Add LiteRT converted swinv2_tiny_window8_256

README.md

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+---
+library_name: litert
+base_model: timm/swinv2_tiny_window8_256.ms_in1k
+tags:
+  - vision
+  - image-classification
+datasets:
+  - imagenet-1k
+---
+
+# swinv2_tiny_window8_256
+
+Converted TIMM image classification model for LiteRT.
+
+- Source architecture: swinv2_tiny_window8_256
+- File: model.tflite
+
+## Model Details
+
+- **Model Type:** Image classification / feature backbone
+- **Model Stats:**
+  - Params (M): 28.3
+  - GMACs: 6.0
+  - Activations (M): 24.6
+  - Image size: 256 x 256
+- **Papers:**
+  - Swin Transformer V2: Scaling Up Capacity and Resolution: https://arxiv.org/abs/2111.09883
+- **Original:** https://github.com/microsoft/Swin-Transformer
+- **Dataset:** ImageNet-1k
+
+## Citation
+
+```bibtex
+@inproceedings{liu2021swinv2,
+  title={Swin Transformer V2: Scaling Up Capacity and Resolution}, 
+  author={Ze Liu and Han Hu and Yutong Lin and Zhuliang Yao and Zhenda Xie and Yixuan Wei and Jia Ning and Yue Cao and Zheng Zhang and Li Dong and Furu Wei and Baining Guo},
+  booktitle={International Conference on Computer Vision and Pattern Recognition (CVPR)},
+  year={2022}
+}
+```
+```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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