Revolutionizing Automotive Parts Classification Using InceptionV3 Transfer Learning
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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 automotive parts classification is demonstrated through its utilization of pre-existing knowledge from diverse domains. The resultant model reflects a capacity to accurately discern spare parts, thereby enhancing the efficiency of the automotive inventory management process. Utilizing InceptionV3 Transfer Learning in this scenario yields a notable and favorable outcome, thereby revolutionizing the traditional framework of automotive parts categorization. The model's efficacy in enhancing the efficiency and accuracy of automotive inventory management is evidenced by its achievement of a notable precision level and a minimal loss rate. The implications of these findings are significant in addressing intricate classification challenges within the automotive industry. They pave the way for utilizing intelligent technologies to optimize parts identification and management processes. This study establishes a foundation for a novel approach to comprehending and applying categorization systems for automotive components. This is achieved by harnessing the capabilities of Transfer Learning using the InceptionV3 model.
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References
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