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hey guys this is shrini and in this video let's discuss the best way to evaluate semantic segmentation and obviously we're going to use intersection over union approach now i hope you know what semantic segmentation is if not you are wasting watching this video now just a quick reminder semantic segmentation by that we refer to classifying individual pixel rather than classifying an image as a cat or a dog but in this case we are classifying individual pixels that belong to a cat or a dog in this example i'm just showing you a rock sample showing different minerals in the rock but this is what semantic segmentation is now why what's wrong with accuracy right i mean we do scikit learn dot metrics and from metrics we normally import our accuracy that actually looks at our prediction and our ground truth and then gives us the accuracy but the problem is it uh it's not a great metric if you have a class imbalance which happens when you have multi-class problems in real life so inaccuracy...
