Copy code in the IOU

Aug 6th, 2022
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How to copy code in the IOU

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hello everyone so in this video we are going to look at IOU or intersection over Union so as you can see over here intersection of Union over Union can be said as this is intersection and this is Union so basically you will be having a ground truth box which is shown in green over here so lets say you are doing an object detection and you get this you have this uh green box as a ground truth box in the training set and then these red ones are your predictions okay so these are your predicted truth boxes so what is intersection or IOU is intersection over Union that is this is intersection and this is the whole thing is Union okay so you can see the areas so obviously this can be many different formats so what is a good intersection over Union bad or okay so anything that is above 0.5 is considered okay so as you can see over here the intersection over Union here is quite large compared to these two but in the second one you can see if the intersection is very very low so basically wha

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MeanIoU class Computes the mean Intersection-Over-Union metric. Formula: iou = truepositives / (truepositives + falsepositives + falsenegatives) Intersection-Over-Union is a common evaluation metric for semantic image segmentation.
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.
Intersection Over Union (IOU) is a crucial metric in object detection. It measures the overlap between two bounding boxes: the ground truth (the actual object location) and the predicted bounding box. IOU is a key metric for evaluating the accuracy of object detection models.
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.
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.
There is no one-size-fits-all recommended threshold for IoU, as it largely depends on the specific object detection task and dataset. However, a common threshold used in practice is 0.5, meaning that a predicted box must have an IoU of at least 0.5 with a ground truth box to be considered a true positive detection.
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.

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