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In this video, Shrini discusses the best method for evaluating semantic segmentation, focusing on the Intersection over Union (IoU) approach. Semantic segmentation involves classifying individual pixels in an image rather than just labeling the entire image (e.g., identifying which pixels belong to a cat or dog). Shrini highlights a common example involving rock samples with different minerals. He points out that using accuracy as a metric can be misleading, especially in cases of class imbalance, which often occurs in multi-class problems. Instead, he advocates for using IoU to provide a more reliable evaluation of semantic segmentation performance.