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

Autor:	Chhikara, Prateek, Goyal, Anil, Sharma, Chirag
Rok vydání:	2022
Předmět:	Computer Science - Computer Vision and Pattern Recognition Computer Science - Machine Learning
Druh dokumentu:	Working Paper
DOI:	10.1007/978-3-031-26422-1_44
Popis:	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)
Databáze:	arXiv
Externí odkaz:	http://arxiv.org/abs/2207.05696 Zobrazit plný text záznamu View this record from Arxiv