# Neural Networks for Fashion Image Classification and Visual Search

Source: [Neural Networks for Fashion Image Classification and Visual Search](<https://www.sota2.com/research/paper/68fa678b1e209131cea16d25>)

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

Data updated: 2025-10-28T13:56:42.780Z

Authors: Fengzi Li, Shashi Kant, Shunichi Araki, Sumer Bangera, Swapna Samir Shukla

Published: 2020-05-17T05:25:41.000Z

## Abstract

We discuss two potentially challenging problems faced by the ecommerce industry. One relates to the problem faced by sellers while uploading pictures of products on the platform for sale and the consequent manual tagging involved. It gives rise to misclassifications leading to its absence from search results. The other problem concerns with the potential bottleneck in placing orders when a customer may not know the right keywords but has a visual impression of an image. An image based search algorithm can unleash the true potential of ecommerce by enabling customers to click a picture of an object and search for similar products without the need for typing. In this paper, we explore machine learning algorithms which can help us solve both these problems.

## 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/2005.08170>)

[PDF](<https://arxiv.org/pdf/2005.08170.pdf>)