train-pose
Build a CSI→pose model without overstating it. The project has a retracted 92.9%/100% history — the discipline below exists so it never recurs.
The non-negotiable: mean-pose baseline first
A pose model that always predicts the dataset's mean pose already scores ~50% PCK. Quote PCK only as a delta over that baseline, on a held-out split with no subject or temporal leakage. Example honest result (ADR-181):
Held-out PCK@20 59.5% vs a 50% mean-pose baseline = +9.4 pp real signal — MEASURED.
Paths
- camera-supervised (ADR-079) — MediaPipe Pose labels the camera frame; paired CSI trains the net. Train/infer in one camera frame so the skeleton aligns.
- camera-free (WiFlow, ADR-152) — no camera at inference; geometry-conditioned.
- in-browser (ADR-181) — WebGPU/WASM trainer; the active backend is shown as a badge (honest about what's executing).
Run it through the harness (ADR-371)
npx @ruvnet/ruview train-plan --mode pose-smoke # command, cwd, outputs; runs nothing npx @ruvnet/ruview train --mode pose-smoke --confirm # SYNTHETIC pipeline smoke (libtorch 2.11 for tch 0.24) npx @ruvnet/ruview train --mode pose --data-dir <in-repo MM-Fi dir> --confirm npx @ruvnet/ruview train-gate --file eval-report.json # mean-pose baseline + leakage gate
The gate returns the only acceptable claim sentence. Quote nothing it fails.
Before you publish a number
- Run the mean-pose baseline on the same split.
- Report
(model − baseline)in pp, with the split definition (chronological / blocked-gap / grouped-bucket; no leakage). ruview_claim_checkthe writeup — it flags any untagged or 100%/perfect claim.- If it's a benchmark vs SOTA, tag MEASURED-EQUIVALENT only with the reproducer.
