ruview-model-training
GitHub用于训练RuView模型，涵盖无相机WiFlow姿态、相机监督姿态、RuVector嵌入、领域泛化及SNN环境适应。支持本地GPU训练及GCloud部署，适用于模型构建、微调、评估和发布。
Trigger Scenarios
Install
npx skills add ruvnet/RuView --skill ruview-model-training -g -y
SKILL.md
Frontmatter
{
"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
Version History
- a3b6e1d Current 2026-08-20 13:12
