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name: keras description: "Guides the legacy Keras segmentation-models stack for UNet++, Nestnet, Xnet, FPN, PSPNet, backbone selection, and BRATS2013 usage." metadata: disco-role: operating disable-model-invocation: true license: NOASSERTION

Keras

Use this sub-skill for the repository's legacy 2D Keras stack. It covers the segmentation_models package, the original helper functions, and the BRATS2013 application script.

Route here when the user asks about

• Unet, Nestnet, Xnet, FPN, PSPNet, or segmentation_models.
• Backbone names such as vgg16, resnet50, densenet121, inceptionv3, or inceptionresnetv2.
• BRATS2013_application.py, plot_model, pydot, or legacy TensorFlow 1.x / Keras 2.2.2 setup.
• Input-shape issues, backbone weight availability, or PSPNet size guards.

What this sub-skill does

• Explains how to instantiate the segmentation models with safe defaults.
• Summarizes available backbones and preprocessing choices.
• Helps diagnose legacy dependency problems and input-shape restrictions.
• Gives a safe runtime smoke script for building tiny models without training.

What to read first

• references/legacy-stack.md for the version and environment assumptions.
• references/backbones.md for the backbone catalog and preprocessing behavior.
• references/api-reference.md for builder signatures and helper functions.
• references/workflows.md for custom-data and BRATS2013 workflows.
• references/troubleshooting.md for common shape, download, and dependency problems.

Recommended workflow order

1. Check whether the user really needs the legacy Keras stack.
2. Confirm the TensorFlow / Keras / Python compatibility assumptions.
3. Pick the architecture and backbone.
4. Verify input-shape constraints before compiling or fitting.
5. Use the bundled smoke script for a tiny build check.

Important guardrails

• This stack is separate from nnU-Net. Do not mix it with the modern PyTorch environment.
• The inspected snapshot used Python 3.6 plus TensorFlow 1.4.1 and Keras 2.2.2 for the legacy route.
• BRATS2013_application.py is a data-bound workflow with many assumptions; it is not a cheap smoke test.
• classification_models pretrained weights and test code can require the network, so treat them as reference-only unless the user explicitly wants a download path.
• The model builders should be exercised with encoder_weights=None first when you only need structure and shape validation.

Bundled runtime helper

• scripts/check-segmentation-models.py builds tiny Unet, Nestnet, Xnet, FPN, and PSPNet models as a safe runtime smoke.

Common questions this sub-skill answers

• Which backbones are available, and which preprocessing function goes with each one?
• Why does a model reject my input size?
• What are the public builder signatures for Unet, Nestnet, Xnet, FPN, and PSPNet?
• How do I interpret the BRATS2013 application arguments and data layout?
• Why is plot_model or pydot failing in this environment?
• What should I do when pretrained weights cannot be downloaded?

Where to go next

• Use references/workflows.md for the concrete recipes.
• Use references/troubleshooting.md when the failure is about shape guards, legacy package pins, or optional dependencies.
• Use the root ../../SKILL.md only if the user has not yet chosen between the Keras and nnU-Net stacks.