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Issues list

is there some thing wrong?
#6 opened Feb 21, 2021 by lvzhengyi0204
4
something about custom dataset
#7 opened Feb 24, 2021 by David-zaiwang
8
'num_classes' not used
#9 opened Mar 1, 2021 by DingLei14
1
Dataset preprocessing
#11 opened Mar 3, 2021 by Cassieyy
9
train error
#12 opened Mar 5, 2021 by over-star
8
Has the performance of TransUNet been exaggerated?
#14 opened Mar 8, 2021 by xychenunc
2
Have you tried to add transformer to all levels of the U-Net?
#16 opened Mar 17, 2021 by NipoLau
1
Tensorflow variant
#17 opened Mar 17, 2021 by andife
1
IndexError:index 1 is out of bounds for dimension 0 with size 1
#18 opened Mar 19, 2021 by lisherlock
8
cannot find the 'R50-ViT-L_16' pretrained model
#22 opened Mar 23, 2021 by yyxun
5
Questions about your data
#23 opened Mar 26, 2021 by qdzwss
10
Request preliminary data
#24 opened Mar 28, 2021 by Fhujinwu
20
Performance is much worse when input image size is changed?
#25 opened Mar 30, 2021 by xychen2022
1
Train error
#26 opened Apr 3, 2021 by StevenAZy
7
Question about "patch_size"
#30 opened Apr 7, 2021 by benzyz1129
2
TransUnet for RGB-images?
#31 opened Apr 7, 2021 by andife
9
train error: after a time, NaN or Inf found in input tensor.
#32 opened Apr 7, 2021 by andife
3
"num_classes" setting
#33 opened Apr 7, 2021 by StevenAZy
2
How about the performance compared with the original U-Net/nnUNet encoder?
#34 opened Apr 9, 2021 by licaizi
2
would please mention in the readme.txt that the size of the input image should be dividable by 16
#35 opened Apr 9, 2021 by MoHassoubah
3
colab error
#36 opened Apr 12, 2021 by YUUUCC
2D slices for testing
#37 opened Apr 13, 2021 by andife
4
Evaluation metric
#39 opened Apr 15, 2021 by Mordokkai
4
RuntimeError: Given groups=1, weight of size [256, 1024, 3, 3], expected input[1, 512, 28, 28] to have 1024 channels, but got 512 channels instead
#41 opened Apr 19, 2021 by YJY9898
2
Have you tried to train the model without pretraining on imagenet?
#42 opened Apr 20, 2021 by licaizi
1
ProTip! Exclude everything labeled bug with -label:bug.