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name: image-hypermodels description: "Routes KerasTuner's built-in HyperResNet, HyperXception, HyperEfficientNet, and HyperImageAugment workflows, including shape contracts, search-space overrides, augmentation composition, backend layout, and safe build gating." disable-model-invocation: true metadata: disco-role: operating license: Apache 2.0

Image Hypermodels

Use this sub-skill when a task names HyperResNet, HyperXception, HyperEfficientNet, or HyperImageAugment, or asks for a KerasTuner image architecture/augmentation search space. Route generic tuner and HyperParameters questions to the parent skill's generic tuning route.

Route by intent

• ResNet classifier or feature extractor → read references/api-reference.md for HyperResNet, then follow the staged architecture workflow in references/workflows.md.
• Xception classifier or feature extractor → use the same API reference and workflow, checking the Xception-specific hyperparameters before building.
• EfficientNet classifier → read the EfficientNet and composition sections first. Its Keras Applications call uses the default ImageNet weights and may download files; do not build it implicitly or in an offline smoke check.
• Searchable image augmentation → use HyperImageAugment. Choose fixed sequential mode with augment_layers=0/None, or RandAugment-like mode with a positive integer or two-integer range.
• Augmentation plus EfficientNet → pass a Keras Model or HyperModel as augmentation_model; the latter is built with the same HyperParameters object. Use the explicit expensive-build gate described in the workflow.

Before calling build(hp)

1. Check keras_tuner.__version__ and the active keras.backend.image_data_format(). input_shape excludes the batch axis and must use the active channel layout.
2. Supply at least one of input_shape and input_tensor. If both are supplied, the implementation uses input_tensor and obtains its source inputs; do not rely on input_shape to override a tensor's shape.
3. For ResNet/Xception, set include_top=True only when classes is supplied. With include_top=False, classes is not required and the returned model is a feature extractor without the classifier compile step.
4. For EfficientNet, always supply a truthy classes value. It has no include_top argument: the built-in hypermodel always constructs and compiles its classifier head.
5. Decide whether a build is safe before starting it. ResNet/Xception can be slow, and EfficientNet also resizes to a variant-specific image size and can fetch ImageNet weights.

Progressive disclosure

• Read references/api-reference.md for exact 1.4.8 signatures, validation, model outputs, hyperparameter names/defaults, transform ranges, and backend layout rules.
• Read references/workflows.md for safe construction, deterministic hyperparameter overrides, input-tensor composition, augmentation modes, and staged EfficientNet use.
• Read references/troubleshooting.md for validation errors, backend shape failures, stale test expectations, slow/OOM builds, and weight-cache/offline recovery.
• From the skill root, run sub-skills/image-hypermodels/scripts/smoke_build.py for a flag-gated augmentation-only check. It uses the installed package by default; pass --repo-root <CHECKOUT> to select a source checkout explicitly. Opt into architecture builds explicitly; opt into EfficientNet only when a possible network/weight-cache access is acceptable and an external timeout is set.

Operating guardrails

• Treat the installed signatures and the current implementation's search-space names as the contract. In particular, current augmentation names are augment_layers and factor_<transform>; do not substitute older randaug_count/randaug_mag names.
• Prefer an existing HyperParameters object with Fixed, Choice, or compatible values to make a smoke build deterministic. A pre-existing name whose domain conflicts with the hypermodel can fail during build.
• Assert the model input(s), output shape, selected hp.values, and compiled state appropriate to include_top before starting a search.
• Keep heavyweight or weight-fetching builds behind a user-visible flag or a bounded subprocess/time limit. Never put an EfficientNet build in an import check or an automatic default smoke path.