Creates an R6 class object for use with the SSD deep learning architecture based on the paper
an SSD loss function
W. Liu, D. Anguelov, D. Erhan, C. Szegedy, S. Reed, C-Y. Fu, A. Berg. SSD: Single Shot MultiBox Detector.
available here:
\url{https://arxiv.org/abs/1512.02325}
ssdLoss <- LossSSD$new( dimension = 2L, backgroundRatio = 3L, minNumberOfBackgroundBoxes = 0L, alpha = 1.0, numberOfClassificationLabels ) ssdLoss$smooth_l1_loss( y_true, y_pred ) ssdLoss$log_loss( y_true, y_pred ) ssdLoss$compute_loss( y_true, y_pred )
A process object.
image dimensionality.
The maximum ratio of background to foreround for weighting in the loss function. Is rounded to the nearest integer. Default is 3.
The minimum number of background boxes to use in loss computation per batch. Should reflect a value in proportion to the batch size. Default is 0.
Weighting factor for the localization loss in total loss computation.
number of classes including background.
$smooth_l1_loss smooth loss
$log_loss log loss
$compute_loss computes total loss.
Tustison NJ