StreetStyle: Exploring world-wide clothing styles from millions of photos

TL;DR

This paper presents a large-scale framework for analyzing worldwide clothing styles using millions of social media images, revealing global and local fashion trends over time.

Contribution

It introduces a new large-scale dataset with clothing annotations, deep learning classifiers, and a method for discovering style clusters across a massive image collection.

Findings

Identified global fashion trends and regional style differences.

Developed a scalable visual discovery framework for fashion analysis.

Provided insights into temporal evolution of clothing styles.

Abstract

Each day billions of photographs are uploaded to photo-sharing services and social media platforms. These images are packed with information about how people live around the world. In this paper we exploit this rich trove of data to understand fashion and style trends worldwide. We present a framework for visual discovery at scale, analyzing clothing and fashion across millions of images of people around the world and spanning several years. We introduce a large-scale dataset of photos of people annotated with clothing attributes, and use this dataset to train attribute classifiers via deep learning. We also present a method for discovering visually consistent style clusters that capture useful visual correlations in this massive dataset. Using these tools, we analyze millions of photos to derive visual insight, producing a first-of-its-kind analysis of global and per-city fashion choices and spatio-temporal trends.