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PyTorch vs Tensorflow: Who Wins in CNN?
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This research paper examines the efficiency of two popular deep learning libraries, TensorFlow and PyTorch, in developing convolutional neural networks. The authors aim to determine if the choice of library impacts the overall performance of the system during training and design. They evaluate both libraries using six criteria: user-friendliness, available documentation, ease of integration, overall training time, overall accuracy, and execution time during evaluation. The paper proposes a novel methodology for comparing these libraries by eliminating external factors that could influence the comparison and focusing solely on the six chosen criteria. The study finds that while both libraries offer similar capabilities, PyTorch is better suited for tasks that prioritize speed and ease of use, while TensorFlow excels in tasks demanding accuracy and flexibility. The authors conclude that the choice of library has a significant impact on both design and performance and that the presented criteria can assist users in selecting the most appropriate library for their specific needs.
Read more: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9699128/pdf/sensors-22-08872.pdf
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