Comments (4)
Hi,
all our experiments use the models provided by torchvision. If you want to reproduce some of the experiments, you should have a look at the run.sh. Alternatively, you can manually set MODEL.ARCH
to vit_b_16
or swin_b
. If you encounter any errors after updating the environment, please provide the details.
from test-time-adaptation.
Hi,
I had the same issue. I was able to use vit_b_16
architecture. However, I get this error on using swin_b
:
raise AttributeError(f"'{type(self).__name__}' object has no attribute '{name}'") AttributeError: 'SwinTransformer' object has no attribute 'patch_embed'
Any suggestions to resolve this?
from test-time-adaptation.
Hi,
can you provide the configuration you are using?
from test-time-adaptation.
Thank you! @RobMarsden it worked for me.
from test-time-adaptation.
Related Issues (17)
- Which part of parameters are updated? HOT 1
- The settings of continual test-time domain adaptation HOT 2
- a basic question about TTA HOT 1
- .pt download fail HOT 1
- ImageNet-C dataset HOT 3
- type error HOT 3
- Error while running MODEL.ARCH Vit-B-16/ Vit-L-14/Vit-B-32 on CLIP-like models HOT 3
- KeyError: 'type' in zenodo_download.py upon running CIFAR10C HOT 1
- wideresnet object has no attribute 'model' HOT 2
- The segmentation task HOT 1
- Can't reproduce the results of ROID on imagenet-c HOT 2
- Cannot reproduce the results on "correlated" setting for CIFAR-10C HOT 1
- Can you provide the code of rmt method for semantic segmentation? HOT 1
- Cannot run the RMT method without exemplars HOT 1
- mixed domains TTA setting HOT 2
- Adding new method SANTA HOT 1
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