ECG SPIHT · 16×

Reproduce the ecg-spiht-16x configuration. Prepare its dataset before running the workflow; use the linked configuration to inspect the exact settings.

Overview

• Modality: ECG
• Structure: codec
• Target compression ratio: 16×
• Sample rate: 256 Hz
• Config: Operating point declared in the registry; no training config.
• Run name: ecg_spiht_256hz_16x_golden
• Hugging Face target: Ambiq/compressionkit-ecg-spiht-16x-v1.0

Dataset & License

• Dataset: PTB-XL (dataset_id: ptb-xl)
• License: CC BY 4.0 (open)
• Notes: Auto-downloaded on first use.

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

Reproduction

# Single command, end-to-end.
uv run compressionkit golden run ecg-spiht-16x

Results land under results/ecg_spiht_256hz_16x_golden/; deploy artifacts under results/ecg_spiht_256hz_16x_golden/deploy/.

Evaluation Metrics

See the modality model zoo for the full metrics table:

• ECG 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 ecg-spiht-16x --publish.