M., E., K., M., R., Y., S., A., M., W., H., M., M., D. (2025). EGYPTIAN ARTIFACTS’ MATERIALS CLASSIFIER BASED ON LIGHTWEIGHT DEEP LEARNING ARCHITECTURES. Egyptian Journal of Archaeological and Restoration Studies, 15(1), 69-77. doi: 10.21608/ejars.2025.434903
Esmat, M.; Moussa, K.; Yousri, R.; Alwardany, S.; Wessam, M.; Mostafa, H.; Darweesh, M.. "EGYPTIAN ARTIFACTS’ MATERIALS CLASSIFIER BASED ON LIGHTWEIGHT DEEP LEARNING ARCHITECTURES". Egyptian Journal of Archaeological and Restoration Studies, 15, 1, 2025, 69-77. doi: 10.21608/ejars.2025.434903
M., E., K., M., R., Y., S., A., M., W., H., M., M., D. (2025). 'EGYPTIAN ARTIFACTS’ MATERIALS CLASSIFIER BASED ON LIGHTWEIGHT DEEP LEARNING ARCHITECTURES', Egyptian Journal of Archaeological and Restoration Studies, 15(1), pp. 69-77. doi: 10.21608/ejars.2025.434903
M., E., K., M., R., Y., S., A., M., W., H., M., M., D. EGYPTIAN ARTIFACTS’ MATERIALS CLASSIFIER BASED ON LIGHTWEIGHT DEEP LEARNING ARCHITECTURES. Egyptian Journal of Archaeological and Restoration Studies, 2025; 15(1): 69-77. doi: 10.21608/ejars.2025.434903
EGYPTIAN ARTIFACTS’ MATERIALS CLASSIFIER BASED ON LIGHTWEIGHT DEEP LEARNING ARCHITECTURES
1School of Information Technology & Computer Science, Nile Univ., Giza, Egypt.
2Wireless Intelligent Networks Center (WINC), Nile Univ., Giza, Egypt. School of Engineering & Applied Sciences, Nile Univ., Giza, Egypt.
3Wireless Intelligent Networks Center (WINC), Nile Univ., Giza, Egypt.
4School of Engineering & Applied Sciences, Nile Univ., Giza, Egypt.
5Electronics & Communications Engineering dept., Cairo Univ, Giza, Egypt. Nanotechnology & Nanoelectronics Program, University of Science and Technology, Zewail City, Giza, Egypt.
Abstract
Artificial Intelligence (AI) plays a crucial role in cultural heritage by enabling the analysis, preservation, and restoration of artifacts and historical documents. Most of these applications may require to be used on devices with limited resources which leads to the need to use lightweight models. This study employs lightweight deep learning models, MobileNet V3 and ResNet-50, to classify Egyptian artifacts based on seven different materials. The models are trained on a dataset of 10.274 images. MobileNet achieves a training accuracy of 99.6% and a validation accuracy of 78.75%, while ResNet-50 achieves 96.62% and 83.23%, respectively. This research represents a novel contribution as previous studies have not specifically add-ressed the classification of materials in Egyptian artifacts. Such advancements highlight AI's potential in making cultural heritage more accessible and enhancing historical under-sta-nding.
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