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Hi, one of the evaluation metric for zero shot object detection is Recall@100, but how to compute it is not very clear.
My understanding of computation process is following.
First, select top 100 detections from an image.
Second, mark a predicted bounding box as positive if it has an IoU greater than a threshold (0.5 for example) and no other higher confidence bounding box has been assigned to the same GT box.
Third, compute recall@100 for this image number_of_positive_prediction / 100.
Forth, compute recall@100 for all images sum(recall@100 for each image) / number_of_image.
Is it correct ? Thanks!
The text was updated successfully, but these errors were encountered:
Hi, one of the evaluation metric for zero shot object detection is Recall@100, but how to compute it is not very clear.
My understanding of computation process is following.
First, select top 100 detections from an image.
Second, mark a predicted bounding box as positive if it has an IoU greater than a threshold (0.5 for example) and no other higher confidence bounding box has been assigned to the same GT box.
Third, compute recall@100 for this image
number_of_positive_prediction / 100
.Forth, compute recall@100 for all images
sum(recall@100 for each image) / number_of_image
.Is it correct ? Thanks!
The text was updated successfully, but these errors were encountered: