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name: configuration-and-experiments description: "Configure a MedicalDetectionToolkit experiment, inspect its CLI and fold lifecycle, onboard a bounded toy fixture, and diagnose configuration or experiment-directory failures without entering model internals or running training." metadata: disco-role: operating disable-model-invocation: true license: Apache 2.0

Configuration and experiments

Use this node when the task is to turn a detector idea into a checked experiments/<name>/configs.py contract, choose a model and 2D/3D operating dimension, create or inspect an experiment directory, select folds, or prepare a small toy fixture. This repository is legacy and explicitly unmaintained; pin the checkout/package version before treating a configuration as reproducible. Do not start training, testing, analysis, or CUDA compilation while merely validating configuration. The bundled scripts are safe inspection/fixture helpers and do not import the source checkout.

Route by intent

1. Need a config or model/dimension decision? Read configuration.md, then route detector-specific behavior to models-and-architectures and data shape/path behavior to data-and-preprocessing.
2. Need an invocation or lifecycle decision? Read cli-reference.md and experiment-layout.md. Use inspect_cli.py to print or validate the bounded command contract without importing exec.py.
3. Need toy onboarding? Read the toy section in experiment-layout.md and use generate_toy_fixture.py. It refuses to overwrite existing fixture files and has hard count, shape, and size caps. Adapt n_train_val_data in a copied config to the fixture count before attempting any framework workflow.
4. A command/config fails? Read troubleshooting.md before changing paths or stored settings. Stop rather than silently switching folds, replacing a snapshot, or assuming a modern dependency is compatible.

Operating contract

• A config module exposes a lowercase configs subclass with constructor configs(server_env=None). It sets self.model and self.dim, then calls DefaultConfigs.__init__(self, self.model, server_env, self.dim) before applying experiment-specific paths, data, schedule, and model settings.
• Supported configuration labels evidenced by the experiment files are mrcnn, retina_net, retina_unet, detection_unet, ufrcnn, and in the shared default dispatch ufrcnn_surrounding and prob_detector. Availability is version/config dependent; the dispatch key must exist or construction fails.
• dim=2 uses 2D patch/list settings; dim=3 uses 3D settings. This choice affects channels, patch shape, augmentation, anchors, and model-specific defaults. Do not infer 3D support from a successful CPU import; route runtime prerequisites to cuda-extensions and model details to models-and-architectures.
• Paths are configuration data, not portable defaults. Replace example machine paths with an existing preprocessed-data root and verify that its input_df_name (normally info_df.pickle) and expected .npy records exist. Do not point a new experiment at a snapshot or private dataset by accident.
• Training/testing snapshot scripts into exp_dir; testing then copies the stored model/backbone back to the source tree as temporary modules. This is a legacy mutable workflow. Inspect and obtain an explicit safe workspace before using it; this node does not perform those writes.
• n_cv_splits defaults to 5. With no --folds, train/test loops use range(cf.n_cv_splits). A supplied fold list is not range-validated by the CLI, so validate it yourself and ensure fold IDs match fold_ids.pickle when the experiment is not hold-out.
• --use_stored_settings is a reproducibility choice, not a generic repair switch. Prefer the experiment snapshot for testing and for resumed work when it is complete; otherwise stop on missing or inconsistent configs.py, default_configs.py, model.py, or backbone.py.
• This node owns configuration/lifecycle routing only. It does not explain detector forward passes, data conversion, prediction consolidation, evaluation metrics, or custom-op builds; use the linked sibling nodes.

All detailed tables, source-backed values, and failure handling live in the four linked references so this file remains a router.