RE-Tagger: A light-weight Real-Estate Image Classifier
This addresses the problem of manual annotation for real-estate images, offering a domain-specific solution that is incremental in nature.
The paper tackled real-estate image classification by proposing RE-Tagger, a two-stage transfer learning pipeline using a custom InceptionV3 architecture, achieving classification into categories like bedroom and bathroom, and deployed it as a REST API on a 2-core, 2 GB RAM machine.
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.