eva_giant_patch14_224.clip_ft_in1k

Converted TIMM image classification model for LiteRT.

• Source architecture: eva_giant_patch14_224
• Source checkpoint: timm/eva_giant_patch14_224.clip_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, 224, 224]
• Output: ImageNet-1K logits, shape [1, 1000]
• Converted artifact size: 3.77 GiB
• Weight storage: buffer_offset

Model Details

• Model Type: Image classification / feature backbone
• Model Stats:
  • Params (M): 1012.6
  • GMACs: 267.2
  • Activations (M): 192.6
  • Image size: 224 x 224
• Papers:
  • EVA-CLIP: Improved Training Techniques for CLIP at Scale: https://arxiv.org/abs/2303.15389
• Original:
  • https://github.com/baaivision/EVA
  • https://huggingface.co/QuanSun/EVA-CLIP

Citation

@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}}
}