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/trainer/Dockerfile

@@ -0,0 +1,43 @@
+# syntax=docker/dockerfile:1.7
+# Parking TRAINER image: the phase-B body-type classifier job. Build CONTEXT is apps/trainer
+# (self-contained Python package). A ONE-OFF JOB on the reviewer's host (art-docker-station),
+# never a booth service: it reads the wash collector's volume (collector.sqlite + crops/)
+# and writes a versioned model folder. CPU-only PyTorch — the host has no usable GPU and a
+# few thousand crops train in minutes/an hour on four Xeon cores.
+# See wiki/decisions/bodytype-classifier-training.md.
+
+FROM ghcr.io/astral-sh/uv:python3.12-bookworm-slim AS base
+WORKDIR /app
+ENV UV_LINK_MODE=copy \
+    UV_COMPILE_BYTECODE=1 \
+    PYTHONUNBUFFERED=1
+
+RUN apt-get update \
+    && apt-get install -y --no-install-recommends libgl1 libglib2.0-0 \
+    && rm -rf /var/lib/apt/lists/*
+
+COPY pyproject.toml uv.lock .python-version ./
+RUN --mount=type=cache,target=/root/.cache/uv \
+    uv sync --frozen --no-install-project --no-dev --extra train
+
+COPY trainer/ ./trainer/
+COPY README.md ./
+RUN --mount=type=cache,target=/root/.cache/uv \
+    uv sync --frozen --no-dev --extra train
+
+# Pre-warm the ImageNet backbone weights INTO the image so a run needs no network (the
+# host has one, but a job that fetches at run time is a job that fails at 2 am). Best-effort:
+# without network at build time torchvision fetches lazily on the first run.
+ENV TORCH_HOME=/app/torch-home
+RUN uv run python -c "import torchvision.models as m; m.resnet18(weights=m.ResNet18_Weights.IMAGENET1K_V1); m.mobilenet_v3_small(weights=m.MobileNet_V3_Small_Weights.IMAGENET1K_V1)" \
+    || echo "[build] backbone weights not pre-warmed (no network) — fetched on first run"
+
+RUN useradd --system --create-home --uid 999 trainer \
+    && mkdir -p /data /out && chown -R trainer:trainer /app /out
+USER trainer
+
+ENV TRAINER_DATA_DIR=/data \
+    TRAINER_OUT_DIR=/out
+VOLUME ["/out"]
+ENTRYPOINT ["uv", "run", "--no-sync", "parking-trainer"]
+CMD ["inspect"]