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name: inference-evaluation description: "Run nnU-Net v2 prediction from dataset/results or explicit model folders, cascades, TTA/tiling/device/process controls, ensembling, postprocessing, evaluation, best-configuration selection, model sharing, and inference troubleshooting." disable-model-invocation: true metadata: disco-role: operating license: Apache 2.0

nnU-Net v2 Inference and Evaluation

Use this sub-skill after a trained, installed, exported, downloaded, or otherwise available nnU-Net v2 model exists and the task is to generate predictions, compare configurations, ensemble outputs, evaluate segmentations, apply postprocessing, or move models between environments.

Route here for

• Running nnUNetv2_predict from nnUNet_results with dataset id/name, trainer, plans, configuration, folds, checkpoint, device, TTA, tiling, process, and split-batch flags.
• Running nnUNetv2_predict_from_modelfolder when the model folder is explicit or nnUNet_results is unavailable.
• Providing previous-stage predictions for cascaded models with -prev_stage_predictions.
• Saving probability maps for downstream nnUNetv2_ensemble and applying postprocessing.pkl.
• Evaluating predictions with nnUNetv2_evaluate_folder or nnUNetv2_evaluate_simple, including ignore labels and partial-prediction --chill behavior.
• Selecting the best trained configuration with nnUNetv2_find_best_configuration after validation probabilities exist.
• Exporting, installing, downloading, or sharing trained/pretrained models, including custom-trainer availability requirements.

Do not handle here

• Creating dataset folders, channel names, labels, or inference input naming from scratch: use data-preparation.
• Planning, preprocessing, experiment planners, or fingerprints: use planning-preprocessing.
• Training folds, validation reruns, --npz recovery, checkpoint creation, or trainer selection for training: use training-configuration.
• Implementing custom trainer subclasses or extension code: use customization-extension; this sub-skill only explains how inference locates custom trainer classes.

Fast Path

1. Confirm the model exists and matches the input dataset: trained result folder with dataset.json, plans.json, and requested fold_* checkpoints, or an explicit model folder with the same files.
2. Check inference image filenames before running expensive prediction:

   python sub-skills/inference-evaluation/scripts/check_inference_inputs.py INPUT_FOLDER MODEL_OR_DATASET_JSON
   

3. Use nnUNetv2_predict when the model is in nnUNet_results:

   nnUNetv2_predict -i INPUT_FOLDER -o OUTPUT_FOLDER -d DATASET001_Example -c 3d_fullres -f 0 1 2 3 4
   

4. Use nnUNetv2_predict_from_modelfolder when the model folder is explicit:

   nnUNetv2_predict_from_modelfolder -i INPUT_FOLDER -o OUTPUT_FOLDER -m MODEL_FOLDER -f 0 1 2 3 4
   

5. Add --save_probabilities when predictions will be ensembled; run nnUNetv2_ensemble, then apply postprocessing if a postprocessing.pkl was selected.
6. Evaluate predictions with nnUNetv2_evaluate_folder when dataset.json and plans.json are available, or nnUNetv2_evaluate_simple for label-list evaluation.

Bundled References

• references/cli-reference.md: exact command routes, flags, defaults, and entry-point distinctions.
• references/inference-and-evaluation.md: workflows for prediction, cascades, ensembling, postprocessing, evaluation, best-configuration selection, and Python API use.
• references/model-sharing.md: export/import/download commands and custom-trainer portability rules.
• references/troubleshooting.md: failure-mode triage for input names, checkpoints, model folders, probabilities, postprocessing metadata, split jobs, devices, and custom trainers.

Bundled Helper

check_inference_inputs.py validates input-folder case/channel naming against dataset.json without loading images or importing nnU-Net. It accepts either a dataset.json path or a model/results folder containing dataset.json.