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LICENSE

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+BSD 3-Clause License
+
+Copyright (c) Soumith Chintala 2016,
+All rights reserved.
+
+Redistribution and use in source and binary forms, with or without
+modification, are permitted provided that the following conditions are met:
+
+* Redistributions of source code must retain the above copyright notice, this
+  list of conditions and the following disclaimer.
+
+* Redistributions in binary form must reproduce the above copyright notice,
+  this list of conditions and the following disclaimer in the documentation
+  and/or other materials provided with the distribution.
+
+* Neither the name of the copyright holder nor the names of its
+  contributors may be used to endorse or promote products derived from
+  this software without specific prior written permission.
+
+THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS"
+AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE
+IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE
+DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE
+FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL
+DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR
+SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER
+CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY,
+OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE
+OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.

README.md

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 library_name: pytorch
 ---
 
-![resnet_logo](resource/ResNet50.png)
-
-# ResNet
+![resnet_logo](resource/ResNet.png)
 
 ResNet is a family of deep convolutional neural networks that introduced residual (skip) connections to enable stable training of very deep architectures with strong representational capacity.
-- Original paper: Deep Residual Learning for Image Recognition, He et al., 2015
-- Paper: https://arxiv.org/abs/1512.03385
+
+Original paper: [Deep Residual Learning for Image Recognition, He et al., 2015](https://arxiv.org/abs/1512.03385)
+
+# ResNet-50
 
 ResNet-50 is a commonly used 50-layer variant that offers a strong balance between accuracy and computational cost and is widely adopted as a baseline and as a backbone feature extractor for tasks such as object detection, segmentation, and re-identification.
-- Reference implementation (ResNet-50): torchvision.models.resnet50
-- Torchvision source: https://pytorch.org/vision/stable/models/generated/torchvision.models.resnet50.html
 
-| Model | Resolution | Device | Model Link |
-| :-----: | :-----: | :-----: | :-----: |
-| Resnet50 | 3x224x224 | N1-655 | Model_Link |
-| Resnet50 | 3x224x224 | CV72 | Model_Link |
-| Resnet50 | 3x224x224 | CV75 | Model_Link |
+Model Configuration:
+- Reference implementation: [ResNet50_v1.5](https://pytorch.org/vision/stable/models/generated/torchvision.models.resnet50.html)
+- Original Weight: [ResNet50_Weights.IMAGENET1K_V2](https://download.pytorch.org/models/resnet50-11ad3fa6.pth)
+- Resolution: 3x224x224
+- Support Cooper version:
+    - Cooper SDK: [2.5.2]
+    - Cooper Foundry: [2.2]
+
+| Model | Device | Model Link |
+| :-----: | :-----: | :-----: |
+| Resnet50 | N1-655 | [Model_Link](https://huggingface.co/Ambarella/ResNet/blob/main/n1-655_resnet_v1.5_50.bin) |
+| Resnet50 | CV72 | [Model_Link](https://huggingface.co/Ambarella/ResNet/blob/main/cv72_resnet_v1.5_50.bin) |
+| Resnet50 | CV75 | [Model_Link](https://huggingface.co/Ambarella/ResNet/blob/main/cv75_resnet_v1.5_50.bin) |