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Thank you for your work. I have a question regarding the demo / the functionality of the cal_giou and cal_giou_3d functions.
It looks like from the demo the inputs must be the same size. that is, [Batch, Number of boxes, 7] for the 3D case.
However, if we have say 2 ground truth boxes in an image, and our model predicts 100 bounding boxes [B, 100,7], is there a way to compute the IoU between each of the boxes to the 2 ground truth boxes? [B,100,7] and [B,2,7] comparison.
--> meaning every predicted box from the 100 is compared to each of the 2 ground truth boxes. thus for any batch, there are 200 comparisons made
Thanks for your time
The text was updated successfully, but these errors were encountered:
Hello,
Thank you for your work. I have a question regarding the demo / the functionality of the
cal_giou
andcal_giou_3d
functions.It looks like from the demo the inputs must be the same size. that is, [Batch, Number of boxes, 7] for the 3D case.
However, if we have say 2 ground truth boxes in an image, and our model predicts 100 bounding boxes [B, 100,7], is there a way to compute the IoU between each of the boxes to the 2 ground truth boxes? [B,100,7] and [B,2,7] comparison.
--> meaning every predicted box from the 100 is compared to each of the 2 ground truth boxes. thus for any batch, there are 200 comparisons made
Thanks for your time
The text was updated successfully, but these errors were encountered: