ruview-model-training

name ruview-model-training description 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. allowed-tools Bash Read Write Edit Glob Grep 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. cd v2 cargo check -p wifi-densepose-train --no-default-features # sanity cargo run -p wifi-densepose-sensing-server -- --model model.rvf --embed cargo run -p wifi-densepose-sensing-server -- --model model.rvf --build-index env Track D — Domain generalization (MERIDIAN, ADR-027) Make a model transfer across environments without retraining. Configured through the training pipeline's domain-generalization options; see ADR-027 and wifi-densepose-train + ruview_metrics . Track E — Local SNN environment adaptation Spiking neural network that adapts to a new room in <30 s, on-device or on a Cognitum Seed: node scripts/snn-csi-processor.js --port 5006 See docs/tutorials/cognitum-seed-pretraining.md , ADR-084/085 (RaBitQ similarity sensor), ADR-086 (edge novelty gate). GPU training on GCloud Project cognitum-20260110 has L4 / A100 / H100 quota. gcloud auth login gcloud config set project cognitum-20260110 bash scripts/gcloud-train.sh --dry-run # smoke test, synthetic data bash scripts/gcloud-train.sh --gpu l4 --hours 2 # prototyping bash scripts/gcloud-train.sh --gpu a100 --config scripts/training-config-sweep.json bash scripts/gcloud-train.sh --sweep # full hyperparameter sweep # VM is auto-deleted after training unless --keep-vm. Cost: L4 ~$0.80/hr, A100 40GB ~$3.60/hr. Local Mac training: bash scripts/mac-mini-train.sh . Model benchmark: python scripts/benchmark-model.py . Publishing a trained model python scripts/publish-huggingface.py # or: bash scripts/publish-huggingface.sh Pushes the RVF artifact + card to Hugging Face. See docs/huggingface/ . Data layout Path Contents data/recordings/ Raw CSI captures ( *.csi.jsonl ), overnight runs data/csi/ CSI datasets for pretraining data/mmfi/ MM-Fi dataset (ADR-015) data/paired/ Camera ↔ CSI paired samples (ADR-079) data/ground-truth/ MediaPipe pose landmarks data/pose_landmarker_lite.task MediaPipe model file models/ Trained artifacts Record more data: python scripts/record-csi-udp.py (UDP CSI capture from a live node). Validation after a training change cd v2 && cargo test --workspace --no-default-features # 1,400+ pass, 0 fail cd .. && python archive/v1/data/proof/verify.py # VERDICT: PASS Then hand off to ruview-verify for the witness bundle. Reference ADRs: 015 (MM-Fi + Wi-Pose datasets), 016 (RuVector training integration — complete), 017 (RuVector signal + MAT), 024 (AETHER), 027 (MERIDIAN), 076 (spectrogram embeddings), 079 (camera ground truth), 084/085 (RaBitQ), 095/096 (on-ESP32 temporal modeling, sparse GQA) Crates: wifi-densepose-train , wifi-densepose-nn , wifi-densepose-ruvector , wifi-densepose-sensing-server scripts/gcloud-train.sh , mac-mini-train.sh , benchmark-wiflow.js , eval-wiflow.js , benchmark-model.py