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name: inference-evaluation description: "Operate MedSegDiff checkpoint inference and image-based segmentation evaluation for ISIC, BRATS, and bounded custom-data workflows." disable-model-invocation: true metadata: disco-role: operating license: MIT

Inference and evaluation

Use this sub-skill when a prepared MedSegDiff runtime must sample masks from a compatible checkpoint or score already-written prediction images. Keep the sampling and scoring decisions separate:

1. Use references/workflows.md to inspect the sampler flags, effective input channels, version and solver behavior, dataset branch contracts, checkpoint compatibility, ensemble aggregation, and output names.
2. Use references/metrics-and-output.md before interpreting ISIC IoU/Dice or per-class results. Its filename and threshold rules deliberately document brittle source behavior.
3. Run scripts/inspect_sample_cli.py for a no-GPU parser/default/effective-plan check. It does not import the project, open a checkpoint, or touch data.
4. Run scripts/evaluate_isic.py for deterministic ISIC-style ensemble-image scoring, or scripts/evaluate_per_class.py for a bounded binary two-class report. These scripts are self-contained and do not import the original repository.
5. Only perform real diffusion sampling in an explicitly prepared runtime with a compatible CUDA device, trained checkpoint, dataset, and approved output directory. Parser checks and image evaluators do not prove that sampling works.

Do not use this sub-skill for the training loop or primary dataset preparation. Do not treat the repository's original sampling/evaluation scripts as safe CPU smoke tests; the known failure modes and required patches are recorded in references/troubleshooting.md.