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All experiments I did were with a batch size of 1 video per GPU given that it already takes quite a bit of GPU memory with long videos / high resolution, so there might be some padding to fix indeed.
I try to train the network on HC-STVGv2 dataset using the command provided in the README.md file:
Unfortunately, I encountered this issue in models/tubedetr.py line 180
. Besides, the durations of the eight samples are:
[100, 100, 69, 100, 65, 86, 100, 100]
.I think this problem is probably related to the padding approach. Do you have any clue with this BUG and how to fix it? Thank you very much!
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