# TextileNet: A Material Taxonomy-based Fashion Textile Dataset

arXiv: https://arxiv.org/abs/2301.06160 · HTML: https://mlsanity.com/paper/2301.06160/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 16 of its 72 references that are in the map.
- First posted on arXiv 15 Jan 2023.

## Builds on (most cited first, 16 of 16)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale](https://mlsanity.com/paper/2010.11929/) (2020)
- [Fashion-MNIST: a Novel Image Dataset for Benchmarking Machine Learning Algorithms](https://mlsanity.com/paper/1708.07747/) (2017)
- [VITON: An Image-based Virtual Try-on Network](https://mlsanity.com/paper/1711.08447/) (2017)
- [DeepFashion2: A Versatile Benchmark for Detection, Pose Estimation, Segmentation and Re-Identification of Clothing Images](https://mlsanity.com/paper/1901.07973/) (2019)
- [CLOTH3D: Clothed 3D Humans](https://mlsanity.com/paper/1912.02792/) (2019)
- [The Dataset Nutrition Label: A Framework To Drive Higher Data Quality Standards](https://mlsanity.com/paper/1805.03677/) (2018)
- [Material Recognition in the Wild with the Materials in Context Database](https://mlsanity.com/paper/1412.0623/) (2014)
- [Open-set Label Noise Can Improve Robustness Against Inherent Label Noise](https://mlsanity.com/paper/2106.10891/) (2021)
- [Fashionpedia: Ontology, Segmentation, and an Attribute Localization Dataset](https://mlsanity.com/paper/2004.12276/) (2020)
- [Cross-domain Image Retrieval with a Dual Attribute-aware Ranking Network](https://mlsanity.com/paper/1505.07922/) (2015)
- [The iMaterialist Fashion Attribute Dataset](https://mlsanity.com/paper/1906.05750/) (2019)
- [Generating Datasets of 3D Garments with Sewing Patterns](https://mlsanity.com/paper/2109.05633/) (2021)
- [ModaNet: A Large-Scale Street Fashion Dataset with Polygon Annotations](https://mlsanity.com/paper/1807.01394/) (2018)
- [Textile Taxonomy and Classification Using Pulling and Twisting](https://mlsanity.com/paper/2103.09555/) (2021)

## Built on by (most cited first, 3 of 3)

- [TouchAI: Exploring human-AI perceptual alignment in touch through language model representations](https://mlsanity.com/paper/2406.06587/) (2024)
- [LLM-Mediated Domain-Specific Voice Agents: The Case of TextileBot](https://mlsanity.com/paper/2406.10590/) (2024)
- [Low-Frequency Shortcuts in Texture-Driven Visual Learning](https://mlsanity.com/paper/2606.03493/) (2026)

## Abstract (arXiv)

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.