Beyond Depth: Evaluating ResNet-50 and ResNet-152 Performance on Cifar-10 Dataset

Deep learning techniques have transformed the area of computer vision, with Residual Networks (ResNets) particularly notable for their capacity to effectively train very deep neural networks. The work conducts a thorough analysis of the use of two popular ResNet architectures-ResNet-50 and ResNet-152-for image classification tasks using the CIFAR-10 dataset. On investigating the delicate relationship between model depth, computational complexity, and classification performance. Providing a thorough investigation of training dynamics, convergence rates, and final model performance. Our findings show that ResNet-152 delivers somewhat greater accuracy, notably on the more complicated CIFAR -10 dataset, but at a substantial expense in terms of processing resources and training time. Quantifying the trade-offs between model depth and efficiency, and provide empirical data to help practitioners choose suitable architectures for comparable picture categorization tasks. Furthermore, investigation ways for optimizing training processes, such as learning rate scheduling and data augmentation strategies, in order to maximize the potential of each design.