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In this video, Shrini discusses how to evaluate semantic segmentation using the intersection over union (IoU) method. Semantic segmentation involves classifying individual pixels in an image, rather than categorizing the entire image as a single entity (e.g., cat or dog). For example, it can classify pixels that represent different minerals in a rock sample. Shrini points out that using overall accuracy as a metric can be misleading, particularly in cases of class imbalance, which is common in multi-class problems. Thus, he emphasizes the importance of alternative metrics like IoU for more effective evaluation in these scenarios.
