Simply put, the IoU is the area of overlap between the predicted segmentation and the ground truth divided by the area of union between the predicted segmentation and the ground truth, as shown on the image to the left. Source: Wikipediaīefore reading the following statement, take a look at the image to the left. The IoU is a very straightforward metric that’s extremely effective. The Intersection-Over-Union (IoU), also known as the Jaccard Index, is one of the most commonly used metrics in semantic segmentation… and for good reason. Intersection-Over-Union (IoU, Jaccard Index) Therefore, I present to you two alternative metrics that are better at dealing with this issue: 2. Unfortunately, class imbalance is prevalent in many real world data sets, so it can’t be ignored. When our classes are extremely imbalanced, it means that a class or some classes dominate the image, while some other classes make up only a small portion of the image. This is meant to illustrate that high pixel accuracy doesn’t always imply superior segmentation ability. As a result, although your accuracy is a whopping 95%, your model is returning a completely useless prediction. So if the model classifies all pixels as that class, 95% of pixels are classified accurately while the other 5% are not. It’s just that one class was 95% of the original image. ![]() Is there something wrong with our calculation? Nope. Not exactly what you were hoping for, huh.
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