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
The far end of the Car Wash review outbox (wiki/concepts/vision-review-outbox.md): a small
Fastify + SQLite service in the monorepo (shares the payload contract and the class
vocabulary via @parking/shared), delivered to art-docker-station by its own stack so
nothing booth-side lands there and nothing of it on a booth.
- POST /ingest: bearer token per booth (constant-time), X-Booth-Id must match, multipart
meta + JPEG (magic checked, 2 MB cap), meta validated against the contract, idempotent on
the item id; crop stored at crops/<booth>/<item>.jpg on the volume + one items row.
- /review + /api/*: the reviewer's screen served by the process (Basic auth, one login):
one pending crop at a time, operator's pick and camera's pick beside it, one button/key
per vocabulary class + unusable + skip; stats per booth and per hashed operator
(agree / disagree / unusable — disagree = the reviewer's class is outside the operator's
category).
- GET /export/labels.csv: reviewed usable rows for training; formula-leading cells are
neutralised (booth-supplied names). Crops stay on the volume for the trainer on the host.
- Booth payload now carries operatorCategory.classes so the comparison needs no site setup.
- Delivery: apps/collector/Dockerfile (monorepo context), docker-compose.collector.yml
(bind to the overlay IP; commented `trainer` profile seam for the GPU), a third build
step in build-images.yml, a `wash-collector` stack in komodo/resources.toml with one
secret per booth referenced from both the collector's token list and the booth's own
stack (park-2 lines templated, commented, DNS name for the URL).
- Tests: app.test.ts (ingest ok/dup/refusals, review + stats + export, config). Image
built and smoke-tested locally (health, ingest, duplicate, auth, verdict, export).
Claude-Session: https://claude.ai/code/session_01FWncR69HgGPuei1dLrW3cU
julian