Dice Score

Functional Interface

torchmetrics.functional.dice_score(preds, target, bg=False, nan_score=0.0, no_fg_score=0.0, reduction='elementwise_mean')

Compute dice score from prediction scores.

Parameters

• preds (Tensor) – estimated probabilities

• target (Tensor) – ground-truth labels

• bg (bool) – whether to also compute dice for the background

• nan_score (float) – score to return, if a NaN occurs during computation

• no_fg_score (float) – score to return, if no foreground pixel was found in target

• reduction (Literal[‘elementwise_mean’, ‘sum’, ‘none’, None]) –

  a method to reduce metric score over labels.

  • 'elementwise_mean': takes the mean (default)

  • 'sum': takes the sum

  • 'none' or None: no reduction will be applied

Return type

Tensor

Returns

Tensor containing dice score

Example

>>> from torchmetrics.functional import dice_score
>>> pred = torch.tensor([[0.85, 0.05, 0.05, 0.05],
...                      [0.05, 0.85, 0.05, 0.05],
...                      [0.05, 0.05, 0.85, 0.05],
...                      [0.05, 0.05, 0.05, 0.85]])
>>> target = torch.tensor([0, 1, 3, 2])
>>> dice_score(pred, target)
tensor(0.3333)