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In this video, Shrini discusses the best approach to evaluate semantic segmentation, focusing on the Intersection over Union (IoU) method. Semantic segmentation involves classifying individual pixels in an image, such as identifying whether each pixel belongs to a cat or dog, rather than classifying the whole image. Shrini presents an example using a rock sample with different minerals to illustrate this concept. He explains that traditional accuracy metrics can be misleading, especially in cases of class imbalance, which often occurs in multi-class problems in real-life scenarios. Thus, accuracy is not an ideal metric for evaluating semantic segmentation tasks.
