skself.data.images_from_directory ================================= .. py:module:: skself.data.images_from_directory Classes ------- .. autoapisummary:: skself.data.images_from_directory.DatasetBuilder Module Contents --------------- .. py:class:: DatasetBuilder(**data: Any) Bases: :py:obj:`pydantic.BaseModel` .. py:class:: Config .. py:attribute:: arbitrary_types_allowed :value: True .. py:attribute:: underscore_attrs_are_private :value: True .. py:method:: peek_dataset() .. py:attribute:: anomaly_composition .. py:attribute:: anomaly_size :type: Optional[int] :value: None .. py:attribute:: batch_size :value: 8 .. py:attribute:: color_dict .. py:attribute:: create_artificial_anomalies :value: True .. py:attribute:: crop_to_aspect_ratio :value: False .. py:attribute:: drop_masks :value: False .. py:property:: ds .. py:attribute:: folders_have_subfolders :value: False .. py:attribute:: global_transform :type: albumentations.Compose :value: None .. py:attribute:: height :value: 256 .. py:attribute:: image_directory .. py:attribute:: mask_directory :type: Optional[pathlib.Path] :value: None .. py:property:: num_classes .. py:property:: num_files .. py:attribute:: pairing_mode :type: Literal['result_only', 'result_with_original', 'result_with_contrastive_pair'] :value: 'result_only' .. py:attribute:: peek :value: True .. py:attribute:: process_deviation :type: albumentations.Compose :value: None .. py:attribute:: repeat :value: True .. py:attribute:: seed :value: 123 .. py:attribute:: shuffle :value: True .. py:attribute:: subset :type: Union[None, Literal['training', 'validation', 'both']] :value: 'training' .. py:attribute:: validation_split :value: 0.2 .. py:attribute:: width :value: 256