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In this video, Shrini discusses the best way to evaluate semantic segmentation, focusing on the intersection over union (IoU) approach. He explains that semantic segmentation involves classifying individual pixels instead of an entire image, using an example of different minerals in a rock. Shrini highlights the limitations of using accuracy as a metric in evaluations, particularly in cases of class imbalance that can occur with multi-class problems. He emphasizes that accuracy may not provide a true representation of performance in these situations, making IoU a more reliable metric for assessing semantic segmentation results.