Real-estate image tagging is one of the essential use-cases to save efforts
involved in manual annotation and enhance the user experience. This paper
proposes an end-to-end pipeline (referred to as RE-Tagger) for the real-estate
image classification problem. We present a two-stage transfer learning approach
using custom InceptionV3 architecture to classify images into different
categories (i.e., bedroom, bathroom, kitchen, balcony, hall, and others).
Finally, we released the application as REST API hosted as a web application
running on 2 cores machine with 2 GB RAM. The demo video is available here.Comment: European Conference on Machine Learning and Principles and Practice
of Knowledge Discovery in Databases (DEMO TRACK