34 lines
1.5 KiB
Markdown
34 lines
1.5 KiB
Markdown
---
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name: train-pose
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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.
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---
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# train-pose
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Build a CSI→pose model without overstating it. The project has a **retracted 92.9%/100%**
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history — the discipline below exists so it never recurs.
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## The non-negotiable: mean-pose baseline first
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A pose model that always predicts the dataset's *mean pose* already scores ~50% PCK.
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**Quote PCK only as a delta over that baseline**, on a held-out split with no subject
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or temporal leakage. Example honest result (ADR-181):
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> Held-out PCK@20 **59.5%** vs a 50% mean-pose baseline = **+9.4 pp real signal** — MEASURED.
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## Paths
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- **camera-supervised** (ADR-079) — MediaPipe Pose labels the camera frame; paired CSI
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trains the net. Train/infer in one camera frame so the skeleton aligns.
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- **camera-free** (WiFlow, ADR-152) — no camera at inference; geometry-conditioned.
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- **in-browser** (ADR-181) — WebGPU/WASM trainer; the active backend is shown as a badge
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(honest about what's executing).
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## Before you publish a number
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1. Run the mean-pose baseline on the same split.
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2. Report `(model − baseline)` in pp, with the split definition (chronological /
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blocked-gap / grouped-bucket; no leakage).
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3. `ruview_claim_check` the writeup — it flags any untagged or 100%/perfect claim.
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4. If it's a benchmark vs SOTA, tag MEASURED-EQUIVALENT only with the reproducer.
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