Torchmetrics v1.0: Visualize model performance with 100+ metrics

TorchMetrics v1.0 marks the library’s first major stable release, introducing built-in Matplotlib-based plotting through a unified `.plot` method that can visualize individual metric values, training histories, multi-output metrics, and specialized outputs such as confusion matrices and ROC curves. The release surpasses 100 metrics across 12 categories, adding 15 capabilities including audio, classification fairness, regression, image, and object-detection metrics, as well as wrappers such as Running for calculating running averages. Other changes include official Python 3.11 support, removal of Python 3.7 support, deprecation of root-level imports for many subpackage metrics, and a shift to pycocotools as the MeanAveragePrecision backend for improved correctness and performance. The project plans to continue expanding its metric coverage and reports adoption in more than 12,000 projects alongside over 1,500 GitHub stars.

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