This repository provides an ImbalancedDatasetSampler for PyTorch that automatically estimates sampling weights to counter class imbalance without requiring a new balanced dataset. It helps prevent model bias toward over-represented classes by adjusting how samples are drawn from the training data. When combined with data augmentation, it also helps mitigate overfitting from naive duplication strategies.