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TextileNet: A Material Taxonomy-based Fashion Textile Dataset by Zhong, Shu; Ribul, Miriam; Cho, Youngjun; Obrist, Marianna is a scholarly article available to read on EtoBox.

What is TextileNet: A Material Taxonomy-based Fashion Textile Dataset about?

The rise of Machine Learning (ML) is gradually digitalizing and reshaping the fashion industry. Recent years have witnessed a number of fashion AI applications, for example, virtual try-ons. Textile material identification and categorization play a crucial role in the fashion textile sector, including fashion design, retails, and recycling. At the same time, Net Zero is a global goal and the fashion industry is undergoing a significant change so that textile materials can be reused, repaired and recycled in a sustainable manner. There is still a challenge in identifying textile materials automatically for garments, as we lack a low-cost and effective technique for identifying them. In light of this, we build the first fashion textile dataset, TextileNet, based on textile material taxonomies - a fibre taxonomy and a fabric taxonomy generated in collaboration with material scientists. TextileNet can be used to train and evaluate the state-of-the-art Deep Learning models for textile materials. We hope to standardize textile related datasets through the use of taxonomies. TextileNet contains 33 fibres labels and 27 fabrics labels, and has in total 760,949 images. We use standard Convol

Author

Zhong, Shu; Ribul, Miriam; Cho, Youngjun; Obrist, Marianna

Published

2023

Language

EN