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In this video, Shrini discusses the best method for evaluating semantic segmentation using the intersection over union (IoU) approach. Semantic segmentation involves classifying individual pixels in an image, rather than classifying the entire image. For example, it identifies which pixels belong to a cat or a dog, rather than just labeling the image itself. Shrini highlights a common issue with accuracy as a metric in evaluating predictions, particularly in cases of class imbalance often seen in multi-class scenarios. He emphasizes that accuracy can be misleading in these situations, signaling the need for more reliable evaluation methods like IoU.
