Minor patch release

[1.7.1] - 2025-04-06

Changed

• Enhance Support Adding a MetricCollection to Another MetricCollection in add_metrics Function (#3032)

Fixed

• Fixed absent class MeanIOU (#2892)
• Fixed detection IoU ignores predictions without ground truth (#3025)
• Fixed error raised in MulticlassAccuracy when top_k>1 (#3039)

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Key Contributors

@Isalia20, @rittik9, @SkafteNicki

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Full Changelog: v1.7.0...v1.7.1

More image metrics

The upcoming release of TorchMetrics is set to deliver a range of innovative features and enhancements across multiple domains, further solidifying its position as a leading tool for machine learning metrics. In the image domain, significant additions include the ARNIQA and DeepImageStructureAndTextureSimilarity metrics, which provide new insights into image quality and similarity. Additionally, the CLIPScore metric now supports more models and processors, expanding its versatility in image-text alignment tasks.

Beyond image analysis, the regression package welcomes the JensenShannonDivergence metric, offering a powerful tool for comparing probability distributions. The clustering package also sees a notable update with the introduction of the ClusterAccuracy metric, which helps evaluate the performance of clustering algorithms more effectively.

In the realm of classification, the Equal Error Rate (EER) metric has been added, providing a crucial measure for assessing the performance of classification models, particularly in scenarios where false positives and false negatives have different costs. Furthermore, the MeanAveragePrecision metric now includes a functional interface, enhancing its usability and flexibility for users.

These updates collectively enhance the capabilities of TorchMetrics, making it an even more comprehensive and indispensable resource for machine learning practitioners and researchers.

[1.7.0] - 2025-03-20

Added

• Additions to image domain:
  • Added ARNIQA metric (#2953)
  • Added DeepImageStructureAndTextureSimilarity (#2993)
  • Added support for more models and processors in CLIPScore (#2978)
• Added JensenShannonDivergence metric to regression package (#2992)
• Added ClusterAccuracy metric to cluster package (#2777)
• Added Equal Error Rate (EER) to classification package (#3013)
• Added functional interface to MeanAveragePrecision metric (#3011)

Changed

• Making num_classes optional for one-hot inputs in MeanIoU (#3012)

Removed

• Removed Dice from classification (#3017)

Fixed

• Fixed edge case in integration between class-wise wrapper and metric tracker (#3008)
• Fixed IndexError in MultiClassAccuracy when using top_k with single sample (#3021)

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Key Contributors

@Isalia20, @LorenzoAgnolucci, @nathanpainchaud, @rittik9, @SkafteNicki

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Full Changelog: v1.6.0...v1.7.0

Minor patch release

[1.6.3] - 2024-03-13

Fixed

• Fixed logic in how metric states referencing is handled in MetricCollection (#2990)
• Fixed integration between class-wise wrapper and metric tracker (#3004)

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Key Contributors

@SkafteNicki

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Full Changelog: v1.6.2...v1.6.3

Minor patch release

[1.6.2] - 2024-02-28

Added

• Added zero_division argument to DiceScore in segmentation package (#2860)
• Added cache_session to DNSMOS metric to control caching behavior (#2974)
• Added disable option to nan_strategy in basic aggregation metrics (#2943)

Changed

• Make num_classes optional for classification in case of micro averaging (#2841)
• Enhance Clip_Score to calculate similarities between same modalities (#2875)

Fixed

• Fixed DiceScore when there is zero overlap between predictions and targets (#2860)
• Fixed MeanAveragePrecision for average="micro" when 0 label is not present (#2968)
• Fixed corner-case in PearsonCorrCoef when input is constant (#2975)
• Fixed MetricCollection.update gives identical results (#2944)
• Fixed missing kwargs in PIT metric for permutation wise mode (#2977)
• Fixed multiple errors in the _final_aggregation function for PearsonCorrCoef (#2980)
• Fixed incorrect CLIP-IQA type hints (#2952)

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Key Contributors

@baskrahmer, @czmrand, @rbedyakin, @rittik9, @SkafteNicki, @wooseopkim

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Full Changelog: v1.6.1...v1.6.2

Minor patch release

[1.6.1] - 2024-12-25

Changed

• Enabled specifying weights path for FID (#2867)
• Delete Device2Host caused by comm with device and host (#2840)

Fixed

• Fixed plotting of multilabel confusion matrix (#2858)
• Fixed issue with shared state in metric collection when using dice score (#2848)
• Fixed top_k for multiclassf1score with one-hot encoding (#2839)
• Fixed slow calculations of classification metrics with MPS (#2876)

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Key Contributors

@Isalia20, @nkaenzig, @podgorki, @rittik9, @yuvalkirstain, @zhaozheng09

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Full Changelog: v1.6.0...v1.6.1

More metrics

The latest release of TorchMetrics introduces several significant enhancements and new features that will greatly benefit users across various domains. This update includes the addition of new metrics and methods that enhance the library's functionality and usability.

One of the key additions is the NISQA audio metric, which provides advanced capabilities for evaluating audio quality. In the classification domain, the new LogAUC and NegativePredictiveValue metrics offer improved tools for assessing model performance, particularly in imbalanced datasets. For regression tasks, the NormalizedRootMeanSquaredError metric has been introduced, providing a normalized measure of prediction accuracy that is less sensitive to outliers.

In the field of image segmentation, the new Dice metric enhances the evaluation of segmentation models by providing a robust measure of overlap between predicted and ground truth masks. Additionally, the merge_state method has been added to the Metric class, allowing for more efficient state management and aggregation across multiple devices or processes.

Furthermore, this release includes support for the propagation of the autograd graph in Distributed Data-Parallel (DDP) settings, enabling more efficient and scalable training of models across multiple GPUs. These enhancements collectively make TorchMetrics a more powerful and versatile tool for machine learning practitioners, enabling more accurate and efficient model evaluation across a wide range of applications.

[1.6.0] - 2024-11-12

Added

• Added audio metric NISQA (#2792)
• Added classification metric LogAUC (#2377)
• Added classification metric NegativePredictiveValue (#2433)
• Added regression metric NormalizedRootMeanSquaredError (#2442)
• Added segmentation metric Dice (#2725)
• Added method merge_state to Metric (#2786)
• Added support for propagation of the autograd graph in DDP setting (#2754)

Changed

• Changed naming and input order arguments in KLDivergence (#2800)

Deprecated

• Deprecated Dice from classification metrics (#2725)

Removed

• Changed minimum supported Pytorch version to 2.0 (#2671)
• Dropped support for Python 3.8 (#2827)
• Removed num_outputs in R2Score (#2800)

Fixed

• Fixed segmentation Dice + GeneralizedDice for 2d index tensors (#2832)
• Fixed mixed results of rouge_score with accumulate='best' (#2830)

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Key Contributors

@Borda, @cw-tan, @philgzl, @rittik9, @SkafteNicki

New Contributors since 1.5.0

• @bfolie made their first contribution in #2793
• @StalkerShurik made their first contribution in #2811
• @philgzl made their first contribution in #2792
• @cw-tan made their first contribution in #2754

Full Changelog: v1.5.0...v1.6.0

Minor patch release

[1.5.2] - 2024-11-07

Changed

• Re-adding numpy 2+ support (#2804)

Fixed

• Fixed iou scores in detection for either empty predictions/targets leading to wrong scores (#2805)
• Fixed MetricCollection compatibility with torch.jit.script (#2813)
• Fixed assert in PIT (#2811)
• Patched np.Inf for numpy 2.0+ (#2826)

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Key Contributors

@adamjstewart, @Borda, @SkafteNicki, @StalkerShurik, @yurithefury

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Full Changelog: v1.5.1...v1.5.2

Minor compatibility patch

[1.5.1] - 2024-10-22

Fixed

• Changing _modules dict type in Pytorch 2.5 preventing to fail collections metrics (#2793)

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Key Contributors

@bfolie

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Full Changelog: v1.5.0...v1.5.1

Shape metric

Shape metrics are quantitative methods used to assess and compare the geometric properties of objects, often in datasets that represent shapes. One such metric is the Procrustes Disparity, which measures the sum of the squared differences between two datasets after applying a Procrustes transformation. This transformation involves scaling, rotating, and translating the datasets to achieve optimal alignment. The Procrustes Disparity is particularly useful when comparing datasets that are similar in structure but not perfectly aligned, allowing for more meaningful comparison by minimizing differences due to orientation or size.

[1.5.0] - 2024-10-18

Added

• Added segmentation metric HausdorffDistance (#2122)
• Added audio metric DNSMOS (#2525)
• Added shape metric ProcrustesDistance (#2723)
• Added MetricInputTransformer wrapper (#2392)
• Added input_format argument to segmentation metrics (#2572)
• Added multi-output support for MAE metric (#2605)
• Added truncation argument to BERTScore (#2776)

Changed

• Tracker higher is better integration (#2649)
• Updated InfoLM class to dynamically set higher_is_better (#2674)

Deprecated

• Deprecated num_outputs in R2Score (#2705)

Fixed

• Fixed corner case in IoU metric for single empty prediction tensors (#2780)
• Fixed PSNR calculation for integer type input images (#2788)

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Key Contributors

@Astraightrain, @grahamannett, @lgienapp, @matsumotosan, @quancs, @SkafteNicki

New Contributors since 1.4.0

• @kalekundert made their first contribution in #2543
• @lgienapp made their first contribution in #2392
• @sweber1 made their first contribution in #2634
• @gxy-gxy made their first contribution in #2347
• @Astraightrain made their first contribution in #2605
• @ndrwrbgs made their first contribution in #2640
• @grahamannett made their first contribution in #2674
• @petertheprocess made their first contribution in #2721
• @rittik9 made their first contribution in #2726
• @vkinakh made their first contribution in #2698
• @likawind made their first contribution in #2732
• @veera-puthiran-14082 made their first contribution in #2753
• @GPPassos made their first contribution in #2727

Full Changelog: v1.4.0...v1.5.0

Minor patch release

[1.4.3] - 2024-10-10

Fixed

• Fixed for Pearson changes inputs (#2765)
• Fixed bug in PESQ metric where NoUtterancesError prevented calculating on a batch of data (#2753)
• Fixed corner case in MatthewsCorrCoef (#2743)

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Key Contributors

@Borda, @SkafteNicki, @veera-puthiran-14082

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Full Changelog: v1.4.2...v1.4.3