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.
@article{arxiv.1811.04374,
title = {Fashion and Apparel Classification using Convolutional Neural Networks},
author = {Alexander Schindler and Thomas Lidy and Stephan Karner and Matthias Hecker},
journal= {arXiv preprint arXiv:1811.04374},
year = {2018}
}
Comments
Proceedings of the 10th Forum Media Technology and 3rd All Around Audio Symposium, St. Poelten, Austria, November 29-30, 2017