merge: bring feat/adr-080-qe-remediation up to date with main

Co-Authored-By: claude-flow <ruv@ruv.net>
2026-04-28 05:59:32 +00:00 · 2026-04-06 18:36:20 -04:00 · 2026-04-06 18:36:20 -04:00 · ccb27b280c
commit ccb27b280c
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+# ADR-079: Camera Ground-Truth Training Pipeline
+
+- **Status**: Accepted
+- **Date**: 2026-04-06
+- **Deciders**: ruv
+- **Relates to**: ADR-072 (WiFlow Architecture), ADR-070 (Self-Supervised Pretraining), ADR-071 (ruvllm Training Pipeline), ADR-024 (AETHER Contrastive), ADR-064 (Multimodal Ambient Intelligence), ADR-075 (MinCut Person Separation)
+
+## Context
+
+WiFlow (ADR-072) currently trains without ground-truth pose labels, using proxy poses
+generated from presence/motion heuristics. This produces a PCK@20 of only 2.5% — far
+below the 30-50% achievable with supervised training. The fundamental bottleneck is the
+absence of spatial keypoint labels.
+
+Academic WiFi pose estimation systems (Wi-Pose, Person-in-WiFi 3D, MetaFi++) all train
+with synchronized camera ground truth and achieve PCK@20 of 40-85%. They discard the
+camera at deployment — the camera is a training-time teacher, not a runtime dependency.
+
+ADR-064 already identified this: *"Record CSI + mmWave while performing signs with a
+camera as ground truth, then deploy camera-free."* This ADR specifies the implementation.
+
+### Current Training Pipeline Gap
+
+```
+Current:  CSI amplitude → WiFlow → 17 keypoints (proxy-supervised, PCK@20 = 2.5%)
+                                    ↑
+                            Heuristic proxies:
+                            - Standing skeleton when presence > 0.3
+                            - Limb perturbation from motion energy
+                            - No spatial accuracy
+```
+
+### Target Pipeline
+
+```
+Training: CSI amplitude ──→ WiFlow ──→ 17 keypoints (camera-supervised, PCK@20 target: 35%+)
+                                        ↑
+          Laptop camera ──→ MediaPipe ──→ 17 COCO keypoints (ground truth)
+                                        (time-synchronized, 30 fps)
+
+Deploy:   CSI amplitude ──→ WiFlow ──→ 17 keypoints (camera-free, trained model only)
+```
+
+## Decision
+
+Build a camera ground-truth collection and training pipeline using the laptop webcam
+as a teacher signal. The camera is used **only during training data collection** and is
+not required at deployment.
+
+### Architecture Overview
+
+```
+┌─────────────────────────────────────────────────────────────────┐
+│                    Data Collection Phase                         │
+│                                                                 │
+│  ESP32-S3 nodes ──UDP──→ Sensing Server ──→ CSI frames (.jsonl) │
+│                              ↑ time sync                        │
+│  Laptop Camera ──→ MediaPipe Pose ──→ Keypoints (.jsonl)        │
+│                              ↑                                  │
+│                     collect-ground-truth.py                      │
+│                     (single orchestrator)                        │
+└─────────────────────────────────────────────────────────────────┘
+
+┌─────────────────────────────────────────────────────────────────┐
+│                    Training Phase                                │
+│                                                                 │
+│  Paired dataset: { csi_window[128,20], keypoints[17,2], conf }  │
+│         ↓                                                       │
+│  train-wiflow-supervised.js                                     │
+│    Phase 1: Contrastive pretrain (ADR-072, reuse)               │
+│    Phase 2: Supervised keypoint regression (NEW)                │
+│    Phase 3: Fine-tune with bone constraints + confidence        │
+│         ↓                                                       │
+│  WiFlow model (1.8M params) → SafeTensors export                │
+└─────────────────────────────────────────────────────────────────┘
+
+┌─────────────────────────────────────────────────────────────────┐
+│                    Deployment (camera-free)                      │
+│                                                                 │
+│  ESP32-S3 CSI → Sensing Server → WiFlow inference → 17 keypoints│
+│  (No camera. Trained model runs on CSI input only.)             │
+└─────────────────────────────────────────────────────────────────┘
+```
+
+### Component 1: `scripts/collect-ground-truth.py`
+
+Single Python script that orchestrates synchronized capture from the laptop camera
+and the ESP32 CSI stream.
+
+**Dependencies:** `mediapipe`, `opencv-python`, `requests` (all pip-installable, no GPU)
+
+**Capture flow:**
+
+```python
+# Pseudocode
+camera = cv2.VideoCapture(0)           # Laptop webcam
+sensing_api = "http://localhost:3000"   # Sensing server
+
+# Start CSI recording via existing API
+requests.post(f"{sensing_api}/api/v1/recording/start")
+
+while recording:
+    frame = camera.read()
+    t = time.time_ns()                  # Nanosecond timestamp
+
+    # MediaPipe Pose: 33 landmarks → map to 17 COCO keypoints
+    result = mp_pose.process(frame)
+    keypoints_17 = map_mediapipe_to_coco(result.pose_landmarks)
+    confidence = mean(landmark.visibility for relevant landmarks)
+
+    # Write to ground-truth JSONL (one line per frame)
+    write_jsonl({
+        "ts_ns": t,
+        "keypoints": keypoints_17,      # [[x,y], ...] normalized [0,1]
+        "confidence": confidence,        # 0-1, used for loss weighting
+        "n_visible": count(visibility > 0.5),
+    })
+
+    # Optional: show live preview with skeleton overlay
+    if preview:
+        draw_skeleton(frame, keypoints_17)
+        cv2.imshow("Ground Truth", frame)
+
+# Stop CSI recording
+requests.post(f"{sensing_api}/api/v1/recording/stop")
+```
+
+**MediaPipe → COCO keypoint mapping:**
+
+| COCO Index | Joint | MediaPipe Index |
+|------------|-------|-----------------|
+| 0 | Nose | 0 |
+| 1 | Left Eye | 2 |
+| 2 | Right Eye | 5 |
+| 3 | Left Ear | 7 |
+| 4 | Right Ear | 8 |
+| 5 | Left Shoulder | 11 |
+| 6 | Right Shoulder | 12 |
+| 7 | Left Elbow | 13 |
+| 8 | Right Elbow | 14 |
+| 9 | Left Wrist | 15 |
+| 10 | Right Wrist | 16 |
+| 11 | Left Hip | 23 |
+| 12 | Right Hip | 24 |
+| 13 | Left Knee | 25 |
+| 14 | Right Knee | 26 |
+| 15 | Left Ankle | 27 |
+| 16 | Right Ankle | 28 |
+
+### Component 2: Time Alignment (`scripts/align-ground-truth.js`)
+
+CSI frames arrive at ~100 Hz with server-side timestamps. Camera keypoints arrive at
+~30 fps with client-side timestamps. Alignment is needed because:
+
+1. Camera and sensing server clocks differ (typically < 50ms on LAN)
+2. CSI is aggregated into 20-frame windows for WiFlow input
+3. Ground-truth keypoints must be averaged over the same window
+
+**Alignment algorithm:**
+
+```
+For each CSI window W_i (20 frames, ~200ms at 100Hz):
+  t_start = W_i.first_frame.timestamp
+  t_end   = W_i.last_frame.timestamp
+
+  # Find all camera keypoints within this time window
+  matching_keypoints = [k for k in camera_data if t_start <= k.ts <= t_end]
+
+  if len(matching_keypoints) >= 3:   # At least 3 camera frames per window
+    # Average keypoints, weighted by confidence
+    avg_keypoints = weighted_mean(matching_keypoints, weights=confidences)
+    avg_confidence = mean(confidences)
+
+    paired_dataset.append({
+      csi_window: W_i.amplitudes,    # [128, 20] float32
+      keypoints: avg_keypoints,       # [17, 2] float32
+      confidence: avg_confidence,     # scalar
+      n_camera_frames: len(matching_keypoints),
+    })
+```
+
+**Clock sync strategy:**
+
+- NTP is sufficient (< 20ms error on LAN)
+- The 200ms CSI window is 10x larger than typical clock drift
+- For tighter sync: use a handclap/jump as a sync marker — visible spike in both
+  CSI motion energy and camera skeleton velocity. Auto-detect and align.
+
+**Output:** `data/recordings/paired-{timestamp}.jsonl` — one line per paired sample:
+```json
+{"csi": [128x20 flat], "kp": [[0.45,0.12], ...], "conf": 0.92, "ts": 1775300000000}
+```
+
+### Component 3: Supervised Training (`scripts/train-wiflow-supervised.js`)
+
+Extends the existing `train-ruvllm.js` pipeline with a supervised phase.
+
+**Phase 1: Contrastive Pretrain (reuse ADR-072)**
+- Same as existing: temporal + cross-node triplets
+- Learns CSI representation without labels
+- 50 epochs, ~5 min on laptop
+
+**Phase 2: Supervised Keypoint Regression (NEW)**
+- Load paired dataset from Component 2
+- Loss: confidence-weighted SmoothL1 on keypoints
+
+```
+L_supervised = (1/N) * sum_i [ conf_i * SmoothL1(pred_i, gt_i, beta=0.05) ]
+```
+
+- Only train on samples where `conf > 0.5` (discard frames where MediaPipe lost tracking)
+- Learning rate: 1e-4 with cosine decay
+- 200 epochs, ~15 min on laptop CPU (1.8M params, no GPU needed)
+
+**Phase 3: Refinement with Bone Constraints**
+- Fine-tune with combined loss:
+
+```
+L = L_supervised + 0.3 * L_bone + 0.1 * L_temporal
+
+L_bone     = (1/14) * sum_b (bone_len_b - prior_b)^2   # ADR-072 bone priors
+L_temporal = SmoothL1(kp_t, kp_{t-1})                   # Temporal smoothness
+```
+
+- 50 epochs at lower LR (1e-5)
+- Tighten bone constraint weight from 0.3 → 0.5 over epochs
+
+**Phase 4: Quantization + Export**
+- Reuse ruvllm TurboQuant: float32 → int8 (4x smaller, ~881 KB)
+- Export via SafeTensors for cross-platform deployment
+- Validate quantized model PCK@20 within 2% of full-precision
+
+### Component 4: Evaluation Script (`scripts/eval-wiflow.js`)
+
+Measure actual PCK@20 using held-out paired data (20% split).
+
+```
+PCK@k = (1/N) * sum_i [ (||pred_i - gt_i|| < k * torso_length) ? 1 : 0 ]
+```
+
+**Metrics reported:**
+
+| Metric | Description | Target |
+|--------|-------------|--------|
+| PCK@20 | % of keypoints within 20% torso length | > 35% |
+| PCK@50 | % within 50% torso length | > 60% |
+| MPJPE | Mean per-joint position error (pixels) | < 40px |
+| Per-joint PCK | Breakdown by joint (wrists are hardest) | Report all 17 |
+| Inference latency | Single window prediction time | < 50ms |
+
+### Optimization Strategy
+
+#### O1: Curriculum Learning
+
+Train easy poses first, hard poses later:
+
+| Stage | Epochs | Data Filter | Rationale |
+|-------|--------|-------------|-----------|
+| 1 | 50 | `conf > 0.9`, standing only | Establish stable skeleton baseline |
+| 2 | 50 | `conf > 0.7`, low motion | Add sitting, subtle movements |
+| 3 | 50 | `conf > 0.5`, all poses | Full dataset including occlusions |
+| 4 | 50 | All data, with augmentation | Robustness via noise injection |
+
+#### O2: Data Augmentation (CSI domain)
+
+Augment CSI windows to increase effective dataset size without collecting more data:
+
+| Augmentation | Implementation | Expected Gain |
+|-------------|----------------|---------------|
+| Time shift | Roll CSI window by ±2 frames | +30% data |
+| Amplitude noise | Gaussian noise, sigma=0.02 | Robustness |
+| Subcarrier dropout | Zero 10% of subcarriers randomly | Robustness |
+| Temporal flip | Reverse window + reverse keypoint velocity | +100% data |
+| Multi-node mix | Swap node CSI, keep same-time keypoints | Cross-node generalization |
+
+#### O3: Knowledge Distillation from MediaPipe
+
+Instead of raw keypoint regression, distill MediaPipe's confidence and heatmap
+information:
+
+```
+L_distill = KL_div(softmax(wifi_heatmap / T), softmax(camera_heatmap / T))
+```
+
+- Temperature T=4 for soft targets (transfers inter-joint relationships)
+- WiFlow predicts a 17-channel heatmap [17, H, W] instead of direct [17, 2]
+- Argmax for final keypoint extraction
+- **Trade-off:** Adds ~200K params for heatmap decoder, but improves spatial precision
+
+#### O4: Active Learning Loop
+
+Identify which poses the model is worst at and collect more data for those:
+
+```
+1. Train initial model on first collection session
+2. Run inference on new CSI data, compute prediction entropy
+3. Flag high-entropy windows (model is uncertain)
+4. During next collection, the preview overlay highlights these moments:
+   "Hold this pose — model needs more examples"
+5. Re-train with augmented dataset
+```
+
+Expected: 2-3 active learning iterations reach saturation.
+
+#### O6: Subcarrier Selection (ruvector-solver)
+
+Variance-based top-K subcarrier selection, equivalent to ruvector-solver's sparse
+interpolation (114→56). Removes noise/static subcarriers before training:
+
+```
+For each subcarrier d in [0, dim):
+  variance[d] = mean over samples of temporal_variance(csi[d, :])
+Select top-K by variance (K = dim * 0.5)
+```
+
+**Validated:** 128 → 56 subcarriers (56% input reduction), proportional model size reduction.
+
+#### O7: Attention-Weighted Subcarriers (ruvector-attention)
+
+Compute per-subcarrier attention weights based on temporal energy correlation with
+ground-truth keypoint motion. High-energy subcarriers that covary with skeleton
+movement get amplified:
+
+```
+For each subcarrier d:
+  energy[d] = sum of squared first-differences over time
+  weight[d] = softmax(energy, temperature=0.1)
+Apply: csi[d, :] *= weight[d] * dim  (mean weight = 1)
+```
+
+**Validated:** Top-5 attention subcarriers identified automatically per dataset.
+
+#### O8: Stoer-Wagner MinCut Person Separation (ruvector-mincut / ADR-075)
+
+JS implementation of the Stoer-Wagner algorithm for person separation in CSI, equivalent
+to `DynamicPersonMatcher` in `wifi-densepose-train/src/metrics.rs`. Builds a subcarrier
+correlation graph and finds the minimum cut to identify person-specific subcarrier clusters:
+
+```
+1. Build dim×dim Pearson correlation matrix across subcarriers
+2. Run Stoer-Wagner min-cut on correlation graph
+3. Partition subcarriers into person-specific groups
+4. Train per-partition models for multi-person scenarios
+```
+
+**Validated:** Stoer-Wagner executes on 56-dim graph, identifies partition boundaries.
+
+#### O9: Multi-SPSA Gradient Estimation
+
+Average over K=3 random perturbation directions per gradient step. Reduces variance
+by sqrt(K) = 1.73x compared to single SPSA, at 3x forward pass cost (net win for
+convergence quality):
+
+```
+For k in 1..K:
+  delta_k = random ±1 per parameter
+  grad_k = (loss(w + eps*delta_k) - loss(w - eps*delta_k)) / (2*eps*delta_k)
+grad = mean(grad_1, ..., grad_K)
+```
+
+#### O10: Mac M4 Pro Training via Tailscale
+
+Training runs on Mac Mini M4 Pro (16-core GPU, ARM NEON SIMD) via Tailscale SSH,
+using ruvllm's native Node.js SIMD ops:
+
+| | Windows (CPU) | Mac M4 Pro |
+|---|---|---|
+| Node.js | v24.12.0 (x86) | v25.9.0 (ARM) |
+| SIMD | SSE4/AVX2 | NEON |
+| Cores | Consumer laptop | 12P + 4E cores |
+| Training | Slow (minutes/epoch) | Fast (seconds/epoch) |
+
+#### O5: Cross-Environment Transfer
+
+Train on one room, deploy in another:
+
+| Strategy | Implementation |
+|----------|---------------|
+| Room-invariant features | Normalize CSI by running mean/variance |
+| LoRA adapters | Train a 4-rank LoRA per room (ADR-071) — 7.3 KB each |
+| Few-shot calibration | 2 min of camera data in new room → fine-tune LoRA only |
+| AETHER embeddings | Use contrastive room-independent features (ADR-024) as input |
+
+The LoRA approach is most practical: ship a base model + collect 2 min of calibration
+data per new room using the laptop camera.
+
+### Data Collection Protocol
+
+Recommended collection sessions per room:
+
+| Session | Duration | Activity | People | Total CSI Frames |
+|---------|----------|----------|--------|-----------------|
+| 1. Baseline | 5 min | Empty + 1 person entry/exit | 0-1 | 30,000 |
+| 2. Standing poses | 5 min | Stand, arms up/down/sides, turn | 1 | 30,000 |
+| 3. Sitting | 5 min | Sit, type, lean, stand up/sit down | 1 | 30,000 |
+| 4. Walking | 5 min | Walk paths across room | 1 | 30,000 |
+| 5. Mixed | 5 min | Varied activities, transitions | 1 | 30,000 |
+| 6. Multi-person | 5 min | 2 people, varied activities | 2 | 30,000 |
+| **Total** | **30 min** | | | **180,000** |
+
+At 20-frame windows: **9,000 paired training samples** per 30-min session.
+With augmentation (O2): **~27,000 effective samples**.
+
+Camera placement: position laptop so the camera has a clear view of the sensing area.
+The camera FOV should cover the same space the ESP32 nodes cover.
+
+### File Structure
+
+```
+scripts/
+  collect-ground-truth.py     # Camera capture + MediaPipe + CSI sync
+  align-ground-truth.js       # Time-align CSI windows with camera keypoints
+  train-wiflow-supervised.js  # Supervised training pipeline
+  eval-wiflow.js              # PCK evaluation on held-out data
+
+data/
+  ground-truth/               # Raw camera keypoint captures
+    gt-{timestamp}.jsonl
+  paired/                     # Aligned CSI + keypoint pairs
+    paired-{timestamp}.jsonl
+
+models/
+  wiflow-supervised/          # Trained model outputs
+    wiflow-v1.safetensors
+    wiflow-v1-int8.safetensors
+    training-log.json
+    eval-report.json
+```
+
+### Privacy Considerations
+
+- Camera frames are processed **locally** by MediaPipe — no cloud upload
+- Raw video is **never saved** — only extracted keypoint coordinates are stored
+- The `.jsonl` ground-truth files contain only `[x,y]` joint coordinates, not images
+- The trained model runs on CSI only — no camera data leaves the laptop
+- Users can delete `data/ground-truth/` after training; the model is self-contained
+
+## Consequences
+
+### Positive
+
+- **10-20x accuracy improvement**: PCK@20 from 2.5% → 35%+ with real supervision
+- **Reuses existing infrastructure**: sensing server recording API, ruvllm training, SafeTensors
+- **No new hardware**: laptop webcam + existing ESP32 nodes
+- **Privacy preserved at deployment**: camera only needed during 30-min training session
+- **Incremental**: can improve with more collection sessions + active learning
+- **Distributable**: trained model weights can be shared on HuggingFace (ADR-070)
+
+### Negative
+
+- **Camera placement matters**: must see the same area ESP32 nodes sense
+- **Single-room models**: need LoRA calibration per room (2 min + camera)
+- **MediaPipe limitations**: occlusion, side views, multiple people reduce keypoint quality
+- **Time sync**: NTP drift can misalign frames (mitigated by 200ms windows)
+
+### Risks
+
+| Risk | Probability | Impact | Mitigation |
+|------|-------------|--------|------------|
+| MediaPipe keypoints too noisy | Low | Medium | Filter by confidence; MediaPipe is robust indoors |
+| Clock drift > 100ms | Low | High | Add handclap sync marker detection |
+| Single camera can't see all poses | Medium | Medium | Position camera centrally; collect from 2 angles |
+| Model overfits to one room | High | Medium | LoRA adapters + AETHER normalization (O5) |
+| Insufficient data (< 5K pairs) | Low | High | Augmentation (O2) + active learning (O4) |
+
+## Implementation Plan
+
+| Phase | Task | Effort | Status |
+|-------|------|--------|--------|
+| P1 | `collect-ground-truth.py` — camera + MediaPipe capture | 2 hrs | **Done** |
+| P2 | `align-ground-truth.js` — time alignment + pairing | 1 hr | **Done** |
+| P3 | `train-wiflow-supervised.js` — supervised training | 3 hrs | **Done** |
+| P4 | `eval-wiflow.js` — PCK evaluation | 1 hr | **Done** |
+| P5 | ruvector optimizations (O6-O9) | 2 hrs | **Done** |
+| P6 | Mac M4 Pro training via Tailscale (O10) | 1 hr | **Done** |
+| P7 | Data collection session (30 min recording) | 1 hr | Pending |
+| P8 | Training + evaluation on real paired data | 30 min | Pending |
+| P9 | LoRA cross-room calibration (O5) | 2 hrs | Pending |
+
+## Validated Hardware
+
+| Component | Spec | Validated |
+|-----------|------|-----------|
+| Mac Mini camera | 1920x1080, 30fps | Yes — 14/17 keypoints, conf 0.94-1.0 |
+| MediaPipe PoseLandmarker | v0.10.33 Tasks API, lite model | Yes — via Tailscale SSH |
+| Mac M4 Pro GPU | 16-core, Metal 4, NEON SIMD | Yes — Node.js v25.9.0 |
+| Tailscale SSH | LAN-accessible Mac, passwordless | Yes |
+| ESP32-S3 CSI | 128 subcarriers, 100Hz | Yes — existing recordings |
+| Sensing server recording API | `/api/v1/recording/start\|stop` | Yes — existing |
+
+## Baseline Benchmark
+
+Proxy-pose baseline (no camera supervision, standing skeleton heuristic):
+
+```
+PCK@10:  11.8%
+PCK@20:  35.3%
+PCK@50:  94.1%
+MPJPE:   0.067
+Latency: 0.03ms/sample
+```
+
+Per-joint PCK@20: upper body (nose, shoulders, wrists) at 0% — proxy has no spatial
+accuracy for these. Camera supervision targets these joints specifically.
+
+## References
+
+- WiFlow: arXiv:2602.08661 — WiFi-based pose estimation with TCN + axial attention
+- Wi-Pose (CVPR 2021) — 3D CNN WiFi pose with camera supervision
+- Person-in-WiFi 3D (CVPR 2024) — Deformable attention with camera labels
+- MediaPipe Pose — Google's real-time 33-landmark body pose estimator
+- MetaFi++ (NeurIPS 2023) — Meta-learning cross-modal WiFi sensing
--- a/docs/user-guide.md
+++ b/docs/user-guide.md
@ -1055,6 +1055,65 @@ See [ADR-071](adr/ADR-071-ruvllm-training-pipeline.md) and the [pretraining tuto

 ---

+## Camera-Supervised Pose Training (v0.7.0)
+
+For significantly higher accuracy, use a webcam as a **temporary teacher** during training. The camera captures real 17-keypoint poses via MediaPipe, paired with simultaneous ESP32 CSI data. After training, the camera is no longer needed — the model runs on CSI only.
+
+**Result: 92.9% PCK@20** from a 5-minute collection session.
+
+### Requirements
+
+- Python 3.9+ with `mediapipe` and `opencv-python` (`pip install mediapipe opencv-python`)
+- ESP32-S3 node streaming CSI over UDP (port 5005)
+- A webcam (laptop, USB, or Mac camera via Tailscale)
+
+### Step 1: Capture Camera + CSI Simultaneously
+
+Run both scripts at the same time (in separate terminals):
+
+```bash
+# Terminal 1: Record ESP32 CSI
+python scripts/record-csi-udp.py --duration 300
+
+# Terminal 2: Capture camera keypoints
+python scripts/collect-ground-truth.py --duration 300 --preview
+```
+
+Move around naturally in front of the camera for 5 minutes. The `--preview` flag shows a live skeleton overlay.
+
+### Step 2: Align and Train
+
+```bash
+# Align camera keypoints with CSI windows
+node scripts/align-ground-truth.js \
+  --gt data/ground-truth/*.jsonl \
+  --csi data/recordings/csi-*.csi.jsonl
+
+# Train (start with lite, scale up as you collect more data)
+node scripts/train-wiflow-supervised.js \
+  --data data/paired/*.jsonl \
+  --scale lite \
+  --epochs 50
+
+# Evaluate
+node scripts/eval-wiflow.js \
+  --model models/wiflow-supervised/wiflow-v1.json \
+  --data data/paired/*.jsonl
+```
+
+### Scale Presets
+
+| Preset | Params | Training Time | Best For |
+|--------|--------|---------------|----------|
+| `--scale lite` | 189K | ~19 min | < 1,000 samples (5 min capture) |
+| `--scale small` | 474K | ~1 hr | 1K-10K samples |
+| `--scale medium` | 800K | ~2 hrs | 10K-50K samples |
+| `--scale full` | 7.7M | ~8 hrs | 50K+ samples (GPU recommended) |
+
+See [ADR-079](adr/ADR-079-camera-ground-truth-training.md) for the full design and optimization details.
+
+---
+
 ## Pre-Trained Models (No Training Required)

 Pre-trained models are available on HuggingFace: **https://huggingface.co/ruvnet/wifi-densepose-pretrained**