# 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.