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In this video, Shrini discusses the best way to evaluate semantic segmentation using the Intersection over Union (IoU) approach. He explains that semantic segmentation involves classifying individual pixels rather than labeling an entire image, illustrating this with an example of mineral classification in a rock. Shrini raises concerns about using accuracy as a metric for evaluation, particularly in the context of class imbalance, which is common in multi-class problems. He emphasizes that while accuracy can be imported and calculated through traditional methods, it may not be a reliable measure in cases of class imbalance encountered in real-world scenarios.
