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In this video, Shrini discusses the best method to evaluate semantic segmentation using the Intersection over Union (IoU) approach. Semantic segmentation involves classifying individual pixels of an image rather than labeling the entire image (e.g., identifying pixels that belong to a cat or dog). Shrini emphasizes that accuracy, a common metric used in machine learning, may not be an effective measure for semantic segmentation due to issues like class imbalance that often arise in multi-class problems. Instead, he advocates for using IoU as a more reliable evaluation metric.
