TorchMetrics Description

TorchMetrics comprises over 90 implementations of metrics designed for PyTorch, along with a user-friendly API that allows for the creation of custom metrics. It provides a consistent interface that enhances reproducibility while minimizing redundant code. The library is suitable for distributed training and has undergone thorough testing to ensure reliability. It features automatic batch accumulation and seamless synchronization across multiple devices. You can integrate TorchMetrics into any PyTorch model or utilize it within PyTorch Lightning for added advantages, ensuring that your data aligns with the same device as your metrics at all times. Additionally, you can directly log Metric objects in Lightning, further reducing boilerplate code. Much like torch.nn, the majority of metrics are available in both class-based and functional formats. The functional versions consist of straightforward Python functions that accept torch.tensors as inputs and yield the corresponding metric as a torch.tensor output. Virtually all functional metrics come with an equivalent class-based metric, providing users with flexible options for implementation. This versatility allows developers to choose the approach that best fits their coding style and project requirements.

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Pricing

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Free
Free Version:
Yes

Integrations

API:
Yes, TorchMetrics has an API
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Company Details

Company:
TorchMetrics
Headquarters:
United States
Website:
torchmetrics.readthedocs.io/en/stable/

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Training Docs
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Online Support

TorchMetrics Features and Options

Application Development Software

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Compatibility Testing
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Mobile Development
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