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

Original implementation from . Loss used in RetinaNet for dense detection:

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

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

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

  • reduction – ‘none’ | ‘mean’ | ‘sum’ ‘none’: No reduction will be applied to the output. ‘mean’: The output will be averaged. ‘sum’: The output will be summed.


Loss tensor with the reduction option applied.


Access comprehensive developer documentation for PyTorch

View Docs


Get in-depth tutorials for beginners and advanced developers

View Tutorials


Find development resources and get your questions answered

View Resources