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In this video, Shrini discusses the evaluation of semantic segmentation using the intersection over union (IoU) approach. Semantic segmentation involves classifying individual pixels within an image rather than classifying the entire image, as illustrated with a rock sample containing different minerals. He highlights that traditional accuracy metrics from tools like Scikit-learn are inadequate for evaluating models with class imbalances, which are common in multi-class problems. The video emphasizes the limitations of accuracy as a metric in these scenarios, advocating for the IoU method to provide a better evaluation of segmentation performance.