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.
翻译:机器学习(ML)的崛起正在逐渐数字化,并改造时装产业。近年来,出现了一些时装AI应用,例如虚拟试镜。纺织品物料的识别和分类在时装纺织部门发挥着关键的作用,包括时装设计、零售和再循环。与此同时,净零是一个全球目标,时装业正在经历重大变革,以便纺织品材料能够以可持续的方式再利用、维修和再循环。在为服装自动识别纺织品材料方面仍然存在挑战,因为我们缺乏低成本和有效的识别技术。鉴于此,我们根据纺织品材料分类(包括时装设计、零售和再循环),建立了第一个时装纺织品数据集(TextileNet),在时装纺织品部门中发挥着关键作用。与此同时,净零是一个全球目标,而时装工业正在经历着巨大的变化,因此,纺织材料业正在以可持续的方式进行培训和评估。我们希望通过使用基于税制的分类,使与纺织品有关的数据集标准化。 纺织品网络包含33个纤维标签和27个纤维CN标签,并在760、949年的时装纺织品数据库中,我们利用了这一模型模型模型的模型模型模型模型模型模型模型模型模型模型模型的模型设计,从而建立这一模型的模型和模型的模型模型模型的模型的模型,从而可以建立。