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Welcome to TorchMetrics
=======================

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TorchMetrics is a collection of 100+ PyTorch metrics implementations and an easy-to-use API to create custom metrics. It offers:

* A standardized interface to increase reproducibility
* Reduces Boilerplate
* Distributed-training compatible
* Rigorously tested
* Automatic accumulation over batches
* Automatic synchronization between multiple devices

You can use TorchMetrics in any PyTorch model, or within `PyTorch Lightning <https://lightning.ai/docs/pytorch/stable/>`_ to enjoy the following additional benefits:

* Your data will always be placed on the same device as your metrics
* You can log :class:`~torchmetrics.Metric` objects directly in Lightning to reduce even more boilerplate

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Install TorchMetrics
--------------------

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For pip users

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    pip install torchmetrics

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Or directly from conda

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    conda install -c conda-forge torchmetrics

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   :description: Use this quickstart guide to learn key concepts.
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   :description: Easily use TorchMetrics in your PyTorch Lightning code.
   :header: TorchMetrics with PyTorch Lightning
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   :description: View the full list of metrics and filter by task and data type.
   :header: Metrics
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   :description: A detailed overview of the TorchMetrics API and concepts.
   :header: Overview
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   :description: Learn how to implement a custom metric with TorchMetrics.
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.. toctree::
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   pages/quickstart
   all-metrics
   gallery/index
   pages/overview
   pages/plotting
   pages/implement
   pages/lightning

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   :name: aggregation
   :caption: Aggregation
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   :name: metrics
   :caption: API Reference

   references/metric
   references/utilities

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   governance
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   generated/CONTRIBUTING.md
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Indices and tables
==================

* :ref:`genindex`
* :ref:`modindex`
* :ref:`search`