ICC demo workspace: recordings, curated datasets, training workflows
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ash pushed to main at ash/icc-demo-0 2026-05-28 03:15:12 -04:00
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R Roman Pope e620d86701 Add Ash's good recordings (USRP 72/97/2GHz captures)
Copied from qoherent/ash_test_demo_repo/good/ for the WavesFM ICC demo.
SigMF data + meta pairs, ~27 MB total. Use these as input to the Curator.
2026-05-28 07:03:52 -04:00
.riahub Training run - 2026-05-28 07:03:18 2026-05-28 07:03:18 -04:00
good Add Ash's good recordings (USRP 72/97/2GHz captures) 2026-05-28 07:03:52 -04:00
scripts Add adapt_dataset.py + re-trigger training canary 2026-05-28 01:48:03 -04:00
.gitattributes Initialize ICC demo workspace with LFS tracking + README 2026-05-28 01:35:31 -04:00
icc_canary_2026_05_28-v1.0.0.h5 Add canary dataset: icc_canary_2026_05_28 v1.0.0 (3 recs, 144 slices) 2026-05-28 01:40:55 -04:00
README.md Initialize ICC demo workspace with LFS tracking + README 2026-05-28 01:35:31 -04:00

ICC Demo Workspace

Recordings, curated datasets, and training workflows for the WavesFM ICC presentation.

Workflow

  1. Upload recordings — drop .sigmf-data + .sigmf-meta pairs. Git LFS tracks them automatically via .gitattributes.
  2. Curate — open the Curator UI, select recordings from this repo, configure slicer + qualifier, produce an HDF5 dataset.
  3. Commit dataset — use the Curator's "Commit to Repository" button to land the curated .h5 back here.
  4. Train — open the Model Trainer, select this repo + WavesFM Linear Probe (or LoRA), pick the dataset, submit the run.
  5. Watch action_run — the trainer renders .riahub/workflows/train.yaml and triggers a runner job. Progress lives under the Actions tab.

Notes

  • WavesFM foundation model: qoherent/wavesfm-base/wavesfm-v1p0.pth — injected into the workflow automatically.
  • If you upload a binary format not in .gitattributes, add it BEFORE the first commit of that file (LFS can't retroactively un-track).