Transform your daily workflows and Extract Data IOU

Aug 6th, 2022
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Simple instructions on how to Extract Data IOU

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How to Extract Data 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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Interpreting IoU scores IoU is quite intuitive to interpret. A score of 1 means that the predicted bounding box precisely matches the ground truth bounding box. A score of 0 means that the predicted and true bounding box do not overlap at all.
you have to calculate tp/(tp + fp + fn) over all images in your test set. That means you sum up tp, fp, fn over all images in your test set for each class and after that you do calculate the IoU. Taking the average of each individual image IoU results in a wrong global IoU.
To define the term, in Machine Learning, IoU means Intersection over Union - a metric used to evaluate Deep Learning algorithms by estimating how well a predicted mask or bounding box matches the ground truth data.
Recall is basically the rate of recognizing the specified class (lets consider a single class problem) and Accuracy is the rate of missing the specified class (well, the opposite). IoU basically takes both measures into account, how come this IoU rate is that low compared to the above?
Intersection Over Union (IoU) is a number that quantifies the degree of overlap between two boxes. In the case of object detection and segmentation, IoU evaluates the overlap of the Ground Truth and Prediction region.
To define the term, in Machine Learning, IoU means Intersection over Union - a metric used to evaluate Deep Learning algorithms by estimating how well a predicted mask or bounding box matches the ground truth data.
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. Dating as far back as the 18th century, at least, IOUs are still very much in use.
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.

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