]
 COLLECTOR_REVIEWER_USER=reviewer
 COLLECTOR_REVIEWER_PASS=[[wash_collector_reviewer_pass]]
 """