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library_name: pytorch |
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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. |
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Original paper: [Deep Residual Learning for Image Recognition, He et al., 2015](https://arxiv.org/abs/1512.03385) |
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# ResNet-50 |
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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. |
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Model Configuration: |
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- Reference implementation: [ResNet50_v1.5](https://pytorch.org/vision/stable/models/generated/torchvision.models.resnet50.html) |
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- Original Weight: [ResNet50_Weights.IMAGENET1K_V2](https://download.pytorch.org/models/resnet50-11ad3fa6.pth) |
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- Resolution: 3x224x224 |
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- Support Cooper version: |
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- Cooper SDK: [2.5.2] |
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- Cooper Foundry: [2.2] |
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| Model | Device | Model Link | |
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| :-----: | :-----: | :-----: | |
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| Resnet50 | N1-655 | [Model_Link](https://huggingface.co/Ambarella/ResNet/blob/main/n1-655_resnet_v1.5_50.bin) | |
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| Resnet50 | CV72 | [Model_Link](https://huggingface.co/Ambarella/ResNet/blob/main/cv72_resnet_v1.5_50.bin) | |
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| Resnet50 | CV75 | [Model_Link](https://huggingface.co/Ambarella/ResNet/blob/main/cv75_resnet_v1.5_50.bin) | |
