7:10 The IoU is not the amount of overlap between the two boxes, it's "Intersection over Union", so the area of overlap / area of union, its the proportion, whereas the intersection alone is the overlap value.
AP (of a single class) is caculated for a fixed IoU, right? Because a P-R value is dependent on confidence and IoU (two factors). By computing the P-R curve, only confidence is changed (IoU is fixed).
1. F1 score is harmonic mean of precision and recall, and just not simply the result of their multiplication. 2. 9:20 you totally failed to clarify things. So what mAP is: a. is it average of AP at different IoUs of a single class b. or average of AP across different classes but then what happened to AP at different IoUs Overall it is informative. But would be better if you can just clarify things a bit more..
AP is calculated using a single IoU, as the mean of precisions achieved at each recall level (different detection thresholds). As AP is calculated for each class, mAP (mean average precision) is calculated as the mean value of average precisions. AP and mAP depend on the selected IoU, and are thus called by its IoU (mAP50, mAP75, etc.)
@@ankitmagan Confidence Value (confidence score) is the probability of the object present in a particular anchor box. Its mostly coming from the classifier. We are talking about IoU. Its overlap/union ratio between the predicted and ground truth(actual) bounding box that we have in our labelled dataset. We can calculate mAP when we have labelled test dataset and we predict boxes and compare how precise bounding boxes are generated with respect to ground truth boxes.
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Thanks ALL FOR instruction
7:10
The IoU is not the amount of overlap between the two boxes, it's "Intersection over Union", so the area of overlap / area of union, its the proportion, whereas the intersection alone is the overlap value.
Best video in this topic
AP (of a single class) is caculated for a fixed IoU, right? Because a P-R value is dependent on confidence and IoU (two factors). By computing the P-R curve, only confidence is changed (IoU is fixed).
Great content!
1. F1 score is harmonic mean of precision and recall, and just not simply the result of their multiplication.
2. 9:20 you totally failed to clarify things. So what mAP is:
a. is it average of AP at different IoUs of a single class
b. or average of AP across different classes
but then what happened to AP at different IoUs
Overall it is informative. But would be better if you can just clarify things a bit more..
I found the same thing on their blog post. Doesn't actually answer the title of the video.
AP is calculated using a single IoU, as the mean of precisions achieved at each recall level (different detection thresholds).
As AP is calculated for each class, mAP (mean average precision) is calculated as the mean value of average precisions.
AP and mAP depend on the selected IoU, and are thus called by its IoU (mAP50, mAP75, etc.)
@@alejandromarceloproiettian5079 You mention different detection thresholds. Is this the confidence value that the model outputs?
@@ankitmagan Confidence Value (confidence score) is the probability of the object present in a particular anchor box. Its mostly coming from the classifier.
We are talking about IoU. Its overlap/union ratio between the predicted and ground truth(actual) bounding box that we have in our labelled dataset. We can calculate mAP when we have labelled test dataset and we predict boxes and compare how precise bounding boxes are generated with respect to ground truth boxes.
Thank you very much
You welcome 🙏🏻
Nice work. Thanks!
Amazingly explained!
Glad it was helpful!
What is that plot with confidence as y-axis at 4.18 its super confusing
Can I get the link to the paper that introduced mAP?
Can I get the code to calculate them?
use tensorflow for that
@@legohistory isn't calculated directly inside google colab algorithm folders?
@@abbasalsiweedi9019 I do not understand. What do you mean?