How to finetune the 384 models with window size 12?

Great works! I have some questions about fine-tuning on ImageNet 1K. In the paper, you claimed 384^2 input models are obtained by fine-tuning as also pointed by #24:

For other resolutions such as 384^2, we fine-tune the models trained at 224^2 resolution, instead of training from scratch, to reduce GPU consumption.

I see that you use window_size 12 for 384 models which makes the fine-tuning confusing because of the existence of parameters: relative_position_bias_table and attn_mask. Do you use interpolation for this issue? Which interpolation method do you use? bicubic?

Thanks for your reply in advance!

[ancientmooner]

hi how load modify the pretrained model of input size 384 to finetune the input 448 when load model ?

bicubic also

[zeliu98]

Instructions and configs for fine-tuning on higher resolution can be found here: https://github.com/microsoft/Swin-Transformer/blob/main/get_started.md#fine-tuning-on-higher-resolution