Agent skill · research science · nvidia
nv-segment-ct-finetune
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.
Why this skill is useful
Provides specific commands and configurations for fine-tuning the NV-Segment-CT model that the AI wouldn't reliably generate on its own.
What it needs
Requires einops, fire, huggingface_hub, monai, nibabel, numpy, pytorch-ignite, pyyaml, scipy, typer installed locally. About 8k tokens when loaded. Last updated 2026-08-07. 2,816 stars on the source repository.
What this skill does
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 datasetdir, datalist, targetanatomy, labelmapping, smoke, sanity, autoseg, softmax, skipformaleval, mlflowtrackinguri, mlflowexperimentname, and mlflowrunname. Manifest outputs are finetunedckpt and schema-checked resultjson. 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/runfinetune.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. Run the committed script in place from the repo root. Do not copy this skill to a runtime directory, and do not use rm or cleanup commands in generated invocations. If a host exposes runscript, use runscript("scripts/runfinetune.py", args=[...]); otherwise run from the repo root. For the shortest workflow check, use --smoke; for MSD Task06 Lung Tumor reproduction, use --sanity. Choose between the standard and --softmax workflows using the criteria below. Do not combine --softmax with --auto-seg or --sanity. Set --mlflow-experiment-name to enable MLflow for the training phase of either workflow. --mlflow-tracking-uri and --mlflow-run-name require an experiment name. Formal pre/post evaluation does not receive MLflow credentials. Read references/task06-and-results.md only when you need Task06 reference details, output-field definitions, or manual bundle setup notes. …
How to use it
Reference it in AdaL, Claude Code, Cursor or any coding agent — nothing to install:
@skills nvidia/nv-segment-ct-finetune