The world of photo sharing and storage is evolving rapidly, and the demand for location data associated with each uploaded image is ever-growing. This invaluable location information fuels advanced features such as automatic tagging suggestions and seamless photo organization, enhancing the user experience. However, a challenge arises when photos lack location metadata, a situation that frequently occurs due to various reasons, including privacy concerns and camera limitations.
To bridge this gap, we aim to infer the location of images by detecting and classifying visible landmarks within the photos. With countless landmarks across the globe and an enormous volume of images uploaded to these platforms, manual classification is simply not feasible.
In this project, I'm taking the initial steps to tackle this challenge head-on. The journey involves the end-to-end machine learning design process, starting with meticulous data preprocessing. I'll then dive into the design and training of Convolutional Neural Networks (CNNs) and evaluate the accuracy of different models. The ultimate goal is to develop an application based on the best-performing CNN I train. Let's embark on this exciting journey of image recognition and location prediction together!