This research presents CIPAC (CNN Inter-framework Parameter Analysis and Comparison), a validation approach designed to ensure the integrity of Deep Learning models during their transfer between computational frameworks. Although initially tested on Convolutional Neural Networks (CNNs), CIPAC is versatile enough for various Deep Learning architectures. It goes beyond traditional methods that focus on output accuracy, by examining the models’ architecture, parameters, and components to maintain consistency after transitions, like moving from PyTorch to ONNX framework. Inspired by software architecture’s stringent validation standards, CIPAC addresses the challenges of working with Machine Learning models on different platforms, making it an essential tool for both researchers and professionals in artificial intelligence.
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