# RE-Tagger: A light-weight Real-Estate Image Classifier

Source: [RE-Tagger: A light-weight Real-Estate Image Classifier](<https://www.sota2.com/research/paper/68fa6f921e209131cea46541>)

Collection index: [llms.txt](<https://www.sota2.com/research/llms.txt>)

Data updated: 2025-10-26T01:22:26.639Z

Authors: Prateek Chhikara, Anil Goyal, Chirag Sharma

Published: 2022-07-12T17:16:06.000Z

## Abstract

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.

## Recorded benchmark results

A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage.

## Original sources and implementation

[Original paper](<https://arxiv.org/abs/2207.05696>)

[PDF](<https://arxiv.org/pdf/2207.05696.pdf>)