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How to Correct IOU

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hey guys this is shrini and in this video lets discuss the best way to evaluate semantic segmentation and obviously were 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 im just showing you a rock sample showing different minerals in the rock but this is what semantic segmentation is now why whats 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 its not a great metric if you have a class imbalance which happens when you have multi-class problems in real life so inaccuracy

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What does IoU mean?

An IOU, a phonetic acronym of the words I owe you, is a document that acknowledges the existence of a debt. An IOU is often viewed as an informal written agreement rather than a legally binding commitment.

What is a good IoU?

Note: 0.5 IoU is typically considered a good score, while 1 is perfect in theory.

How do you calculate IoU in semantic segmentation?

Intersection-Over-Union is a common evaluation metric for semantic image segmentation. To compute IoUs, the predictions are accumulated in a confusion matrix, weighted by sampleweight and the metric is then calculated from it. If sampleweight is None , weights default to 1. Use sampleweight of 0 to mask values.

Why is IoU 0 and 1?

In case there are 2 boxes that do not intersect, the area of their intersection would be 0, and therefore the IOU would also be 0. In case there are 2 boxes that completely overlap, the area of the intersection would be equal to the area of their union, and therefore the IOU would be 1.

How to calculate IoU?

IoU is calculated by dividing the overlap between the predicted and ground truth annotation by the union of these.

How do you calculate IoU between boxes?

Youre calculating the area of the intersection of the two boxes. And dividing by the area of the union of the two boxes. Well, go look at the Jaccard index (IoU) formula. The correct Jaccard Index formula is: iou = intersectionarea / (unionarea - intersectionarea) .

What is a good IoU score?

Note: 0.5 IoU is typically considered a good score, while 1 is perfect in theory. No matter where you get the ground-truth data or how carefully you label, its extremely unlikely to have the predicted output match the ground-truth bounding box coordinates.

What is IoU in math?

IOU(Intersection over Union) is a term used to describe the extent of overlap of two boxes. The greater the region of overlap, the greater the IOU.

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