
This model is a TensorFlow port of ViT B-16 [1] trained with recipes from [2]. ImageNet-1k dataset was used for training purposes. You can refer to [this notebook](https://github.com/sayakpaul/probing-vits/blob/main/notebooks/load-jax-weights-vitb16.ipynb) to know how the porting was done.

## References

[1] An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale: https://arxiv.org/abs/2010.11929

[2] How to train your ViT? Data, Augmentation, and Regularization in Vision Transformers: https://arxiv.org/abs/2106.10270
{"library_name": "keras"}
keras arxiv:2010.11929 arxiv:2106.10270 region:us
TAGS
#keras #arxiv-2010.11929 #arxiv-2106.10270 #region-us 


This model is a TensorFlow port of ViT B-16 [1] trained with recipes from [2]. ImageNet-1k dataset was used for training purposes. You can refer to this notebook to know how the porting was done.

## References

[1] An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale: URL

[2] How to train your ViT? Data, Augmentation, and Regularization in Vision Transformers: URL
## References

[1] An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale: URL

[2] How to train your ViT? Data, Augmentation, and Regularization in Vision Transformers: URL
TAGS
#keras #arxiv-2010.11929 #arxiv-2106.10270 #region-us 
 ## References

[1] An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale: URL

[2] How to train your ViT? Data, Augmentation, and Regularization in Vision Transformers: URL
passage: TAGS
#keras #arxiv-2010.11929 #arxiv-2106.10270 #region-us 
## References

[1] An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale: URL

[2] How to train your ViT? Data, Augmentation, and Regularization in Vision Transformers: URL