PPG SPIHT · 2×

Reproduce the ppg-spiht-2x configuration. Prepare its dataset before running the workflow; use the linked configuration to inspect the exact settings.

Overview

• Modality: PPG
• Structure: codec
• Target compression ratio: 2×
• Sample rate: 64 Hz
• Config: Operating point declared in the registry; no training config.
• Run name: ppg_spiht_64hz_02x_golden
• Hugging Face target: Ambiq/compressionkit-ppg-spiht-2x-v1.0

Dataset & License

• Dataset: Open unified PPG v1 (dataset_id: ppg-unified-strict-sanitize-v1)
• License: Open (BIDMC, BUT PPG, PPG-DaLiA, WESAD — mixed open licenses, no restricted-access dependency)
• Notes: Sources: BIDMC, BUT PPG, PPG-DaLiA, and WESAD. Published v1 PPG goldens are MESA-free. Build the cache with scripts/build_ppg_cache.py.

The lifecycle runner pre-flights dataset availability before training (see the dataset contract).

Reproduction

# Single command, end-to-end.
uv run compressionkit golden run ppg-spiht-2x

Results land under results/ppg_spiht_64hz_02x_golden/; deploy artifacts under results/ppg_spiht_64hz_02x_golden/deploy/.

Evaluation Metrics

See the modality model zoo for the full metrics table:

• PPG models

Evaluation writes quality_scorecard.json and summary.json under its results/<run>/.

Deploy Artifacts

The deploy package contains the following artifacts, depending on the export configuration:

• spiht_config.json / spiht_app_config.h — codec parameters (language-neutral + C header).
• c_sources/spiht.[ch] — portable C99 SPIHT reference.
• sample_stimulus.npz / reference_vectors.npz — license-safe test frames and known-good encode/decode vectors.
• model_card.json, deploy_manifest.json — metadata.

Customization Notes

• This operating point is declared directly in compressionkit/experiments/registry.py (no training YAML) — add a new registry entry to explore a neighbouring operating point.
• To resume publishing without retraining, pass --skip-train to compressionkit golden run ppg-spiht-2x --publish.