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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, unlike traditional image classification which categorizes an entire image. For example, in a rock sample, different minerals are classified at the pixel level. Shrini highlights that using accuracy as a metric can be misleading, especially in cases of class imbalance that often occur in multi-class scenarios. Thus, the focus is on why IoU is a more effective metric compared to traditional accuracy measures.