Dice Score¶
Functional Interface¶
- torchmetrics.functional.dice_score(preds, target, bg=False, nan_score=0.0, no_fg_score=0.0, reduction='elementwise_mean')[source]
Compute dice score from prediction scores.
- Parameters
bg¶ (
bool
) – whether to also compute dice for the backgroundnan_score¶ (
float
) – score to return, if a NaN occurs during computationno_fg_score¶ (
float
) – score to return, if no foreground pixel was found in targetreduction¶ (
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'
orNone
: no reduction will be applied
- Return type
- 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)