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All Journal Jurnal Ilmiah Multidisiplin Indonesia
Taswanda Taryo
Master’s Program in Informatics Engineering, Postgraduate Program, Pamulang University
Agriculture, Biological Sciences & Forestry Computer Science & IT Earth & Planetary Sciences Engineering Physics

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Implementation of Convolutional Neural Networks for Eyeglass Product Image Retrieval: A Comparative Study of ResNet-50 and MobileNetV2 Handri Taufik; Sajarwo Anggai; Taswanda Taryo
Jurnal Ilmiah Multidisiplin Indonesia (JIM-ID) Vol. 5 No. 02 (2026): Jurnal Ilmiah Multidisplin Indonesia (JIM-ID), February 2026
Publisher : Sean Institute

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Abstract

The increasing similarity among eyewear product designs poses significant challenges for conventional text-based search systems, highlighting the need for effective Content-Based Image Retrieval (CBIR) approaches. This study proposes a CNN-based CBIR system for eyeglass frame and sunglasses retrieval, employing a comparative analysis of ResNet50 and MobileNetV2 as feature extractors. The dataset comprises 4,500 gallery images and 300 query images, with feature similarity measured using cosine similarity and accelerated through FAISS indexing. Experimental results indicate that ResNet50 achieves higher recall (0.0622), demonstrating its ability to capture more complex visual features. In contrast, MobileNetV2 provides superior ranking performance, achieving an mAP of 0.6091 and an MRR of 0.1427, outperforming ResNet50 (mAP of 0.5019 and MRR of 0.0713), while also reducing feature extraction time (0.1348 s versus 0.2023 s). These findings suggest that ResNet50 is more suitable for accuracy-oriented retrieval tasks, whereas MobileNetV2 is better suited for real-time and resource-constrained applications.
Co-Authors Handri Taufik Sajarwo Anggai