---

name: customization description: "Add custom models and datasets that match the repo registry, templates, and option-injection contracts." disable-model-invocation: true metadata: disco-role: operating license: NOASSERTION

Customization

Use this sub-skill when the task is to add or debug a custom model class, custom dataset class, registry naming, parser defaults, or model/dataset data-key coupling for this repository.

Route elsewhere when the request is really about:

• running a completed custom training, testing, inference, pretrained, CPU/GPU, or DDP command: translation-workflows
• formatting raw data roots, pairing A/B folders, validating standard layouts, downloading assets, or Cityscapes/HED data preparation: data-preparation
• full new architecture research beyond the registry/template contract: treat as a long-tail gap unless the bundled references below already cover the decision.

References and helper

• references/model-extension.md: model filename/class contract, BaseModel obligations, required lists/methods, checkpoints, and network factory choices.
• references/dataset-extension.md: dataset filename/class contract, BaseDataset obligations, transforms, data dictionaries, and keys expected by existing models.
• references/option-registry.md: dynamic parser injection order, verified default couplings, and CLI implications.
• references/troubleshooting.md: import/class-name, default, device, checkpoint, and data-key failure recovery.
• scripts/check_extension_names.py: safe import-free name checker for --kind model|dataset --name <mode> with optional --repo-root file/class text checks.

Operating order

1. Choose the registry value first: --model <model> or --dataset_mode <mode>.
2. Use the registry naming contract before writing code: model files are models/<model>_model.py with a <Model>Model subclass; dataset files are data/<mode>_dataset.py with a <Mode>Dataset subclass.
3. Put model defaults and model-specific flags in the model class modify_commandline_options; put data-layout/channel defaults and dataset-specific flags in the dataset class modify_commandline_options.
4. Make the dataset dictionary returned by __getitem__ match the model's set_input keys and tensor channel counts.
5. Validate names with the bundled checker before testing command-line imports. If names pass but training/test commands are needed, route to translation-workflows.