nfnets-pytorch
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A decoder for Semantic Segmentation
Hi,
I am interested in using your architecture for a semantic segmentation problem. I am therefore using the segmentation_models.pytorch library, which luckily implements timm and therefore your architecture as the encoder.
However, all of the decoders supported by segmentation_models.pytorch
use normalization. Should I just replace all instances of Conv2D
followed by BatchNorm2D
with a ScaledStdConv2D
, or do you have a better suggestion? (Should I also then put the ReLU before the ScaledStdConv2D, as you seem to do?)
Thank you in advance.