Bold pattern in the IOU

Aug 6th, 2022
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How to bold pattern in the IOU

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in todays video were going to be going over one of the most controversial yet easiest to understand design patterns the singleton design pattern were going to cover what it is when you should use it when you shouldnt use it and were going to go over some code that is not using the singleton design pattern and then were going to refactor to use the singleton design pattern like it should be before we start refactoring any code to use the singleton pattern lets first talk about what it is in essence the singleton pattern is just a way of creating a single object that is shared amongst a bunch of different resources throughout your application without having to recreate that object or losing the information inside of it all of the state for that object the variables methods all of those are shared among all these different other objects that are using it and theres just one source of information for this object and theres only ever a single type of this object created this is why

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Interpretation of IoU Values IoU value lies in the range [0, 1] where 0 means no overlap between two boxes being checked and 1 indicates a perfect overlap. Based on your application, you can set an IoU threshold to determine what good detection means.
Acceptable IoU values are typically above 0.5, while good IoU values are above 0.7. However, these thresholds may vary depending on the application and task.
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. For binary (two classes) or multi-class segmentation, the mean IoU of the image is calculated by taking the IoU of each class and averaging them.
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.
The IoU score will be high if there is much overlap between the anticipated and ground truth boxes. In contrast, a low overlap will result in a low IoU score. An IoU score of 1 indicates a perfect match between the projected box and the ground truth box, whereas a score of 0 means no overlapping between the boxes.
The IoU value ranges from 0 to 1, where 0 indicates no overlap between the boxes, and 1 represents complete overlap or identical regions. Higher IoU values indicate greater similarity or agreement between the two bounding boxes.
Intersection over Union calculation example In such a case: Area of Overlap: 5 * 5 = 25; Area of Union: Area of the Red Bounding Box + Area of the Green Bounding Box - Area of Overlap = 10 10 + 10 10 - 25 = 175. IoU: Area of Overlap / Area of Union = 25 / 175 ~ 0.14 - poor IoU.
IoU measures the overlap between predicted bounding boxes and ground truth boxes, with scores ranging from 0 to 1. The IoU metric is essential for comparing and evaluating the performance of detection models.

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