2023
DOI: 10.35870/ijsecs.v3i3.1784
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Revolutionizing Automotive Parts Classification Using InceptionV3 Transfer Learning

Djarot Hindarto

Abstract: This study presents a novel methodology for classifying automotive parts by implementing the Transfer Learning technique, utilizing the InceptionV3 architecture. We use a proprietary dataset encompassing diverse categories of automotive components for training and evaluating the model. The experimental findings demonstrate that this approach attains a performance accuracy level of 93.78% and a loss rate of 0.2938. The efficacy of InceptionV3 Transfer Learning in addressing the intricacies associated with autom… Show more

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