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Intersection over Union (IoU) | Precision [%] | Recall [%] | Average Precision (AP) | Number of False Positives (#FP) | |||||||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
0.00 | - | 0.00 | - | 0 | |||||||||||||
Meaning | without predicted instances, there is no intersecting areainstance | without predicted instances, the Precision value cannot be estimated | without predicted instances, the number of true-positive
all annotated instances are false-negative
| without a defined value for Precision, the AP curve cannot be generated | without predicted instances, the number of false-positive instances is per definition zero |
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Add more images/ROIs containing annotated and labeled areas instances of the labels that were poorly recognized to your training set.
If the annotations are correct and complete, include new images/ROIs containing annotated and labeled areas instances in the training set.
Check if all areas instances in your validation images/ROIs were annotated and labeled correctly. If not, annotate and label all missing areas instances and correct any inaccuracies in your validation set.
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Intersection over Union (IoU) | Precision [%] | Recall [%] | Average Precision (AP) | Number of False Positives (#FP) | |||||||||||||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
0.00 | 0.00 | - | - | n | |||||||||||||||||||
Meaning | without annotated instances, there is no intersecting areainstance | without annotated instances, there is no true-positive
all predicted instances are false-positive
| without annotated instances, the Recall value cannot be estimated | without a defined value for Recall, the AP curve cannot be generated | there are n (n - number)predictions in the image or the ROI, but none of them corresponds to annotations, so all of them are false-positive
|
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This is a false-positive prediction by your trained application, meaning it predicts areas instances that shouldn’t be predicted.
If the Precision value is greater than 0.00 but still unexpectedly low, your trained application seems to predict areas instances in your validation images that have not been annotated.
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the app has made false-positive predictions, or
not all areas instances are annotated and labeled on your validation images.
What can you do?
Check if all areas instances in your validation images/ROIs were annotated and labeled correctly:
If not, annotate and label all missing areas instances and correct any inaccuracies in your validation set.
If yes, add background images/ROIs to the training set.
Add images/ROIs containing areas instances with a similar appearance as the false-positive predictions to the training set.
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