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The library is a collection of the most used metrics within deep learning in pytorch. The library is hugely inspiret by ignites.Metric module, but is intented to be a standalon library for only computing metrics on pytorch tensor. The goal is to get as many of the sklearn.metrics ported to pytorch.

Availble metrics:

• Regression metrics

  • MeanSquaredError
  • MeanAbsoluteError
  • RootMeanSquaredError
  • ExplainedVariance
  • R2Score
  • MaxError
  • MeanSquaredLogarithmicError
  • MeanTweedieDeviance
  • MeanPoissonDeviance
  • MeanGammaDeviance
  • CosineSimilarity
  • Correlation

• Classification metrics

  • Accuracy
  • BalancedAccuracy
  • FilteredAccuracy
  • Recall
  • Precision
  • BalancedAccuracy
  • F1
  • TopKAccuracy
  • ROC
  • AUC
  • ConfusionMatrix

The library have a number of wrappers that (as the name suggest) can be use in combination with the selection of metrics to offer extra functionality:

• MetricCollection
• RunningAverage
• Sum, Mean, Product

Please see the docs for more information.