Reference#

Encyclopedia MAITEanica.

protocols#

`protocols.ArrayLike`
`protocols.image_classification.Augmentation`(...)	An augmentation protocol for the image classification AI task.
`protocols.image_classification.DataLoader`(...)	A dataloader protocol for the image classification AI task providing batch-level data access.
`protocols.image_classification.Dataset`(...)	A dataset protocol for image classification AI task providing datum-level data access.
`protocols.image_classification.Metric`(*args, ...)	A metric protocol for the image classification AI task.
`protocols.image_classification.Model`(*args, ...)	A model protocol for the image classification AI task.
`protocols.object_detection.Augmentation`(...)	An augmentation protocol for the object detection AI task.
`protocols.object_detection.ObjectDetectionTarget`(...)	An object-detection target protocol.
`protocols.object_detection.DataLoader`(*args, ...)	A dataloader protocol for the object detection AI task providing batch-level data access.
`protocols.object_detection.Dataset`(*args, ...)	A dataset protocol for object detection AI task providing datum-level data access.
`protocols.object_detection.Metric`(*args, ...)	A metric protocol for the object detection AI task.
`protocols.object_detection.Model`(args, *kwargs)	A model protocol for the object detection AI task.

`tasks.evaluate`(*, model[, metric, ...])	Evaluate a model's performance on data according to some metric with optional augmentation.
`tasks.predict`(*, model[, dataloader, ...])	Make predictions for a given model & data source with optional augmentation.
`tasks.evaluate_from_predictions`(*, metric, ...)	Evaluate pre-calculated predictions against target (truth) data for some specified metric.

`interop.metrics.torchmetrics.TMClassificationMetric`(metric)	MAITE-compliant Wrapper for TorchMetrics Classification Metrics.
`interop.metrics.torchmetrics.TMDetectionMetric`(metric)	MAITE-compliant Wrapper for Torchmetrics Detection Metrics.
`interop.models.yolo.YoloObjectDetector`(...)	MAITE-wrapped object detection YOLO model.

`check_type`(name, arg, type_, *[, optional])	Check that an argument is an instance of one or more types.
`check_domain`(name, arg, *[, lower, upper, ...])	Check that an argument falls within `[lower <=] arg [<= upper]`.
`check_one_of`(name, arg, collection, *vals[, ...])	Check that `arg` is a member of `collection` or of `vals`.
`chain_validators`(*validators)	Enable validators to be chained together.

`MaiteException`	Base exception thrown by the MAITE.
`InvalidArgument`	A MAITE interface was passed a bad value or type.

`docs.validate_docstring`(obj[, ignore, ...])	Validate an object's docstring against the NumPy docstring standard [Rf5befd11ff4f-1].
`docs.NumpyDocErrorCode`
`docs.NumPyDocResults`
`pyright.pyright_analyze`(*code_objs_and_or_paths)	Scan a Python object, docstring, or file with pyright.
`pyright.PyrightOutput`	The schema for the JSON output of a pyright scan
`pyright.list_error_messages`(results)	A convenience function that returns a list of error messages reported by pyright.
`pytest.cleandir`()	A pytest fixture that runs a test in a temporary directory as the current working directory.
`project.ModuleScan`()	Uses pyright's type completeness scan to summarize a module's contents.
`project.get_public_symbols`(scan[, ...])	Return all public symbols (functions, classes, etc.) from a module's API.