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2026-07-18 18:01:23 -04:00
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docs/user-guide.md
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@@ -1861,6 +1861,23 @@ node scripts/eval-wiflow.js \
--data data/paired/*.jsonl
```
> **Model format boundary:** `train-wiflow-supervised.js` produces the
> JavaScript WiFlow model `wiflow-v1.json`. There is currently no supported
> command that converts that JSON model into the sensing server's binary RVF
> container, and renaming the file to `.rvf` does not convert it. Use the JSON
> model with the JavaScript evaluation/inference tools. To train a model that
> the Rust sensing server can load, use its native training path, which writes
> RVF directly:
>
> ```bash
> cargo run -p wifi-densepose-sensing-server --release -- \
> --train --dataset data/mmfi --dataset-type mmfi \
> --epochs 100 --save-rvf models/room-model.rvf
> ```
>
> The camera+CSI paired JSONL workflow and the native RVF trainer are separate
> pipelines today. A JSON-to-RVF exporter is future work.
**Evaluation protocol matters.** Use `eval-wiflow.js` (torso-normalized
PCK@20, the metric comparable to published WiFi-pose results) on a temporal
hold-out, and sanity-check that predictions actually vary across frames