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How to Remove Value Choice into the IOU

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all right i was having some screen tearing issues but i think were good now so let me pull that a little bit closer all right welcome to this unofficial part two uh of this object detection series so uh what were gonna try to understand in this video is how to evaluate a bounding box prediction so you know we have some target bounding box for an object and we have some predicted bounding box and we want to have a way of quantifying or measure how good is our predicted bounding box for that object and for that were going to learn about a metric called intersection over union and then were also going to implement that in pytorch so thatll be fun so without further ado lets get started lets roll that intro and then lets get started with intersection over [Music] union so the question is how do we measure how good a bounding box is so we have an image with in this case a car in it and we are given a target bounding box for that object and then we have some prediction bounding box f

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How accurate is object detection?

The accuracy of an object detection model depends on the quality and number of training samples, the input imagery, the model parameters, and the requirement threshold for accuracy.

Why do we use IoU?

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.

Whats 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 the IoU metric for segmentation?

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.

Is IoU a good metric?

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. The IoU is a very straightforward metric thats extremely effective.

What is IoU metrics?

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.

What is the threshold for IoU?

IoU = Area of INTERSECTION /Area of UNION. IoU score 0.5 is considered as good.

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