With the increasing complexity of deep neural networks and continual architectural improvements, AI models have achieved remarkable success in image classification, surpassing 90% TOP-1 accuracy on ImageNet. Such high performance highlights their effectiveness across diverse domains and supports reliable transfer learning for smaller, specialized datasets. Artifact classification is one example where knowledge transfer from large-scale datasets proves highly beneficial. In (a), a pretrained CNN from ImageNet provides powerful feature extraction for archaeological image classification. In (b), this transferred knowledge is further extended to multi-image classification tasks through optimized feature fusion, enhancing overall model performance.