LIGHTWEIGHT DEEP LEARNING-BASED PRODUCT OBJECT CLASSIFICATION SCHEME FOR EDGE SERVERS

Abstract

This paper presents a lightweight deep learning-based product object classification scheme designed for deployment on edge servers. Leveraging the ImageNet Large Scale Visual Recognition Challenge 2012 (ILSVRC2012) dataset, six classes relevant to product objects are selected for model training and evaluation. The proposed scheme optimizes hyperparameters within the Vision Transformer (ViT) model architecture to ensure efficient operation on edge servers. Through rigorous evaluation, the model demonstrates high frame per second (FPS) for object classification, achieving 120.43 FPS, and a top-1 accuracy of 71.45%. Additionally, the NetScore metric, assessing the model's practical utility, yields a score of 51.05%. These results indicate the efficacy and potential of the proposed scheme for real-world deployment in online transaction environments.