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Deep Residual networks ResNet

Deep Residual networks ResNet RykXM

Deep Residual networks, shortly named as ResNet is developed based on the core idea addressed as shortcut connections or skip connections. These connections provide an alternate pathway for data and gradients to flow, thus making the training possible. The simplest model is ResNet-18 which has 18 layers. The input image size of this model is 224×224×3. It was developed by Kaiming et al. , [20] and was the winner of ILSVRC-2015. The detailed architecture of ResNet- 18 and how to skip connections run in parallel is shown in Fig. 5

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