Source architecture: beit_large_patch16_512
Source checkpoint: timm/beit_large_patch16_512.in22k_ft_in22k_in1k
File: model.tflite
Runtime target: CPU only. GPU delegation is not expected for this converted file.
Input: float32 tensor in NCHW layout, shape [1, 3, 512, 512]
Output: ImageNet-1K logits, shape [1, 1000]
Converted artifact size: 2.64 GiB
Weight storage: buffer_offset

Model Details

Model Type: Image classification / feature backbone
Model Stats:
- Params (M): 305.7
- GMACs: 362.2
- Activations (M): 656.4
- Image size: 512 x 512
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

bibtex

@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}
}

bibtex

@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}
}

bibtex

@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}}
}

litert-community/beit_large_patch16_512.in22k_ft_in22k_in1k

beit_large_patch16_512.in22k_ft_in22k_in1k

Model Details

Citation

litert-community/
beit_large_patch16_512.in22k_ft_in22k_in1k