ruvnet/RuView v0.7.0

v0.7.0 — WiFlow Camera-Supervised Pose Model (92.9% PCK@20)

on GitHub

WiFlow v1 — Camera-Supervised WiFi Pose Estimation

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

Headline Result

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

# 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