Comparative Analysis of TensorFlow and PyTorch for Image Classification Using CNN with Parallelism Strategies

With an emphasis on convolutional neural networks (CNNs), this research does a thorough analysis of the effectiveness and suitability of the TensorFlow and PyTorch frameworks for image classification tasks. The first objective compares runtime efficiency and computing capacity in detail, emphasising the effects of data parallelism techniques. The focus of second objective is to assess the accuracy and robustness of the model, specifically using the CIFAR-10 and CIFAR-100 dataset. The study intends to give decision-makers in the deep learning and image classification fields insightful information on the real-world consequences of choosing TensorFlow or PyTorch. The comparative analysis helps researchers and practitioners alike make well-informed decisions by fostering a deeper awareness of the advantages and disadvantages of various frameworks.