VisionData#

class VisionData[source]#

VisionData is the base data object of deepcheck vision used as input to checks and suites.

Parameters

batch_loader: A batch_loader which load a batch of data in an iterative manner. Batch loader batch output must be a dictionary in BatchOutputFormat format. The batch loader must provide SHUFFLED batches.
task_typestr: The task type of the data. can be one of the following: ‘classification’, ‘semantic_segmentation’, ‘object_detection’, ‘other’. For ‘other’, only image related checks (such as ImagePropertyOutliers) will be run.
label_mapDict[int, str], optional: A dictionary mapping class ids to their names.
dataset_name: str, optional: Name of the dataset to use in the displays instead of “Train” or “Test”.
reshuffle_data: bool, default=True: If True we will attempt to shuffle the batch loader. Only set this to False if the data is already shuffled.

__init__(batch_loader, task_type: Literal['classification', 'object_detection', 'semantic_segmentation', 'other'], label_map: Optional[Dict[int, str]] = None, dataset_name: Optional[str] = None, reshuffle_data: bool = True)[source]#

Attributes

`VisionData.batch_loader`	Return the batch loader used be the vision data.
`VisionData.has_additional_data`	Return True if the batch loader contains additional_data.
`VisionData.has_embeddings`	Return True if the batch loader contains embeddings.
`VisionData.has_image_identifiers`	Return True if the batch loader contains image identifiers.
`VisionData.has_images`	Return True if the batch loader contains images.
`VisionData.has_labels`	Return True if the batch loader contains labels.
`VisionData.has_predictions`	Return True if the batch loader contains predictions.
`VisionData.num_classes`	Return a number of possible classes based on model proba, label map or observed classes.
`VisionData.number_of_images_cached`	Return True if the number of images processed and whose statistics were cached.
`VisionData.task_type`	Return True if the batch loader contains labels.

Methods

`VisionData.copy`([reshuffle_data, batch_loader])	Create new copy of the vision data object with clean cache.
`VisionData.get_cache`([use_class_names])	Return a dictionary of stored cache.
`VisionData.get_observed_classes`([...])	Return a dictionary of observed classes either as class ids or as the class names.
`VisionData.head`([num_images_to_display])	Show data from a single batch of this VisionData.
`VisionData.init_cache`()	Initialize the cache.
`VisionData.update_cache`(batch_size, ...)	Update cache based on newly arrived batch.
`VisionData.validate`()	Validate the VisionData functionalities implemented by the user and set which formatters were implemented.

vision_data

VisionData.batch_loader