labelstats#
- dataeval.metrics.stats.labelstats(labels: Iterable[_SupportsArray[dtype[Any]] | _NestedSequence[_SupportsArray[dtype[Any]]] | bool | int | float | complex | str | bytes | _NestedSequence[bool | int | float | complex | str | bytes]]) LabelStatsOutput#
Calculates statistics for data labels
This function computes counting metrics (e.g., total per class, total per image) on the labels.
- Parameters:
labels (ArrayLike, shape - [label] | [[label]] or (N,M) | (N,)) – Lists or numpy array of labels. A set of lists where each list contains all labels per image - (e.g. [[label1, label2], [label2], [label1, label3]] or [label1, label2, label1, label3]). If a numpy array, N is the number of images, M is the number of labels per image.
- Returns:
A dictionary-like object containing the computed counting metrics for the labels.
- Return type:
LabelStatsOutput
Examples
Calculating the statistics on labels for a set of data
>>> stats = labelstats(labels) >>> stats.label_counts_per_class {'chicken': 3, 'cow': 8, 'horse': 9, 'pig': 7, 'sheep': 7} >>> stats.label_counts_per_image [3, 2, 3, 4, 1, 5, 4, 4, 4, 4] >>> stats.image_counts_per_label {'chicken': 2, 'cow': 6, 'horse': 7, 'pig': 5, 'sheep': 7} >>> (stats.image_count, stats.class_count, stats.label_count) (10, 5, 34)