beit_base_patch16_224

beit_base_patch16_224

Converted TIMM image classification model for LiteRT.

• Source architecture: beit_base_patch16_224
• File: model.tflite

Model Details

• Model Type: Image classification / feature backbone
• Model Stats:
  • Params (M): 86.5
  • GMACs: 17.6
  • Activations (M): 23.9
  • Image size: 224 x 224
• Papers:
  • BEiT: BERT Pre-Training of Image Transformers: https://arxiv.org/abs/2106.08254
  • An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale: https://arxiv.org/abs/2010.11929v2
• Dataset: ImageNet-1k
• Pretrain Dataset: ImageNet-22k
• Original: https://github.com/microsoft/unilm/tree/master/beit

Citation

@article{bao2021beit,
  title={Beit: Bert pre-training of image transformers},
  author={Bao, Hangbo and Dong, Li and Piao, Songhao and Wei, Furu},
  journal={arXiv preprint arXiv:2106.08254},
  year={2021}
}

@article{dosovitskiy2020vit,
  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 Uszkoreit, Jakob and Houlsby, Neil},
  journal={ICLR},
  year={2021}
}

@misc{rw2019timm,
  author = {Ross Wightman},
  title = {PyTorch Image Models},
  year = {2019},
  publisher = {GitHub},
  journal = {GitHub repository},
  doi = {10.5281/zenodo.4414861},
  howpublished = {\url{https://github.com/huggingface/pytorch-image-models}}
}