Keras
Keras 3 API documentation
KerasCV
Losses
Binary Penalty Reduced Focal CrossEntropy

Binary Penalty Reduced Focal CrossEntropy

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BinaryPenaltyReducedFocalCrossEntropy class 

keras_cv.losses.BinaryPenaltyReducedFocalCrossEntropy(
    alpha=2.0,
    beta=4.0,
    from_logits=False,
    positive_threshold=0.99,
    positive_weight=1.0,
    negative_weight=1.0,
    reduction="sum_over_batch_size",
    name="binary_penalty_reduced_focal_cross_entropy",
)

Implements CenterNet modified Focal loss.

Compared with keras.losses.BinaryFocalCrossentropy, this loss discounts for negative labels that have value less than positive_threshold, the larger value the negative label is, the more discount to the final loss.

User can choose to divide the number of keypoints outside the loss computation, or by passing in sample_weight as 1.0/num_key_points.

Arguments

  • alpha: a focusing parameter used to compute the focal factor. Defaults to 2.0. Note, this is equivalent to the gamma parameter in keras.losses.BinaryFocalCrossentropy.
  • beta: a float parameter, penalty exponent for negative labels, defaults to 4.0.
  • from_logits: Whether y_pred is expected to be a logits tensor, defaults to False.
  • positive_threshold: Anything bigger than this is treated as positive label, defaults to 0.99.
  • positive_weight: single scalar weight on positive examples, defaults to 1.0.
  • negative_weight: single scalar weight on negative examples, defaults to 1.0.

Inputs: y_true: [batch_size, …] float tensor y_pred: [batch_size, …] float tensor with same shape as y_true.

References