Torchmetrics v1.2

Torchmetrics v1.2 introduces a clustering subdomain with 11 metrics for evaluating unsupervised clustering results, distinguishing between eight extrinsic metrics that compare predicted clusters with available ground-truth labels and three intrinsic metrics that assess cluster cohesion and separation using embedded data without labels. The release explains clustering as a technique for finding hidden data structures when the number and meaning of groups are not known in advance, contrasting it with supervised classification. It also expands MeanAveragePrecision for object detection with micro averaging across classes and selectable computation backends, including faster-coco-eval alongside pycocotools. Additional fixes address duplicate dictionary keys in MetricCollection, missing or incorrect metric metadata, and issues affecting BootStrapper, PearsonCorrCoef, and RecallAtFixedPrecision. The project recommends upgrading, notes adoption in more than 2,400 projects and over 1,600 GitHub stars, and invites community feedback and contributions.

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