DLAICVJan 15, 2023

TextileNet: A Material Taxonomy-based Fashion Textile Dataset

arXiv:2301.06160v110 citationsh-index: 39
Originality Synthesis-oriented
AI Analysis

This dataset addresses a bottleneck in the fashion industry for applications like design, retail, and recycling, though it is incremental as it introduces a new dataset rather than a novel method.

The authors tackled the challenge of automatically identifying textile materials for garments by creating TextileNet, the first fashion textile dataset based on material taxonomies, containing 760,949 images across 33 fibre and 27 fabric labels, and established baselines using CNNs and ViTs.

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 Convolutional Neural Networks (CNNs) and Vision Transformers (ViTs) to establish baselines for this dataset. Future applications for this dataset range from textile classification to optimization of the textile supply chain and interactive design for consumers. We envision that this can contribute to the development of a new AI-based fashion platform.

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