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v0.7.0 -- WiFlow Camera-Supervised Pose Model (92.9_ PCK@20) source code.tar.gz 2026-04-06 42.1 MB
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WiFlow v1 — Camera-Supervised WiFi Pose Estimation

First WiFlow model trained on real ESP32 CSI + real camera ground truth.

Headline Result

Metric Value
PCK@20 92.9%
Eval loss 0.082
Bone constraint 0.008
Parameters 186,946
Model size 974 KB
Training time 19 minutes

What's New

Camera Ground-Truth Pipeline (ADR-079)

  • collect-ground-truth.py — MediaPipe webcam capture synced with CSI
  • align-ground-truth.js — Nanosecond time alignment
  • train-wiflow-supervised.js — 3-phase curriculum training
  • eval-wiflow.js — PCK/MPJPE evaluation
  • record-csi-udp.py — Lightweight ESP32 CSI recorder

ruvector Optimizations

  • O6: Subcarrier selection (70→35, 50% reduction)
  • O7: Attention-weighted subcarriers
  • O8: Stoer-Wagner min-cut person separation
  • O9: Multi-SPSA gradient estimation
  • O10: Scalable model (lite/small/medium/full)

Data Collection

  • ESP32-S3 CSI: 7,000 frames at 23fps (5 min)
  • Mac Mini M4 Pro camera: 6,470 frames via MediaPipe (5 min)
  • 345 time-aligned paired samples

Model Archive Contents

  • wiflow-v1.json — Trained model weights (974 KB)
  • training-log.json — Loss curves per phase
  • baseline-report.json — Pre-training baseline metrics
  • MODEL_CARD.md — Model documentation

How to Use

:::bash
# Collect your own ground truth
python scripts/collect-ground-truth.py --duration 300 --preview
python scripts/record-csi-udp.py --duration 300

# Train
node scripts/train-wiflow-supervised.js --data data/paired/your-data.jsonl --scale lite

# Evaluate
node scripts/eval-wiflow.js --model models/wiflow-real/wiflow-v1.json --data data/paired/your-data.jsonl

Related

  • PR [#363]: Camera ground-truth training pipeline
  • ADR-079: Camera Ground-Truth Training Pipeline
  • ADR-072: WiFlow Architecture
Source: README.md, updated 2026-04-06