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nv-segment-ct-finetune

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Runs standard or fixed-channel softmax finetuning of NV-Segment-CT VISTA3D on CT NIfTI image/label datasets, with optional MONAI-native MLflow tracking and checkpoint evidence. Uses softmax for predefined, mutually exclusive classes; keeps the standard workflow when point prompts or runtime-variable classes are needed. Not for clinical validation.

skills/nv-segment-ct-finetune/SKILL.md

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Nov 11, 2026
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Apache-2.0

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# NV-Segment-CT Finetune

## Purpose

- Used for smoke or dataset finetuning of NV-Segment-CT VISTA3D on CT NIfTI labels, including the upstream fixed-channel softmax workflow and optional MLflow tracking. Not for clinical validation.
- Wraps the upstream MONAI bundle entrypoint; do not replace it with handwritten training or inference code.
- Manifest inputs are `dataset_dir`, `datalist`, `target_anatomy`, `label_mapping`, `smoke`, `sanity`, `auto_seg`, `softmax`, `skip_formal_eval`, `mlflow_tracking_uri`, `mlflow_experiment_name`, and `mlflow_run_name`.
- Manifest outputs are `finetuned_ckpt` and schema-checked `result_json`.

## Instructions

- Run only the caller-requested preset, dataset, output directory, and compute budget. Dependency setup and remote tracking require the caller's approval. After reporting the result, stop; further training, publishing, or deployment is a separate request.
- Run `scripts/run_finetune.py`; do not patch files under `bundle/` or upstream checkouts during normal skill use.
- For standalone Bash, include the fresh-environment setup line before the wrapper; benchmark venvs start empty.
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