$\mathtt {RE\text{- }Tagger}$: A Light-Weight Real-Estate Image Classifier

Prateek Chhikara, Anil Goyal, Chirag Sharma

ECML-PKDD 2022 pp. 627-630

doi:10.1007/978-3-031-26422-1_44 /ecmlpkdd/2022/chhikara2022ecmlpkdd-re/

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.

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Cite

Text

Chhikara et al. "$\mathtt {RE\text{- }Tagger}$: A Light-Weight Real-Estate Image Classifier." European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases, 2022. doi:10.1007/978-3-031-26422-1_44

Markdown

[Chhikara et al. "$\mathtt {RE\text{- }Tagger}$: A Light-Weight Real-Estate Image Classifier." European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases, 2022.](https://mlanthology.org/ecmlpkdd/2022/chhikara2022ecmlpkdd-re/) doi:10.1007/978-3-031-26422-1_44

BibTeX

@inproceedings{chhikara2022ecmlpkdd-re,
  title     = {{$\mathtt {RE\text{- }Tagger}$: A Light-Weight Real-Estate Image Classifier}},
  author    = {Chhikara, Prateek and Goyal, Anil and Sharma, Chirag},
  booktitle = {European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases},
  year      = {2022},
  pages     = {627-630},
  doi       = {10.1007/978-3-031-26422-1_44},
  url       = {https://mlanthology.org/ecmlpkdd/2022/chhikara2022ecmlpkdd-re/}
}