Comments (2)
Hi,
Thank you for the comment!
It means that results can differ depending on a random seed. This is an expensive setup that takes 16v100 and 170h, and we tried it only once. Also, there is a mismatch in default PyTorch and TF augmentation of ImageNet images; in PyTorch, to the best of my knowledge, there is no min_object_covered parameter https://github.com/google-research/simclr/blob/3fb622131d1b6dee76d0d5f6aac67db84dab3800/data_util.py#L269.
Let us know if you have any further questions!
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Thank you~
from simclr-pytorch.
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