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License: MIT License
What input normalization did you use?
I'm using your TF-converted model (tiny) and testing on ImageNet 1K validation images, but I'm getting 78% accuracy instead of the officially published 82%.
Do you think that a 4 percent drop could be because of Torch/Tensorflow difference? Another converted model (https://github.com/sayakpaul/ConvNeXt-TF) attains 81% and the only difference between you and sayakpaul is the implementation of depthwise convolution. Have you tested your model on validation and if so, can you share the steps so that I could figure out the problem?
Do you have the pretrained weights for tf somewhere saved?
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