Comments (4)
I recently study the tutorial, I have the problem too. I don't know whether you solve or not. when you encounter the problem, you print the tensor 1 and tensor 2, and then you will find the tensor 1 is tensor([0]). It is that some pictures have no gound_truth boxes. You should filter them out in the function of create_data_lists in utils.py. I changed the the author's code len(object) == 0 to len(object["boxes"] == 0 and it works. Hope, this can help.
from a-pytorch-tutorial-to-object-detection.
Me too. I need help!
from a-pytorch-tutorial-to-object-detection.
I recently study the tutorial, I have the problem too. I don't know whether you solve or not. when you encounter the problem, you print the tensor 1 and tensor 2, and then you will find the tensor 1 is tensor([0]). It is that some pictures have no gound_truth boxes. You should filter them out in the function of create_data_lists in utils.py. I changed the the author's code len(object) == 0 to len(object["boxes"] == 0 and it works. Hope, this can help.
THX
from a-pytorch-tutorial-to-object-detection.
@indamutsa @YoongiKim , I had faced the same issue. The criterion() expects a 2D tensor as input i.e a list of all bounding boxes which themselves are lists, so either change the shape of the boxes and labels in the train() in train.py or change the shape of boxes and labels in the forward() of class MultiBoxLoss(). You can use unsqueeze() to resize the tensors. Hope this helps.
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