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/decisions/vision-service-packaging.md

@@ -92,6 +92,14 @@ The skeleton is **built and wired** (no recognizer models yet):
   that has `alpr`. Rule (2026-09-07, after three red runs): pure post-processing tests get numpy
   from the **dev group**; anything needing OpenCV uses `pytest.importorskip("cv2")`; the service
   itself imports both lazily inside functions.
+- **The same pattern, second package (2026-09-07):** `apps/trainer` (`@parking/trainer`,
+  [[bodytype-classifier-training]]) — light core (numpy, opencv-headless, onnxruntime) + a
+  `train` extra (CPU-only torch/torchvision/onnx/onnxscript from PyTorch's wheel index via
+  `tool.uv.index`); CI syncs without it, torch tests `importorskip("torch")`, the module that
+  imports torch is imported lazily by the `train` command only. Its own image
+  (`parking-trainer`, context `apps/trainer`, uv base image, bakes `--extra train` + the
+  ImageNet backbone weights) is built by build-images.yml beside the other three; both Python
+  contexts now carry a `.dockerignore` (venv/caches/weights out). Workspace count 7→8.
 - **Light-core, heavy-optional:** core deps boot in **stub mode** (no model download) so `uv sync` +
   tests work offline; the real stack is the `alpr` extra (`uv sync --extra alpr` →
   fast-alpr + onnxruntime). `VISION_RECOGNIZER=fast_alpr` switches it on.