Comments (9)
@HolmesShuan In my experiment, more scales get bad results
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The scales in FastFCN
is consistent with PyTorch-Encoding
under this commit.
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This is caused by the different distribution between the training image and testing image. With larger crop size in the training phase, the size in the testing phase should also be enlarged.
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@wuhuikai checkpoint have record the crop size of training phase,which are equal to the crop-size of testing phase.
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In testing phase, ms
and flip
are employed. The scales in ms
should be manually tuned.
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So, if i want to train ADE20k on crop-size=520 for better performance, how should i modify the code?
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Tuning this line on the val
set by yourself.
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@Anikily @wuhuikai
Hi, I wonder why the multiscale testing setting in FastFCN is different from PyTorch-Encoding.
FastFCN:
scales = [0.5, 0.75, 1.0, 1.25, 1.5, 1.75, 2.0, 2.25] if args.dataset == 'citys' else \
[0.5, 0.75, 1.0, 1.25, 1.5, 1.75]
Enocding:
scales = [0.75, 1.0, 1.25, 1.5, 1.75, 2.0, 2.25] if args.dataset == 'citys' else \
[0.5, 0.75, 1.0, 1.25, 1.5, 1.75, 2.0]
Thanks.
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@Anikily @wuhuikai Thanks for the quick responses.
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