TorchMetrics
TorchMetrics offers over 100 implementations of metrics for PyTorch, featuring a user-friendly API that simplifies the creation of custom metrics. It enhances reproducibility through a standardized interface, supports distributed training, and ensures automatic batch accumulation and device synchronization. Users can easily integrate it into their PyTorch and PyTorch Lightning models, streamlining metric logging and reducing boilerplate code.
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Top TorchMetrics Features
- Standardized metric interface
- Custom metric creation
- Supports distributed training
- Automatic batch accumulation
- Multi-device synchronization
- Easy integration with PyTorch
- Direct logging in Lightning
- Class-based and functional metrics
- Extensive metrics library
- Multimodal data handling
- Support for various data types
- Rigorously tested implementations
- Reduces code boilerplate
- Quickstart guide available
- Comprehensive API documentation
- Community support and resources
- Built-in performance tracking
- Flexible metric filtering
- Compatible with pip and conda
- User-friendly for developers.
