论文标题
重新调味：轻巧的房地产图像分类器
RE-Tagger: A light-weight Real-Estate Image Classifier
论文作者
论文摘要
房地产图像标记是节省手动注释并增强用户体验的努力的重要用例之一。本文为房地产图像分类问题提出了一条端到端管道（称为重新调用）。我们使用Custom InceptionV3体系结构提出了两阶段的转移学习方法，将图像分为不同类别（即卧室，浴室，厨房，阳台，厅等）。最后，我们以REST API为托管时发布了该应用程序，该应用程序是在2个带有2 GB RAM的内核机上运行的Web应用程序。演示视频可在此处使用。
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.
