Remove Demanded Field from the IOU and eSign it in minutes

Aug 6th, 2022
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How to Remove Demanded Field from 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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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.
Alternatives to IoU Average Precision (AP) or mean Average Precision (mAP) are common alternatives, both of which are used to evaluate models such as Faster RCNN, Mask RCNN, and YOLO. AP is calculated for every single class, meaning the number of classes and AP values should be equal.
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.
The Intersection over Union can then be computed on Line 27 by dividing the intersection area by the union area of the two bounding boxes, taking care to subtract out the intersection area from the denominator (otherwise the intersection area would be doubly counted).
Note: 0.5 IoU is typically considered a good score, while 1 is perfect in theory.
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.
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.
Commonly, IoU 0.5 means that it was a hit, otherwise it was a fail. For each class, one can calculate the. True Positive (TP(c)): a proposal was made for class c and there actually was an object of class c. False Positive (FP(c)): a proposal was made for class c, but there is no object of class c.

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