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In this tutorial, Shrini discusses the best way to evaluate semantic segmentation using the Intersection Over Union (IoU) approach. Semantic segmentation involves classifying individual pixels rather than classifying an entire image. For example, distinguishing pixels belonging to a cat or dog. Shrini highlights an important aspect of accuracy; while it can be calculated using scikit-learn metrics, it is inadequate for evaluating models with class imbalance, which is common in multi-class problems. This makes accuracy not a reliable metric for semantic segmentation, prompting the need for more suitable evaluation methods like IoU.
