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Aug 6th, 2022
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How to Finish result in the IOU

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[Music] hello welcome to a lightboard series in this video we are going to do image segmentation and the loss functions for it so lets see what is image segmentation its one of the most fundamental problems in computer vision but its basically the same as classification problem which means trying to determine the class of pixels so let me demonstrate what we want to do so given an image we have to we wanted to determine or detect an object inside lets say we want to detect an animal lets say it looks like a cat and we want to detect this animal but the problem is usually the image is very high-dimensional it has like around ten thousand three pixels and we want to classify each pixel in this image we want to classify whether it is a background pixel or if its a foreground pixel foreground pixels are the pixels belonging to the class we are trying to detect so by nature usually these problems are highly unbalanced so these classification problems are unbalanced because most of th

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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.
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.
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.
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. IOU is mainly used in applications related to object detection, where we train a model to output a box that fits perfectly around an object.
IoU is used to evaluate the object detection algorithm. It is the overlap between the ground truth and the predicted bounding box, i.e it calculates how similar the predicted box is with respect to the ground truth.
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 is an evaluation metric used to measure the accuracy of an object detector on a particular dataset. We often see this evaluation metric used in object detection challenges such as the popular PASCAL VOC challenge.
Intersection over Union (IoU) is used when calculating mAP. It is a number from 0 to 1 that specifies the amount of overlap between the predicted and ground truth bounding box. IoU is an important accuracy measure to track when gathering human annotations.

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