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In this video tutorial, Shrini discusses the best method for evaluating semantic segmentation using the intersection over union (IoU) approach. Semantic segmentation involves classifying individual pixels within an image, as opposed to classifying the entire image. For example, distinguishing pixels belonging to a cat or dog, or identifying different minerals in a rock sample. Shrini highlights the limitations of using accuracy as a metric, particularly in cases of class imbalance common in multi-class scenarios. He emphasizes that relying solely on accuracy can be misleading when evaluating performance in semantic segmentation tasks.
