Development of VGG-16 transfer learning framework for geographical landmark recognition

Computer vision mandates the development of landmark recognition paradigms for efficient Image Recognition. In this article, the concept of Visual Geometry Group Network (VGG-16) transfer learning is used to develop an AI model over a geographical landmarks' dataset. The dataset is a small part of Google Landmarks dataset V2 which originally consists of over 4M images. A VGG-16 model trained on ImageNet dataset is used to transfer knowledge. A positive transfer of knowledge is seen and it was observed that the trained model was a highly generalized model for our dataset. Not only a training accuracy of more than 0.85 is observed but equivalent validation accuracy suggests that the developed model is highly robust with minimal overfitting. A comparison of our proposed approach was made with classical machine learning techniques like KNN (K Nearest Neighbor), Decision Trees, Random Forest, SVM (Support Vector Machines) and a 3 Layered CNN. The results clearly depict that the proposed approach outperforms all other machine learning classifiers in consideration.