wehub-resource-sync
9740bc64c9
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1.7 KiB
1.7 KiB
description, argument-hint
| description | argument-hint |
|---|---|
| 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. | [camera-free|camera-supervised|embeddings|domain-gen|snn|gpu] [--epochs N] |
/ruview-train
Train, fine-tune, evaluate, or publish a RuView model.
- Invoke the
ruview-model-trainingskill. - 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 50then-- --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 ondata/paired/, eval withnode scripts/eval-wiflow.js. ~19 min, 92.9% PCK@20. Needsdata/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-runto smoke-test). VM auto-deletes unless--keep-vm.
- camera-free (Track A) —
- After training:
cd v2 && cargo test --workspace --no-default-features,python archive/v1/data/proof/verify.py. To publish:python scripts/publish-huggingface.py. - Hand off to
/ruview-verifyfor the witness bundle.