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In this video tutorial, Shrini explains that the best way to evaluate semantic segmentation is through the intersection over union approach. Semantic segmentation involves classifying individual pixels rather than entire images, as demonstrated with a rock sample showing different minerals. The problem with using accuracy as a metric for semantic segmentation is that it does not work well with class imbalances, which often occur in real-life multi-class problems. So, using intersection over union is a more robust approach for evaluating semantic segmentation accuracy.
