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2.4 KiB
2.4 KiB
/ruview-train — train a RuView model
Train / evaluate / publish a RuView model. Track: $ARGUMENTS (one of camera-free, camera-supervised, embeddings, domain-gen, snn, gpu; if empty, ask).
- camera-free (WiFlow pose, no labels):
cd v2 && 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. Benchnode scripts/benchmark-wiflow.js, evalnode scripts/eval-wiflow.js. - camera-supervised (ADR-079, 92.9% PCK@20, ~19 min):
python scripts/collect-ground-truth.py(MediaPipe landmarks; needsdata/pose_landmarker_lite.task),python scripts/collect-training-data.py(CSI capture),node scripts/align-ground-truth.js(timestamp align), thencd v2 && cargo run -p wifi-densepose-sensing-server -- --train --dataset data/paired/ --epochs <N> --save-rvf model.rvf, evalnode scripts/eval-wiflow.js(reports PCK@20). - embeddings (AETHER ADR-024 / spectrogram ADR-076):
wifi-densepose-train+wifi-densepose-ruvector;-- --model model.rvf --embed,-- --model model.rvf --build-index env. 171K emb/s on M4 Pro. - domain-gen (MERIDIAN ADR-027): domain-generalization options in the training pipeline +
ruview_metrics. - snn (local env adaptation, <30 s):
node scripts/snn-csi-processor.js --port 5006;docs/tutorials/cognitum-seed-pretraining.md; ADR-084/085 (RaBitQ), ADR-086 (novelty gate). - gpu:
gcloud auth login && gcloud config set project cognitum-20260110, thenbash scripts/gcloud-train.sh --dry-run(smoke),bash scripts/gcloud-train.sh --gpu l4 --hours 2(proto,$0.80/hr),$3.60/hr),bash scripts/gcloud-train.sh --gpu a100 --config scripts/training-config-sweep.json(bash scripts/gcloud-train.sh --sweep(full sweep). VM auto-deletes unless--keep-vm. Local Mac:bash scripts/mac-mini-train.sh. Bench:python scripts/benchmark-model.py.
Data: data/recordings/ raw CSI · data/csi/ pretrain · data/mmfi/ MM-Fi · data/paired/ camera↔CSI · data/ground-truth/ MediaPipe · models/ artifacts. Record more: python scripts/record-csi-udp.py.
After training: cd v2 && cargo test --workspace --no-default-features, cd .. && python archive/v1/data/proof/verify.py (VERDICT: PASS). Publish: python scripts/publish-huggingface.py (or .sh; docs/huggingface/). Then run /ruview-verify.