In computer vision, scale imbalance refers to a challenge where objects of interest in images have dramatically different sizes (scales), and smaller objects are underrepresented or harder to detect compared to larger ones​.For example, in an aerial image, a car (small) and a building (large) differ greatly in scale; a model might bias toward detecting large objects while missing small ones. This imbalance can hurt model performance, as standard training may not sufficiently learn features for tiny objects​Addressing scale imbalance can involve techniques like multi-scale training and inference (e.g., using image pyramids or Feature Pyramid Networks), data augmentation by rescaling objects, or specialized network architectures that preserve detail for small objects. Ensuring the model sees objects at various scales during training helps it perform well across the spectrum of object sizes.