Taking ResNet to the NextLevel

ResNeXt enhances the traditional ResNet architecture by introducing a cardinality dimension, which increases the number of parallel paths within residual blocks, leading to improved feature representation and model accuracy. This development leverages grouped convolutions to efficiently expand the network's capacity without significantly increasing computational complexity, with comprehensive PyTorch implementation guidance provided to facilitate adoption.