SegmentationBinaryAUROC#

class torch_uncertainty.metrics.segmentation.SegmentationBinaryAUROC(max_fpr=None, thresholds=None, ignore_index=None, validate_args=True, **kwargs)[source]#

Image-averaged binary AUROC for dense binary segmentation tasks.

At each image, a per-pixel binary AUROC is computed from the pixel scores \(s_{ij}\) and binary labels \(y_{ij} \in \{0, 1\}\):

\[\text{AUROC}_b = \int_0^1 \text{TPR}_b\!\left(\text{FPR}_b^{-1}(u)\right) \mathrm{d}u,\]

where TPR and FPR are computed by sweeping a threshold over the pixel-level scores of image \(b\). The final metric is the average over all images:

\[\text{AUROC} = \frac{1}{B} \sum_{b=1}^{B} \text{AUROC}_b.\]

This image-wise averaging is the convention used in the dense OOD-detection literature (e.g., MUAD) and behaves better than computing AUROC over the flattened set of all pixels when image sizes or OOD prevalences vary.

Parameters:

  • max_fpr (float | None) – If set, computes the partial AUROC up to this FPR value (passed to BinaryAUROC).

  • thresholds (int | list[float] | Tensor | None) – Optional explicit thresholds to use when computing the ROC.

  • ignore_index (int | None) – Optional label value to ignore.

  • validate_args (bool) – Whether to validate input arguments.

  • kwargs (Any) – Additional keyword arguments, see Advanced metric settings.