dataeval¶

DataEval provides a simple interface to characterize image data and its impact on model performance across classification and object-detection tasks. It also provides capabilities to select and curate datasets to test and train performant, robust, unbiased and reliable AI models and monitor for data shifts that impact performance of deployed models.

Submodules¶

`config`	Global configuration settings for DataEval.
`data`	Provides utility functions for interacting with Computer Vision datasets.
`detectors`	Detectors can determine if a dataset or individual images in a dataset are indicative of a specific issue.
`metadata`	Explanatory functions using metadata and additional features such as ood or drift
`metrics`	Metrics are a way to measure the performance of your models or datasets that can then be analyzed in the context of a given problem.
`outputs`	Output classes for DataEval to store function and method outputs
`typing`	Common type protocols used for interoperability with DataEval.
`utils`	The utility classes and functions are provided by DataEval to assist users in setting up data and architectures that are guaranteed to work with applicable DataEval metrics.
`workflows`	Workflows perform a sequence of actions to analyze the dataset and make predictions.

Functions¶

`log`([level, handler])	Helper for quickly adding a StreamHandler to the logger. Useful for debugging.