Efficient Training of Visual Transformers with Small Datasets

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Deep Learning VTs-Drloc
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  • Strange reproduced results of Swin transformer

    Strange reproduced results of Swin transformer

    Hi authors, I have reproduced all results based on your codes. Most of them are consistent with the reported results, except the swin transformer. Below are some results (with reported results followed in brackets): Trained with 8 gpus (a100): Cifar10: 75.00 (59.47), CIFAR100: 52.26 (53.28), SVHN: 38.10 (71.60) Trained with 4 gpus: CIFAR10: 81.91 (59.47), CIFAR100: 62.30 (53.28), SVHN: 91.29 (71.60) It seems that the batch size affect swin a lot from results above. All reproduced results are comparable with vit. (e.g. ViT on CIFAR10 with 8 gpus: 77.00 (71.70)). Do you have any idea on the reason?

    opened by xiangyu8 6
  • compare to CvT

    compare to CvT

    Hi

    Thanks for sharing this good work. I'm curious about why the proposed loss function can outperform CvT, which contains a depthwise convolution that is capable to learn local features.

    opened by liyunsheng13 3
  • Imagenet-100 split

    Imagenet-100 split

    Thanks for your amazing work! I also want to train with imagenet-100 using the subset in the file /scripts/imagenet-100.lst. But I didn't find its train/val split. May I know your splits or split reference?

    opened by xiangyu8 2
  • Augmentation settings on CIFAR10/100

    Augmentation settings on CIFAR10/100

    Hi, thank you so much for sharing this excellent work.

    I have some confusion about the experimental setup of CIFAR10/100. Commonly used augmentation settings are random cropping and padding=4, and the input image resolution is 32x32. But this setting does not seem to be able to get the output resolution of 7x7 as described in the paper when using SwinT. So could you please tell me the detailed augmentation settings you used on CIFAR10/100, and whether there are any changes to the network structure of original VTs.

    Thanks again.

    opened by lkhl 2
  • [ Pretrained Models ]

    [ Pretrained Models ]

    Hi,

    Thanks for the wonderful work. Could you please, share links to the default models used for finetunning experiments ?

    Specifically, the pretrained models for finetunning experiments are they trained from scratch on ImageNet1K ? - Because the official ones published for ViTs models are trained on ImageNet21K and finetunned on ImageNet1K ?

    Thanks,

    opened by IemProg 1
  • CVE-2007-4559 Patch

    CVE-2007-4559 Patch

    Patching CVE-2007-4559

    Hi, we are security researchers from the Advanced Research Center at Trellix. We have began a campaign to patch a widespread bug named CVE-2007-4559. CVE-2007-4559 is a 15 year old bug in the Python tarfile package. By using extract() or extractall() on a tarfile object without sanitizing input, a maliciously crafted .tar file could perform a directory path traversal attack. We found at least one unsantized extractall() in your codebase and are providing a patch for you via pull request. The patch essentially checks to see if all tarfile members will be extracted safely and throws an exception otherwise. We encourage you to use this patch or your own solution to secure against CVE-2007-4559. Further technical information about the vulnerability can be found in this blog.

    If you have further questions you may contact us through this projects lead researcher Kasimir Schulz.

    opened by TrellixVulnTeam 0
Owner
Yahui Liu
Yahui Liu currently studies in Multimedia and Human Understanding Group (MHUG), University of Trento, Italy.
Yahui Liu
GitHub
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