Multiscale MAXIM

[FBehrad]

Hello,
First of all, thank you so much for sharing the maxim architecture in PyTorch. Unfortunately, the provided code does not work with a num_supervision_scales higher than 1. I have changed the code slightly but I have not been successful to solve the issues yet. For example, in line 1115, we have buttleneckblocks, while according to the paper we need backbone blocks (depth 4 in Table 9 of the paper). Where are these blocks?
According to issue #3, I have downloaded the weights, but the name of layers are different from your code. Therefore, the PyTorch model cannot load the weights properly.
Finally, according to the paper maxim has around 14 million parameters for image enhancement, but the PyTorch model has around 400 million parameters. What is the problem? I used the following code to calculate the number of parameters.
print(sum(p.numel() for p in maxim.parameters() if p.requires_grad))
Can you please let me know when the final version of the model will be available?

[vztu]

Hi thanks for your attention, we have released a clean version here: https://github.com/vztu/maxim-pytorch/tree/main/maxim_pytorch. Please check it out and let us know if it works for you.