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.
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 imagessum(recall@100 for each image) / number_of_image.Is it correct ? Thanks!