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

apps/vision/.env.example

@@ -29,3 +29,11 @@ VISION_MIN_CONFIDENCE=0.5
 # VISION_VEHICLE_MODEL_PATH=models/yolox_s.onnx
 # VISION_VEHICLE_INPUT_SIZE=640
 # VISION_VEHICLE_MIN_CONFIDENCE=0.4
+
+# Phase B: the body-type classifier (sedan/hatchback/suv/… on the detector's crop), trained
+# by apps/trainer on the reviewer's labels. The Docker image bakes it at
+# /app/models/bodytype.onnx (+ .json sidecar) when models/bodytype.version pins a published
+# version; locally copy a trainer output folder's two files into apps/vision/models/.
+# Path set but no file = stage off (the normal state before the first model).
+# VISION_VEHICLE_CLASSIFIER_PATH=models/bodytype.onnx
+# VISION_VEHICLE_CLASSIFIER_MIN_CONFIDENCE=0.6