ClassificationData

class ClassificationData

The ClassificationData class is used to load and preprocess data for a classification task.

It is a subclass of the VisionData class. The ClassificationData class is containing additional data and general methods intended for easily accessing metadata relevant for validating a computer vision classification ML models.

__init__(data_loader: DataLoader, num_classes: Optional[int] = None, label_map: Optional[Dict[int, str]] = None, transform_field: Optional[str] = 'transforms')

__new__(*args, **kwargs)

Attributes

ClassificationData.classes_indices	Return dict of classes as keys, and list of corresponding indices (in Dataset) of samples that include this class (in the label).
ClassificationData.data_dimension	Return how many dimensions the image data have.
ClassificationData.data_loader	Return the data loader.
ClassificationData.has_images	Return True if the data loader has images.
ClassificationData.has_labels	Return True if the data loader has labels.
ClassificationData.n_of_samples_per_class	Return a dictionary containing the number of samples per class.
ClassificationData.num_classes	Return the number of classes in the dataset.
ClassificationData.num_samples	Return the number of samples in the dataset.
ClassificationData.original_num_samples	Return the number of samples in the original dataset.
ClassificationData.task_type	Return the task type (classification).
ClassificationData.transform_field	Return the data loader.

Methods

ClassificationData.assert_images_valid()	Assert the image formatter defined is valid.
ClassificationData.assert_labels_valid()	Assert the label formatter defined is valid.
ClassificationData.batch_of_index(*indices)	Return batch samples of the given batch indices.
ClassificationData.batch_to_images(batch)	Transform a batch of data to images in the accpeted format.
ClassificationData.batch_to_labels(batch)	Extract the labels from a batch of data.
ClassificationData.copy([n_samples, ...])	Create new copy of this object, with the data-loader and dataset also copied, and altered by the given parameters.
ClassificationData.from_dataset(data[, ...])	Create VisionData instance from a Dataset instance.
ClassificationData.get_augmented_dataset(aug)	Return a copy of the vision data object with the augmentation in the start of it.
ClassificationData.get_classes(batch_labels)	Get a labels batch and return classes inside it.
ClassificationData.get_transform_type()	Return transforms handler created from the transform field.
ClassificationData.infer_on_batch(batch, ...)	Return the predictions of the model on a batch of data.
ClassificationData.init_cache()	Initialize the cache of the classes' metadata info.
ClassificationData.is_sampled()	Return whether the vision data is running on sample of the data.
ClassificationData.label_id_to_name(class_id)	Return the name of the class with the given id.
ClassificationData.to_batch(*samples)	Use the defined collate_fn to transform a few data items to batch format.
ClassificationData.to_dataset_index(...)	Return for the given batch_index the sample index in the dataset object.
ClassificationData.update_cache(batch)	Get labels and update the classes' metadata info.
ClassificationData.validate_format(model[, ...])	Validate the correctness of the data class implementation according to the expected format.
ClassificationData.validate_get_classes(batch)	Validate that the get_classes function returns data in the correct format.
ClassificationData.validate_image_data(batch)	Validate that the data is in the required format.
ClassificationData.validate_label(batch)	Validate the label.
ClassificationData.validate_prediction(...)	Validate the prediction.
ClassificationData.validate_shared_label(other)	Verify presence of shared labels.

previous

VisionData.validate_shared_label

next

ClassificationData.classes_indices