TorchMetrics
The facts below are what the maker publishes about TorchMetrics, checked and dated. Our editors have not written about it yet.
Plans and prices
From TorchMetrics's own pricing page, checked 11 Oct 2026.
| Plan | Price | What it gives you |
|---|---|---|
| TorchMetrics | Free | 100+ PyTorch metrics; Apache 2.0 license |
The facts
Each one read from TorchMetrics's own pages, with the date it was checked.
What it does
- Batch accumulation
- Metrics can accumulate results across batches and compute an aggregate value.checked 11 Oct 2026
- Bayesian analysis
- Nochecked 23 Sep 2026
- Community support
- The project repository directs users with questions to its Discord community.checked 11 Oct 2026
- Compatibility limit
- The documentation says TorchMetrics supports the latest two major PyTorch Lightning versions and recommends keeping both frameworks up to date.checked 11 Oct 2026
- Custom metrics
- Users can create custom metrics by subclassing Metric and implementing update() and compute().checked 11 Oct 2026
- Distributed training
- Metrics support distributed training and synchronize state across devices.checked 11 Oct 2026
- Installation
- The project documents installation with pip, conda, uv, or directly from its source repository.checked 11 Oct 2026
- Interfaces
- TorchMetrics provides module-based metrics and functional metrics that take tensors as input and return metric values as tensors.checked 11 Oct 2026
- License
- The project repository states that TorchMetrics is licensed under Apache 2.0.checked 11 Oct 2026
- Lightning integration
- When used with PyTorch Lightning, metrics can be logged with self.log and are automatically placed on the correct device when properly defined inside a LightningModule.checked 11 Oct 2026
- Metric collections
- MetricCollection wraps multiple metrics in one callable metric class.checked 11 Oct 2026
- Metric coverage
- The project lists more than 100 metrics across audio, classification, detection, information retrieval, image, multimodal, nominal, regression, segmentation, and text domains.checked 11 Oct 2026
- Optional dependencies
- Specialized audio, image, and text metrics may require extra dependencies, which can be installed with package extras.checked 11 Oct 2026
- Plotting
- Modular metrics provide a plot method, and the plotting interface requires matplotlib.checked 11 Oct 2026
- Purpose
- TorchMetrics is a collection of more than 100 PyTorch metrics implementations with an API for creating custom metrics.checked 11 Oct 2026
- PyTorch integration
- TorchMetrics can be used directly with PyTorch models independently of PyTorch Lightning.checked 11 Oct 2026
- PyTorch Lightning integration
- When metrics are properly defined inside a LightningModule, Lightning can place them on the module’s device and log them with self.log or self.log_dict.checked 11 Oct 2026
- Regression models
- Nochecked 23 Sep 2026
- Scripting support
- Yeschecked 23 Sep 2026
- Standardized interface
- Its standardized interface is intended to improve reproducibility and reduce boilerplate.checked 11 Oct 2026
- Statistical tests
- Nochecked 23 Sep 2026
- Support
- The project directs users to its Discord community for help and questions.checked 11 Oct 2026
- Supported metric areas
- The documentation organizes metrics by areas including classification, regression, image, audio, and text.checked 11 Oct 2026
Money
- Free plan
- Yeschecked 23 Sep 2026
