/ruview-train

Train a RuView model — camera-free WiFlow pose, camera-supervised pose (92.9% PCK@20), RuVector embeddings, domain generalization, local SNN, with optional GPU on GCloud.

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ruvnet@ruvnet

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ruview

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Install

> /plugin marketplace add ruvnet/RuView
> /plugin install ruview@ruview

How it fires

How this command gets triggered: by you, by Claude, or both.

Fires itselfClaude auto-loads it when your prompt matches the work.
You can call itInvoke it directly when you want it.
Slash command/ruview-train

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What this command does when you run it.

Train a RuView model — camera-free WiFlow pose, camera-supervised pose (92.9% PCK@20), RuVector embeddings, domain generalization, local SNN, with optional GPU on GCloud.

Command definition

ruview-train.md

description: Train a RuView model — camera-free WiFlow pose, camera-supervised pose (92.9% PCK@20), RuVector embeddings, domain generalization, local SNN, with optional GPU on GCloud.
argument-hint: "[camera-free|camera-supervised|embeddings|domain-gen|snn|gpu] [--epochs N]"

/ruview-train

Train, fine-tune, evaluate, or publish a RuView model.

1. Invoke the **`ruview-model-training`** skill. 2. Pick the track from `$ARGUMENTS`; if empty, ask which:

**camera-free** (Track A) — `cargo run -p wifi-densepose-sensing-server -- --pretrain --dataset data/csi/ --pretrain-epochs 50` then `-- --train --dataset data/mmfi/ --epochs 100 --save-rvf model.rvf`. ~84 s on M4 Pro, modest accuracy.
**camera-supervised** (Track B, ADR-079) — `python scripts/collect-ground-truth.py`, `python scripts/collect-training-data.py`, `node scripts/align-ground-truth.js`, then train on `data/paired/`, eval with `node scripts/eval-wiflow.js`. ~19 min, 92.9% PCK@20. Needs `data/pose_landmarker_lite.task`.
**embeddings** (Track C, AETHER ADR-024) — `wifi-densepose-train` + `wifi-densepose-ruvector`; `-- --model model.rvf --embed`, `-- --build-index env`.
**domain-gen** (Track D, MERIDIAN ADR-027) / **snn** (Track E) — `node scripts/snn-csi-processor.js --port 5006`; cognitum-seed-pretraining tutorial.
**gpu** — `gcloud config set project cognitum-20260110`; `bash scripts/gcloud-train.sh --gpu l4 --hours 2` (or `--gpu a100 --sweep`, `--dry-run` to smoke-test). VM auto-deletes unless `--keep-vm`.

3. After training: `cd v2 && cargo test --workspace --no-default-features`, `python archive/v1/data/proof/verify.py`. To publish: `python scripts/publish-huggingface.py`. 4. Hand off to `/ruview-verify` for the witness bundle.

Read it on GitHub ↗

Ships withruview

π RuView turns commodity WiFi signals into real-time spatial intelligence, vital sign monitoring, and presence detection — all without a single pixel of video.

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