Train RuView Models
Trains, fine-tunes and evaluates RuView pose and embedding models, then publishes them.
Install
npx skills add ruvnet/RuView --skill ruview-model-training -g -a claude-code -y- Repository
- ruvnet/RuView102k
- Category
- Data and ML
- Updated
- 3d ago
- License
- MIT
- Context cost
- Lightabout 1,209 tokens
- Runs scripts
- No, instructions only
What it does
Train RuView models, camera-free WiFlow pose (10 sensor signals, no labels), camera-supervised pose (MediaPipe + ESP32 CSI → 92.9% PCK@20, ADR-079), RuVector contrastive embeddings (AETHER, ADR-024), domain generalization (MERIDIAN, ADR-027), local SNN environment adaptation, plus GPU training on GCloud and Hugging Face publishing. Use when building, fine-tuning, evaluating, or shipping a model.
RuView Model Training
RuView trains several kinds of model. Pick the track that matches the goal; all of them run on a laptop, with an optional GPU path.
Track A, Camera-free pose (WiFlow), no cameras, no labels
Trains 17-keypoint pose from 10 sensor signals. Fast, fully unsupervised, modest accuracy.
cd v2
# Pretrain on raw CSI (contrastive)
cargo run -p wifi-densepose-sensing-server -- --pretrain --dataset data/csi/ --pretrain-epochs 50
# Train pose head, save an RVF artifact
cargo run -p wifi-densepose-sensing-server -- --train --dataset data/mmfi/ --epochs 100 --save-rvf model.rvf
~84 s on an M4 Pro. Benchmarks: node scripts/benchmark-wiflow.js, eval: node scripts/eval-wiflow.js.
Track B, Camera-supervised pose (ADR-079) → 92.9% PCK@20
Uses a webcam + MediaPipe as ground truth, paired with ESP32 CSI. ~19 min on a laptop.
# 1. Collect paired data (camera + CSI)
python scripts/collect-ground-truth.py # MediaPipe pose landmarks
python scripts/collect-training-data.py # CSI capture, time-synced
node scripts/align-ground-truth.js # align camera ↔ CSI timestamps
# 2. Train (the camera-supervised path through the sensing-server / train crate)
cd v2
cargo run -p wifi-densepose-sensing-server -- --train --dataset data/paired/ --epochs <N> --save-rvf model.rvf
# 3. Evaluate
cd .. && node scripts/eval-wiflow.js # reports PCK@20
Requires data/pose_landmarker_lite.task (MediaPipe model). See docs/adr/ADR-079-camera-ground-truth-training.md.
Track C, RuVector contrastive embeddings (AETHER, ADR-024)
CSI subcarrier amplitude/phase → embeddings for re-ID and retrieval (171K emb/s on M4 Pro). Driven by wifi-densepose-train + wifi-densepose-ruvector (RuVector v2.0.4). Spectrogram embeddings: ADR-076.
