/train-pose

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).

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

1. Run the mean-pose baseline on the same split. 2. Report `(model − baseline)` in pp, with the split definition (chronological / blocked-gap / grouped-bucket; no leakage). 3. `ruview_claim_check` the writeup — it flags any untagged or 100%/perfect claim. 4. If it's a benchmark vs SOTA, tag MEASURED-EQUIVALENT only with the reproducer.