gupta-abhay / pytorch-vit

An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale

Vision Transformers

Implementation of Vision Transformer in PyTorch, a new model to achieve SOTA in vision classification with using transformer style encoders. Associated blog article.

Credits to Phil Wang for the gif

Features

• ViT
• ViT with convolutional patches
• ViT with convolutional stems
  • Early Convolutional Stem
  • Scaled ReLU Stem
• GAP Pooling

Citations

@article{dosovitskiy2020image,
  title={An image is worth 16x16 words: Transformers for image recognition at scale},
  author={Dosovitskiy, Alexey and Beyer, Lucas and Kolesnikov, Alexander and Weissenborn, Dirk and Zhai, Xiaohua and Unterthiner, Thomas and Dehghani, Mostafa and Minderer, Matthias and Heigold, Georg and Gelly, Sylvain and others},
  journal={arXiv preprint arXiv:2010.11929},
  year={2020}
}

@article{xiao2021early,
  title={Early convolutions help transformers see better},
  author={Xiao, Tete and Singh, Mannat and Mintun, Eric and Darrell, Trevor and Doll{\'a}r, Piotr and Girshick, Ross},
  journal={arXiv preprint arXiv:2106.14881},
  year={2021}
}

@article{wang2021scaled,
  title={Scaled ReLU Matters for Training Vision Transformers},
  author={Wang, Pichao and Wang, Xue and Luo, Hao and Zhou, Jingkai and Zhou, Zhipeng and Wang, Fan and Li, Hao and Jin, Rong},
  journal={arXiv preprint arXiv:2109.03810},
  year={2021}
}

@article{zhai2021scaling,
  title={Scaling vision transformers},
  author={Zhai, Xiaohua and Kolesnikov, Alexander and Houlsby, Neil and Beyer, Lucas},
  journal={arXiv preprint arXiv:2106.04560},
  year={2021}
}