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

wiki/index.md

@@ -133,7 +133,7 @@ Counts: 4 sources · 19 entities · 47 concepts · 8 decision records.
 - [[dingtian-vs-mqtt]] — transport choice: direct HTTP/UDP now, MQTT parked until multi-lane scale.
 - [[session-model]] — business layer start: session = projection; transient-first; pay-on-foot. New event types.
 - [[vision-service]] — build a host-side ANPR + vehicle-verification service; replaces edge-LPR; scoped AGPL exception.
-- [[bodytype-classifier-training]] — phase B (SUV vs sedan) training path: trainer on the collector host → evaluate → publish weights → bake into the vision image → TAG bump; runs on the Xeon E3-1225 v5 CPU (feature-extraction head first, full fine-tune later), Quadro FX 3800 unusable, cloud GPU rejected (crops stay on premises). Decided 2026-09-07, NOT built.
+- [[bodytype-classifier-training]] — phase B (SUV vs sedan): `apps/trainer` trains on the collector host (time split, floor, features/finetune modes, feature cache) → report → publish to the Gitea generic package → `models/bodytype.version` pin bakes it into the vision image → TAG bump; sidecar = the preprocessing contract; CPU-only torch on the Xeon E3-1225 v5, Quadro FX 3800 unusable, cloud GPU rejected. Built 2026-09-07; first run waits for labels.
 - [[vision-service-packaging]] — the vision service lives in this monorepo (apps/vision/), separate process, wired into Turbo via a package.json shim; uv-managed Python.
 - [[event-streams-split]] — split the signed business ledger (ledger_events) from unsigned device telemetry (device_events).
 - [[desktop-shell-tauri]] — ✅ Tauri v2 chosen over Electron for the desktop kiosk shell; thin wrapper, server keeps all logic. Best case Ubuntu 26.04 LTS (resolves WebKitGTK); worst case Windows+WSL → kiosk browser, no native shell. Auto-updater mirrors signed releases to public `mca/public_releases` (source repo is private — field appliances have no Gitea creds).