wifi-densepose

Commit Graph

Author	SHA1	Message	Date
ruv	327d0d13f6	feat: scalable WiFlow model with 4 size presets (#362 ) Add --scale flag with 4 presets for dataset-appropriate sizing: lite: ~190K params, 2 TCN blocks k=3 (trains in seconds) small: ~200K params, 4 TCN blocks k=5 (trains in minutes) medium: ~800K params, 4 TCN blocks k=7 (trains in ~15 min) full: ~7.7M params, 4 TCN blocks k=7 (trains in hours) Refactored model to use dynamic TCN block count, kernel size, channel widths, hidden dim, and SPSA perturbation count — all driven by the scale preset. Default is 'lite' for fast iteration. Validated: lite model completes 30 epochs on 265 samples in ~2 min on Windows CPU (vs stuck at epoch 1 with full model). Scale up with: --scale small\|medium\|full as dataset grows. Co-Authored-By: claude-flow <ruv@ruv.net>	2026-04-06 14:55:35 -04:00
ruv	33f5abd0e0	feat: ruvector + DynamicMinCut optimizations for WiFlow training (#362 ) Add 4 ruvector-inspired optimizations to the training pipeline: - O6: Subcarrier selection (ruvector-solver) — variance-based top-K selection reduces 128→56 subcarriers (56% input reduction) - O7: Attention-weighted subcarriers (ruvector-attention) — motion- correlated weighting amplifies informative channels - O8: Stoer-Wagner min-cut person separation (ruvector-mincut) — identifies person-specific subcarrier clusters via correlation graph partitioning for multi-person training - O9: Multi-SPSA gradient estimation — K=3 perturbations per step reduces gradient variance by sqrt(3) vs single SPSA Also fixes data loader to accept both `kp`/`keypoints` field names and flat CSI arrays with `csi_shape`, and scalar `conf` values. Co-Authored-By: claude-flow <ruv@ruv.net>	2026-04-06 14:22:08 -04:00
ruv	e3522ddcda	feat: camera ground-truth training pipeline (ADR-079, #362 ) Add 4 scripts for camera-supervised WiFlow pose training: - collect-ground-truth.py: synchronized webcam + CSI capture via MediaPipe PoseLandmarker (17 COCO keypoints at 30fps) - align-ground-truth.js: time-align camera keypoints with CSI windows using binary search, confidence-weighted averaging - train-wiflow-supervised.js: 3-phase supervised training (contrastive pretrain → supervised keypoint regression → bone-constrained refinement) with curriculum learning and CSI augmentation - eval-wiflow.js: PCK@10/20/50, MPJPE, per-joint breakdown, baseline proxy mode for benchmarking Baseline benchmark (proxy poses, no camera supervision): PCK@10: 11.8% \| PCK@20: 35.3% \| PCK@50: 94.1% \| MPJPE: 0.067 Camera pipeline validated over Tailscale to Mac Mini M4 Pro (1920x1080, 14/17 keypoints visible, MediaPipe confidence 0.94-1.0). Target after camera-supervised training: PCK@20 > 50% Closes #362 Co-Authored-By: claude-flow <ruv@ruv.net>	2026-04-06 14:07:25 -04:00

Author

SHA1

Message

Date

ruv

327d0d13f6

feat: scalable WiFlow model with 4 size presets (#362 )

Add --scale flag with 4 presets for dataset-appropriate sizing:

  lite:   ~190K params, 2 TCN blocks k=3  (trains in seconds)
  small:  ~200K params, 4 TCN blocks k=5  (trains in minutes)
  medium: ~800K params, 4 TCN blocks k=7  (trains in ~15 min)
  full:   ~7.7M params, 4 TCN blocks k=7  (trains in hours)

Refactored model to use dynamic TCN block count, kernel size,
channel widths, hidden dim, and SPSA perturbation count — all
driven by the scale preset. Default is 'lite' for fast iteration.

Validated: lite model completes 30 epochs on 265 samples in ~2 min
on Windows CPU (vs stuck at epoch 1 with full model).

Scale up with: --scale small|medium|full as dataset grows.

Co-Authored-By: claude-flow <ruv@ruv.net>

2026-04-06 14:55:35 -04:00

ruv

33f5abd0e0

feat: ruvector + DynamicMinCut optimizations for WiFlow training (#362 )

Add 4 ruvector-inspired optimizations to the training pipeline:

- O6: Subcarrier selection (ruvector-solver) — variance-based top-K
  selection reduces 128→56 subcarriers (56% input reduction)
- O7: Attention-weighted subcarriers (ruvector-attention) — motion-
  correlated weighting amplifies informative channels
- O8: Stoer-Wagner min-cut person separation (ruvector-mincut) —
  identifies person-specific subcarrier clusters via correlation
  graph partitioning for multi-person training
- O9: Multi-SPSA gradient estimation — K=3 perturbations per step
  reduces gradient variance by sqrt(3) vs single SPSA

Also fixes data loader to accept both `kp`/`keypoints` field names
and flat CSI arrays with `csi_shape`, and scalar `conf` values.

Co-Authored-By: claude-flow <ruv@ruv.net>

2026-04-06 14:22:08 -04:00

ruv

e3522ddcda

feat: camera ground-truth training pipeline (ADR-079, #362 )

Add 4 scripts for camera-supervised WiFlow pose training:

- collect-ground-truth.py: synchronized webcam + CSI capture via
  MediaPipe PoseLandmarker (17 COCO keypoints at 30fps)
- align-ground-truth.js: time-align camera keypoints with CSI windows
  using binary search, confidence-weighted averaging
- train-wiflow-supervised.js: 3-phase supervised training (contrastive
  pretrain → supervised keypoint regression → bone-constrained
  refinement) with curriculum learning and CSI augmentation
- eval-wiflow.js: PCK@10/20/50, MPJPE, per-joint breakdown, baseline
  proxy mode for benchmarking

Baseline benchmark (proxy poses, no camera supervision):
  PCK@10: 11.8% | PCK@20: 35.3% | PCK@50: 94.1% | MPJPE: 0.067

Camera pipeline validated over Tailscale to Mac Mini M4 Pro
(1920x1080, 14/17 keypoints visible, MediaPipe confidence 0.94-1.0).

Target after camera-supervised training: PCK@20 > 50%

Closes #362

Co-Authored-By: claude-flow <ruv@ruv.net>

2026-04-06 14:07:25 -04:00

3 Commits