## [1.8.2] - 2025-09-03 ### Fixed - Fixed `BinaryPrecisionRecallCurve` now returns `NaN` for precision when no predictions meet a threshold ([#3227](https://github.com/Lightning-AI/torchmetrics/pull/3227)) - Fixed `precision_at_fixed_recall` and `recall_at_fixed_precision` to correctly return `NaN` thresholds when recall/precision conditions are not met ([#3226](https://github.com/Lightning-AI/torchmetrics/pull/3226)) --- ### Key Contributors @iamkulbhushansingh _If we forgot someone due to not matching commit email with GitHub account, let us know :]_ --- **Full Changelog**: https://github.com/Lightning-AI/torchmetrics/compare/v1.8.1...v1.8.2

## [1.8.1] - 2025-08-07 ### Changed - Added `reduction='none'` to `vif` metric (#3196) - Float input support for segmentation metrics (#3198) ### Fixed - Fixed unintended `sigmoid` normalization in `BinaryPrecisionRecallCurve` (#3182) --- ### Key Contributors @iamkulbhushansingh, @PussyCat0700, @simonreise _If we forgot someone due to not matching commit email with GitHub account, let us know :]_ --- **Full Changelog**: https://github.com/Lightning-AI/torchmetrics/compare/v1.8.0...v1.8.1

The upcoming TorchMetrics v1.8.0 release introduces three flagship metrics, each designed to address critical evaluation needs in real-world applications. Video Multi-Method Assessment Fusion (VMAF) brings a perceptual video-quality score that closely mirrors human judgment, powering streaming services such as Netflix and YouTube to optimize encoding ladders for consistent viewer experiences and enabling video-restoration labs to quantify improvements achieved by denoising and super-resolution algorithms. Continuous Ranked Probability Score (CRPS) enables comprehensive evaluation of full predictive distributions rather than point estimates; meteorological centers leverage CRPS to benchmark probabilistic precipitation and temperature forecasts, improving public weather alerts, while energy companies apply it to assess uncertainty in load-demand predictions and refine grid management and trading strategies. Lip Vertex Error (LVE) measures the discrepancy between predicted and ground-truth lip landmarks to quantify audio-visual synchronization. Localization studios use LVE to validate lip-sync accuracy during film dubbing, while AR/VR developers integrate it into avatar pipelines to ensure natural mouth movements in real-time virtual meetings and social experiences. --- ## [1.8.0] - 2025-07-23 ### Added - Added `VMAF` metric to new video domain (#2991) - Added `CRPS` in regression domain (#3024) - Added `aggregation_level` argument to `DiceScore` (#3018) - Added support for `reduction="none"` to `LearnedPerceptualImagePatchSimilarity` (#3053) - Added support single `str` input for functional interface of `bert_score` (#3056) - Enhance: `BERTScore` to evaluate hypotheses against multiple references (#3069) - Added `Lip Vertex Error (LVE)` in multimodal domain (#3090) - Added `antialias` argument to `FID` metric (#3177) - Added `mixed` input format to segmentation metrics (#3176) ### Changed - Changed `data_range` argument in `PSNR` metric to be a required argument (#3178) ### Removed - Removed `zero_division` argument from `DiceScore` (#3018) --- ## Key Contributors @nkaenzig, @rittik9, @simonreise, @SkafteNicki ### New Contributors * @lantiga made their first contribution in https://github.com/Lightning-AI/torchmetrics/pull/3054 * @AlexVerine made their first contribution in https://github.com/Lightning-AI/torchmetrics/pull/3057 * @ZhiyuanChen made their first contribution in https://github.com/Lightning-AI/torchmetrics/pull/3059 * @ahmedhshahin made their first contribution in https://github.com/Lightning-AI/torchmetrics/pull/3101 * @gratus907 made their first contribution in https://github.com/Lightning-AI/torchmetrics/pull/3103 * @cyyever made their first contribution in https://github.com/Lightning-AI/torchmetrics/pull/3118 * @Armannas made their first contribution in https://github.com/Lightning-AI/torchmetrics/pull/3124 * @alifa98 made their first contribution in https://github.com/Lightning-AI/torchmetrics/pull/3128 * @simonreise made their first contribution in https://github.com/Lightning-AI/torchmetrics/pull/3176 _If we forgot someone due to not matching commit email with GitHub account, let us know :]_ --- **Full Changelog**: https://github.com/Lightning-AI/torchmetrics/compare/v1.7.0...v1.8.0

## [1.7.3] - 2025-06-13 ### Fixed - Fixed: ensure `WrapperMetric` resets `wrapped_metric` state (#3123) - Fixed `top_k` in `multiclass_accuracy` (#3117) - Fixed compatibility to COCO format for `pycocotools` 2.0.10 (#3131) --- ### Key Contributors @rittik9 _If we forgot someone due to not matching commit email with GitHub account, let us know :]_ --- **Full Changelog**: https://github.com/Lightning-AI/torchmetrics/compare/v1.7.2...v1.7.3

## [1.7.2] - 2025-05-27 ### Changed - Enhance: improve performance of `_rank_data` (#3103) ### Fixed - Fixed `UnboundLocalError` in `MatthewsCorrCoef` (#3059) - Fixed MIFID incorrectly converts inputs to `byte` dtype with custom encoders (#3064) - Fixed `ignore_index` in `MultilabelExactMatch` (#3085) - Fixed: disable non-blocking on MPS (#3101) --- ### Key Contributors @ahmedhshahin, @gratus907, @rittik9, @ZhiyuanChen _If we forgot someone due to not matching commit email with GitHub account, let us know :]_ --- **Full Changelog**: https://github.com/Lightning-AI/torchmetrics/compare/v1.7.1...v1.7.2

## [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) --- ### Key Contributors @Isalia20, @rittik9, @SkafteNicki _If we forgot someone due to not matching commit email with GitHub account, let us know :]_ --- **Full Changelog**: https://github.com/Lightning-AI/torchmetrics/compare/v1.7.0...v1.7.1

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) --- ### Key Contributors @Isalia20, @LorenzoAgnolucci, @nathanpainchaud, @rittik9, @SkafteNicki _If we forgot someone due to not matching commit email with GitHub account, let us know :]_ --- **Full Changelog**: https://github.com/Lightning-AI/torchmetrics/compare/v1.6.0...v1.7.0

## [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) --- ### Key Contributors @SkafteNicki _If we forgot someone due to not matching commit email with GitHub account, let us know :]_ --- **Full Changelog**: https://github.com/Lightning-AI/torchmetrics/compare/v1.6.2...v1.6.3