| name | train-pose |
| description | Train/evaluate WiFi pose models honestly — camera-supervised (MediaPipe + CSI) and camera-free (WiFlow), always checked against the mean-pose baseline before any PCK is quoted. |
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).
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_check the writeup — it flags any untagged or 100%/perfect claim.- If it's a benchmark vs SOTA, tag MEASURED-EQUIVALENT only with the reproducer.
