# Fashion and Apparel Classification using Convolutional Neural Networks

Source: [Fashion and Apparel Classification using Convolutional Neural Networks](<https://www.sota2.com/research/paper/68fa65161e209131cea082f7>)

Collection index: [llms.txt](<https://www.sota2.com/research/llms.txt>)

Data updated: 2025-10-25T21:18:52.954Z

Authors: Alexander Schindler, Thomas Lidy, Stephan Karner, Matthias Hecker

Published: 2018-11-11T09:18:32.000Z

## Abstract

We present an empirical study of applying deep Convolutional Neural Networks (CNN) to the task of fashion and apparel image classification to improve meta-data enrichment of e-commerce applications. Five different CNN architectures were analyzed using clean and pre-trained models. The models were evaluated in three different tasks person detection, product and gender classification, on two small and large scale datasets.

## Recorded benchmark results

A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage.

## Original sources and implementation

[Original paper](<https://arxiv.org/abs/1811.04374>)

[PDF](<https://arxiv.org/pdf/1811.04374.pdf>)