:py:mod:`anomalib.utils.callbacks.metrics_configuration`
========================================================

.. py:module:: anomalib.utils.callbacks.metrics_configuration

.. autoapi-nested-parse::

   Metrics Configuration Callback.



Module Contents
---------------

Classes
~~~~~~~

.. autoapisummary::

   anomalib.utils.callbacks.metrics_configuration.MetricsConfigurationCallback




.. py:class:: MetricsConfigurationCallback(adaptive_threshold: bool, task: str = 'segmentation', default_image_threshold: Optional[float] = None, default_pixel_threshold: Optional[float] = None, image_metric_names: Optional[List[str]] = None, pixel_metric_names: Optional[List[str]] = None, normalization_method: str = 'min_max')

   Bases: :py:obj:`pytorch_lightning.callbacks.Callback`

   Metrics Configuration Callback.

   .. py:method:: setup(_trainer: pytorch_lightning.Trainer, pl_module: pytorch_lightning.LightningModule, stage: Optional[str] = None) -> None

      Setup image and pixel-level AnomalibMetricsCollection within Anomalib Model.

      :param _trainer: PyTorch Lightning Trainer
      :type _trainer: pl.Trainer
      :param pl_module: Anomalib Model that inherits pl LightningModule.
      :type pl_module: pl.LightningModule
      :param stage: fit, validate, test or predict. Defaults to None.
      :type stage: Optional[str], optional