Built-in component catalogue#

Almost everything in a KonfAI config is a component referenced by name: a model, a loss, a metric, a transform, an augmentation, a scheduler, or a storage backend. This section is the catalogue of what ships in the box — these names are exactly what you reference in YAML: copy them into your config verbatim, with the constructor arguments listed alongside.

Note

These pages are generated from a source-level read of konfai/: class names, constructor signatures and defaults are taken directly from the code.

The pages#

Start with Models (the network you train), then Losses & metrics (what you attach to its named outputs). Transforms and Augmentations cover the data pipeline, Schedulers the loss-weight and learning-rate schedules, and Storage backends & formats the on-disk formats.

How a name is resolved#

Every component name in a config is resolved by konfai.utils.utils.get_module in one of two ways:

Form

Example

Resolves to

bare name

Dice, Standardize, Flip

inside that kind’s package (konfai.metric.measure, konfai.data.transform, konfai.data.augmentation, …)

module:Class

torch:nn:L1Loss, monai.losses:DiceLoss, Loss:MyWrapper

any importable module — an installed library or a local .py file next to your config (the current working directory is on sys.path)

So the tables below list the bare name for built-ins; you are never limited to them — any importable class that satisfies the same contract works via the module:Class form. See Configuration model for the full resolution rules and Extension points for how to write your own.

How to discover a component’s parameters#

The tables give the key constructor arguments and defaults, but the exact, always-current parameter set is whatever the class’s __init__ declares — the reflection engine binds YAML keys directly to constructor parameter names. Two ways to get the exhaustive list for any component:

  1. Let KonfAI materialise the defaults. Reference the component in a config and run the workflow (or run with KONFAI_CONFIG_MODE=default). KonfAI writes every resolved default back into the YAML file, giving you a complete, fully-expanded subtree to edit. (This is the same config-mutation behaviour that surprises new users — here it is a feature.)

  2. Read the signature. Bare names map to konfai/metric/measure.py, konfai/data/transform.py, konfai/data/augmentation.py, konfai/models/**, and konfai/metric/schedulers.py.

Next steps#