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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
parking-trainer
The phase-B body-type classifier job. Reads the wash collector's volume
(collector.sqlite + crops/), trains a classifier on the reviewer's labels, and writes a
versioned model folder the vision image bakes in — or refuses when validation is below the
floor. Design and decisions: wiki/decisions/bodytype-classifier-training.md.
parking-trainer inspect --data /data # what a run would train on
parking-trainer train --data /data --out /out # features mode (minutes)
parking-trainer train --mode finetune --epochs 12 ... # full fine-tune (about an hour on 4 cores)
parking-trainer evaluate --model /out/<version>/bodytype.onnx --data /data
parking-trainer publish /out/<version> --url https://git.infra.msai.al/api/packages/mca/generic/parking-bodytype
Exit codes: 0 model written · 2 not enough labels · 3 below the floor (report written,
no model) · 1 other.
A passing run writes <out>/<version>/:
| file | what |
|---|---|
bodytype.onnx |
the classifier; input image = RGB float32 0–255 [N,3,S,S], output logits [N,K]; normalisation is inside the graph |
bodytype.json |
sidecar: version, class list (in vocabulary order), input size, crop margin, backbone, mode, label counts, validation metrics |
report.md |
the human report: accuracy, per-class recall/precision, confusion matrix, dropped classes, loss weights |
metrics.json |
the same numbers, machine-readable |
On the reviewer's host (the wash-collector stack):
docker compose -f docker-compose.collector.yml --profile train run --rm trainer inspect
docker compose -f docker-compose.collector.yml --profile train run --rm trainer train --min-accuracy 0.85
Local dev: uv sync --extra train (CPU torch, ~200 MB), uv run pytest -q. The test suite
runs without the extra (torch tests skip), matching CI.