Add release note for v1.1.0

README.md

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+library_name: pytorch
+---
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+![swinv2_logo](resource/SwinV2.png)
+
+Swin Transformer V2 extends the original Swin Transformer with improved attention scaling and positional encoding, enabling stable training and strong performance on very large and high-resolution vision datasets.
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+Original paper: [Swin Transformer V2: Scaling Up Capacity and Resolution](https://arxiv.org/abs/2111.09883)
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+# Swin Transformer V2-Tiny
+
+This model uses the Swin Transformer V2-Tiny variant, a compact hierarchical transformer that applies shifted window self-attention for efficient computation. It is well suited for high-resolution image classification and as a backbone for dense vision tasks such as detection and segmentation.
+
+Model Configuration:
+- Reference implementation: [torchvision.models.swin_v2_t](https://pytorch.org/vision/0.20/models/swin_transformer.html)
+- Original Weight: [Swin_V2_T_Weights.IMAGENET1K_V1](https://download.pytorch.org/models/swin_v2_t-b137f0e2.pth)
+- Resolution: 3x256x256
+- Support Cooper version:
+    - Cooper SDK: [2.5.2]
+    - Cooper Foundry: [2.2]
+
+| Model | Device | Model Link |
+| :-----: | :-----: | :-----: |
+| SwinV2-Tiny | N1-655 | [Model_Link](https://huggingface.co/Ambarella/SwinV2/blob/main/n1-655_swin_tiny_v2.bin) |
+| SwinV2-Tiny | CV72 | [Model_Link](https://huggingface.co/Ambarella/SwinV2/blob/main/cv72_swin_tiny_v2.bin) |
+| SwinV2-Tiny | CV75 | [Model_Link](https://huggingface.co/Ambarella/SwinV2/blob/main/cv75_swin_tiny_v2.bin) |
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