## 5 Assessing Dataset Availability and Practicality
### Sorting Datasets  
The sorting of textiles and garments is a critical component in the reuse and recycling process, with the introduction of AI technology aiming to enhance efficiency and speed. Various datasets have been developed to facilitate the sorting of textiles, leveraging advanced technologies such as machine vision and near-infrared spectroscopy. These datasets are crucial for training and evaluating sorting systems, ensuring they can handle the complexities of textile waste, including variations in garment or item type, fiber type, color, construction, and condition.  
[PERSON] et al. [15] used a sample set of 123 pieces and a validation set of 154 pieces, consisting of both uniformly and multicolored textiles pre-sorted into six color categories (black, grey, red, white, blue, and 'nature'). These textiles were manually pre-sorted by a waste textile sorting company. A critical review of the sample details would note the manual pre-sorting process, which introduces human error and subjectivity. The study's reliance on manual sorting highlights a gap in automated sorting technologies, crucial for scaling up recycling efforts. Additionally, while the inclusion of both uniformly and multicolored textiles broadens the scope, it presents a challenge for consistency in sorting due to the inherent complexity of multicolored textiles. The sample size is moderate, allowing for some level of statistical analysis, but it may not fully represent the vast diversity of textile waste.  
Various datasets have also been developed to identify the fiber content of textiles and garments, aiding in sorting for the purpose of recycling. For instance, [PERSON] et al. [27] used 7000 garment images developed as part of a previous study [63] for detecting the percentage of cotton in textile samples. Similarly, [PERSON] et al. [16] collected near-infrared (NIR) spectra for six different textile fibers, including cotton, viscose, acrylic, polyamide, polyester, and cotton-viscose blends for textile fabric classification. Once again, no other textile characterization details were provided (e.g., structure, thickness, or weight), and during testing parameters, it was specified that \"a few\" specimens were layered to prevent light leakage. In another study, 840 images of fabrics from the Contrado and The Textile District were collected at 50-100\(\times\) magnification via a microscope attached to a smartphone [21]. The fabrics included categories according to tradename, such as Lycra, as well as fabric structure, i.e., crepe, jersey, and velvet, as well as fiber type, i.e., cotton-linen blend. These images are publicly available on Kaggle. Furthermore, [PERSON] et al. [26] established an online NIR spectral library, including 13 types of waste textiles identified only by fiber type, e.g., polyester, cotton, wool, and various blends such as silk/cotton and nylon/'spandex'. This library supports the development of NIR-based sorting systems by providing spectral data for different textile compositions. Similarly, another study [24] examined 370 commercially supplied textile samples, using NIR spectroscopy combined with deep learning to classify pure fibers and binary mixtures based on the fiber content information provided by the manufacturers.