Add LiteRT converted vit_tiny_patch16_224

README.md

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+---
+library_name: litert
+base_model: timm/vit_tiny_patch16_224.augreg_in21k_ft_in1k
+tags:
+  - vision
+  - image-classification
+datasets:
+  - imagenet-1k
+---
+
+# vit_tiny_patch16_224
+
+Converted TIMM image classification model for LiteRT.
+
+- Source architecture: vit_tiny_patch16_224
+- File: model.tflite
+
+## Model Details
+
+- **Model Type:** Image classification / feature backbone
+- **Model Stats:**
+  - Params (M): 5.7
+  - GMACs: 1.1
+  - Activations (M): 4.1
+  - Image size: 224 x 224
+- **Papers:**
+  - How to train your ViT? Data, Augmentation, and Regularization in Vision Transformers: https://arxiv.org/abs/2106.10270
+  - An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale: https://arxiv.org/abs/2010.11929v2
+- **Dataset:** ImageNet-1k
+- **Pretrain Dataset:** ImageNet-21k
+- **Original:** https://github.com/google-research/vision_transformer
+
+## Citation
+
+```bibtex
+@article{steiner2021augreg,
+  title={How to train your ViT? Data, Augmentation, and Regularization in Vision Transformers},
+  author={Steiner, Andreas and Kolesnikov, Alexander and and Zhai, Xiaohua and Wightman, Ross and Uszkoreit, Jakob and Beyer, Lucas},
+  journal={arXiv preprint arXiv:2106.10270},
+  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}}
+}
+```

model.tflite

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