Writing ResNet from Scratch in PyTorch

ResNet, a breakthrough in computer vision, solved the problem of network performance degradation in deep networks. It introduced residual connections to address the vanishing gradient problem. The architecture uses skip connections to allow alternate paths for gradients to flow. A 34-layer ResNet was implemented and trained on the CIFAR10 dataset, which contains 60,000 32x32 color images across 10 classes. The model achieved 82.87% accuracy on the test set after 10 epochs of training. ResNet's approach enables the creation of deeper networks while maintaining generalization capability.