Add LiteRT converted swin_small_patch4_window7_224

README.md

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+---
+library_name: litert
+base_model: timm/swin_small_patch4_window7_224.ms_in22k_ft_in1k
+tags:
+  - vision
+  - image-classification
+datasets:
+  - imagenet-1k
+---
+# swin_small_patch4_window7_224
+
+Converted TIMM image classification model for LiteRT.
+
+- Source architecture: `swin_small_patch4_window7_224`
+- Source checkpoint: `timm/swin_small_patch4_window7_224.ms_in22k_ft_in1k`
+- File: `model.tflite`
+- Input: `float32` tensor in NCHW layout, shape `[1, 3, 224, 224]`
+- Output: ImageNet-1K logits, shape `[1, 1000]`
+
+## Runtime Status
+
+- CPU smoke test: passed with LiteRT `CompiledModel`.
+- GPU delegation: currently blocked for this model by rank-5 tensor patterns in the GPU backend, mostly `RESHAPE`, `TRANSPOSE`, and related window/attention operations. The model is published as CPU-ready while GPU support is being improved.
+
+## Model Details
+
+- **Model Type:** Image classification / feature backbone
+- **Model Stats:**
+  - Params (M): 49.6
+  - GMACs: 8.8
+  - Activations (M): 27.5
+  - Image size: 224 x 224
+- **Papers:**
+  - Swin Transformer: Hierarchical Vision Transformer using Shifted Windows: https://arxiv.org/abs/2103.14030
+- **Original:** https://github.com/microsoft/Swin-Transformer
+- **Dataset:** ImageNet-1k
+- **Pretrain Dataset:** ImageNet-22k
+
+## Citation
+
+```bibtex
+@inproceedings{liu2021Swin,
+  title={Swin Transformer: Hierarchical Vision Transformer using Shifted Windows},
+  author={Liu, Ze and Lin, Yutong and Cao, Yue and Hu, Han and Wei, Yixuan and Zhang, Zheng and Lin, Stephen and Guo, Baining},
+  booktitle={Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV)},
+  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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