README.md

metadata

library_name: litert
base_model: timm/eva02_large_patch14_448.mim_m38m_ft_in1k
license: mit
tags:
  - vision
  - image-classification
  - timm
datasets:
  - imagenet-1k

eva02_large_patch14_448.mim_m38m_ft_in1k

Converted TIMM image classification model for LiteRT.

• Source architecture: eva02_large_patch14_448
• Source checkpoint: timm/eva02_large_patch14_448.mim_m38m_ft_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, 448, 448]
• Output: ImageNet-1K logits, shape [1, 1000]
• Converted artifact size: 1.14 GiB
• Weight storage: inline

Model Details

• Model Type: Image classification / feature backbone
• Model Stats:
  • Params (M): 305.1
  • GMACs: 362.3
  • Activations (M): 689.9
  • Image size: 448 x 448
• Papers:
  • EVA-02: A Visual Representation for Neon Genesis: https://arxiv.org/abs/2303.11331
  • EVA-CLIP: Improved Training Techniques for CLIP at Scale: https://arxiv.org/abs/2303.15389
• Original:
  • https://github.com/baaivision/EVA
  • https://huggingface.co/Yuxin-CV/EVA-02
• Pretrain Dataset: ImageNet-22k
• Dataset: ImageNet-1k

Citation

@article{EVA02,
  title={EVA-02: A Visual Representation for Neon Genesis},
  author={Fang, Yuxin and Sun, Quan and Wang, Xinggang and Huang, Tiejun and Wang, Xinlong and Cao, Yue},
  journal={arXiv preprint arXiv:2303.11331},
  year={2023}
}

@article{EVA-CLIP,
  title={EVA-02: A Visual Representation for Neon Genesis},
  author={Sun, Quan and Fang, Yuxin and Wu, Ledell and Wang, Xinlong and Cao, Yue},
  journal={arXiv preprint arXiv:2303.15389},
  year={2023}
}

@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}}
}