torchvision.ops.sigmoid_focal_loss(inputs: Tensor, targets: Tensor, alpha: float = 0.25, gamma: float = 2, reduction: str = 'none') Tensor[source]

Loss used in RetinaNet for dense detection:

  • inputs (Tensor) – A float tensor of arbitrary shape. The predictions for each example.

  • targets (Tensor) – A float tensor with the same shape as inputs. Stores the binary classification label for each element in inputs (0 for the negative class and 1 for the positive class).

  • alpha (float) – Weighting factor in range (0,1) to balance positive vs negative examples or -1 for ignore. Default: 0.25.

  • gamma (float) – Exponent of the modulating factor (1 - p_t) to balance easy vs hard examples. Default: 2.

  • reduction (string) – 'none' | 'mean' | 'sum' 'none': No reduction will be applied to the output. 'mean': The output will be averaged. 'sum': The output will be summed. Default: 'none'.


Loss tensor with the reduction option applied.


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