This repository provides the implementation for Deep Imbalanced Regression (DIR), a framework that tackles learning from imbalanced data with continuous target values. It addresses missing data in certain target regions and generalizes to the full target range. The authors curate and benchmark large-scale DIR datasets including age prediction, text similarity scoring, health condition scoring, and depth estimation. The code supports PyTorch-based training and includes a Google Colab tutorial.