Average precision per class

Description

Detection only. On a segmentation fit this refuses by name and points at evaluate_cases(). The name carries no detection_ prefix on purpose: this is an S3 generic, so a future task gains a method under the same name rather than a second spelling of one idea.

Usage

evaluate_classes(object, ...)

## S3 method for class 'platypus_fit'
evaluate_classes(object, model = NULL, split = "validation", ...)

Arguments

object	A fit from platypus_fit() on a detection specification.
…	Unused.
model	Which model, when the specification trained several.
split	Which split to score.

Details

The row that matters on unbalanced data, which is most detection data: BCCD has 4,155 red cells against 372 white and 361 platelets, so a single number is a number about red cells. evaluate() deliberately carries no overall precision or recall for the same reason - averaging them over classes needs a weighting, and every choice of weighting is a different claim.

Value

A data frame, one row per class: average precision at IoU 0.5, the mean overlap of the boxes that matched, the number of true boxes, and precision and recall at the model’s operating_point.

Examples

library("rplatypus")

evaluate_classes(fit, "bccd")

# The row that matters on unbalanced data, which is most data: BCCD has 4155 red
# cells against 372 white and 361 platelets, so a single number is a number about
# red cells.