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name: unet-plus-plus description: "Routes UNet++ work across the official PyTorch nnU-Net stack and the legacy Keras segmentation-models stack." metadata: disco-role: operating disable-model-invocation: true license: NOASSERTION

UNet++

Use this skill for the repository's two supported operating stacks:

• sub-skills/nnunet/ for the official PyTorch nnU-Net implementation.
• sub-skills/keras/ for the official Keras / segmentation_models implementation.

This repo is not a single uniform package. The PyTorch and Keras sides have very different dependency and runtime assumptions, so future agents should pick one sub-skill first instead of trying to force a combined environment.

What this skill covers

• 3D nnU-Net training, preprocessing, inference, ensembling, pretrained-model management, and trainer/model-selection utilities.
• 2D Keras segmentation model construction, backbone selection, preprocessing, and the BRATS2013 application workflow.
• Safety guidance for legacy TensorFlow/Keras versus modern PyTorch/CUDA environments.

Route first, then read deeper

If the user mentions any of these signals, route immediately:

• nnUNet_train, nnUNet_predict, nnUNet_plan_and_preprocess, nnUNet_convert_decathlon_task, nnUNet_ensemble, nnUNet_determine_postprocessing, nnUNet_download_pretrained_model, nnUNet_change_trainer_class, TaskXXX, nnUNet_raw_data_base, nnUNet_preprocessed, or RESULTS_FOLDER -> use sub-skills/nnunet/.
• Unet, Nestnet, Xnet, FPN, PSPNet, segmentation_models, BRATS2013_application.py, backbone, vgg16, resnet50, or legacy TensorFlow/Keras 1.x setup -> use sub-skills/keras/.

Start here

• Read references/overview.md for the stack split and a quick capability map.
• Read references/troubleshooting.md for cross-cutting environment and routing pitfalls.
• Read references/repo-provenance.md if you need to check whether the generated skill is stale relative to the source checkout.

Minimal verification helpers

• sub-skills/nnunet/scripts/check-nnunet-runtime.py checks the nnU-Net import path, CLI entry points, CUDA status, and a tiny sliding-window smoke.
• sub-skills/keras/scripts/check-segmentation-models.py checks the Keras segmentation model builders on safe tiny inputs.

Operating rules

• Do not mix the two stacks into one Python environment unless you already know the exact compatibility story. The inspected repo snapshot required separate environments.
• Do not depend on the original checkout at runtime. Every instruction here is written to be useful after the source repo is gone.
• Prefer the dedicated sub-skill for concrete workflows and use this root only as a router.
• Treat full nnU-Net training, legacy BRATS2013 training, pretrained model downloads, and other large workflows as documented operations, not as default smoke tests.

Common user prompts this router should send onward

• "How do I train UNet++ with nnU-Net?" -> sub-skills/nnunet/
• "How do I build Xnet with a ResNet backbone?" -> sub-skills/keras/
• "Why does nnUNet_predict say RESULTS_FOLDER is missing?" -> sub-skills/nnunet/
• "Why does PSPNet reject my input shape?" -> sub-skills/keras/
• "What pretrained nnU-Net models are available?" -> sub-skills/nnunet/
• "Which backbones and weights does the Keras stack support?" -> sub-skills/keras/

Cross-cutting references

• references/overview.md for the repo split.
• references/troubleshooting.md for shared environment and selection issues.
• references/repo-routing-metadata.json for managed router placement.

What this root does not do

• It does not teach the full nnU-Net or Keras APIs inline.
• It does not describe source-checkout-only files.
• It does not bundle installation logs, verification reports, or usability cases. Those belong in the review/test artifact tree.

Use the sub-skill that matches the user's stack, then follow its bundled references and scripts.