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In this video, Shrini discusses the best way to evaluate semantic segmentation through the intersection over union (IoU) approach. He explains that semantic segmentation involves classifying individual pixels rather than labeling whole images, such as identifying the parts of an image that belong to a cat or a dog. Shrini emphasizes that traditional accuracy metrics can be misleading, especially in cases of class imbalance that often occur in multi-class problems. He illustrates the concept using an example of a rock sample with different minerals, highlighting the limitations of accuracy as a performance metric in these scenarios.