Add release note for v1.3.1

README.md

@@ -8,7 +8,7 @@ Swin Transformer V2 extends the original Swin Transformer with improved attentio
 
 Original paper: [Swin Transformer V2: Scaling Up Capacity and Resolution](https://arxiv.org/abs/2111.09883)
 
-# Swin Transformer V2-Tiny
+# SwinV2-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.
 
@@ -29,4 +29,4 @@ Model Configuration:
 | SwinV2-Tiny | CV72 | Amba_optimized | [Model_Link](https://huggingface.co/Ambarella/SwinV2/blob/main/cv72_swin_tiny_v2_amba_optimized.bin) |
 | SwinV2-Tiny | CV72 | Activation_fp16 | [Model_Link](https://huggingface.co/Ambarella/SwinV2/blob/main/cv72_swin_tiny_v2_act16.bin) |
 | SwinV2-Tiny | CV75 | Amba_optimized | [Model_Link](https://huggingface.co/Ambarella/SwinV2/blob/main/cv75_swin_tiny_v2_amba_optimized.bin) |
-| SwinV2-Tiny | CV75 | Activation_fp16 | [Model_Link](https://huggingface.co/Ambarella/SwinV2/blob/main/cv75_swin_tiny_v2_act16.bin) |
+| SwinV2-Tiny | CV75 | Activation_fp16 | [Model_Link](https://huggingface.co/Ambarella/SwinV2/blob/main/cv75_swin_tiny_v2_act16.bin) |

config.json

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x7_swin_tiny_v2_act16.bin

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x7_swin_tiny_v2_amba_optimized.bin

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