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In this tutorial, Shrini discusses the optimal way to evaluate semantic segmentation using the Intersection over Union (IoU) approach. He defines semantic segmentation as the process of classifying individual pixels within an image instead of labeling the entire image (e.g., identifying pixels belonging to a cat or dog). Shrini highlights the limitations of using accuracy as a metric for semantic segmentation, especially in cases of class imbalance common in multi-class problems. He emphasizes that traditional accuracy metrics may not effectively represent performance in these scenarios.
