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License: MIT License
TokenMix: Rethinking Image Mixing for Data Augmentation in Vision Transformers (ECCV 2022)
License: MIT License
Hi, I am curious about the problem of dimension inconsistency.
(1) The shape of "score_map" that generated in generate_label.py is [2, 5, H, W], but the dimension of score_maps seems to be [2, H, W, 5] in "score_maps[-1,0,0,5]=target " (line 68 of TokenMix)/relabel/dataset.py )
(2) The dimension of "label_maps_topk" in line 48 of TokenMix/tokenmix.py is [batch_size, 3, H, H, 5], but I cannot find how to transform "score_maps" to "label_maps_topk", and what information is stored in the 0, 1, 2 dimension of "label_maps_topk", respectively.
What is the basis or prior knowledge of your partition?
Thank you for your nice work about TokenMix!!
Could you share the pre-trained transformer model (Especially, Swin-Tiny) trained with TokenMix?
The paper briefly mentioned that uniform random TokenMix is ineffective for CNNs but beneficial for Transformers. However, I did not find empirical numbers for these claims. Could you provide the numbers for comparison with CutMix as well as structured random TokenMix?
Dear @jihaonew
How we can run TokenMix on CIFAR10 dataset?
Very sorry, probably because my code ability is too weak and did not find the code about the neural activation map part, can you tell me how it manifests.
Thank you very much
When is the source code going to be released?!
Hello guys,
I am Bill from ECCV. We had a nice talk about your work during your poster session.
Congrats for one more time :)
I would like to ask whether you could provide us the checkpoints of your pretrained models.
Thanks a lot ;)
hi i'm very interested in the data augmentation which you release.
can you release the code which is about tokenmix add in to swin transformer?
thank you alot
In addition to TokenMix before the first Transformer block, have you considered or tried TokenMix in the middle of the model?
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