# Hotels-50K: A Global Hotel Recognition Dataset

## TL;DR

This paper introduces Hotels-50K, a large-scale dataset of over 1 million hotel room images from 50,000 hotels, aimed at improving hotel recognition for human trafficking investigations, and provides baseline methods for this challenging task.

## Contribution

The creation of the Hotels-50K dataset, comprising diverse real-world hotel images, and the development of baseline recognition approaches tailored to this domain.

## Key findings

- Dataset contains over 1 million images from 50,000 hotels.
- Baseline models achieve initial performance benchmarks.
- Data augmentation improves recognition accuracy.

## Abstract

Recognizing a hotel from an image of a hotel room is important for human trafficking investigations. Images directly link victims to places and can help verify where victims have been trafficked, and where their traffickers might move them or others in the future. Recognizing the hotel from images is challenging because of low image quality, uncommon camera perspectives, large occlusions (often the victim), and the similarity of objects (e.g., furniture, art, bedding) across different hotel rooms. To support efforts towards this hotel recognition task, we have curated a dataset of over 1 million annotated hotel room images from 50,000 hotels. These images include professionally captured photographs from travel websites and crowd-sourced images from a mobile application, which are more similar to the types of images analyzed in real-world investigations. We present a baseline approach based on a standard network architecture and a collection of data-augmentation approaches tuned to this problem domain.