Exploring databases for training models in machine learning in the Fashion industry
DOI:
https://doi.org/10.29147/datjournal.v9i2.877Keywords:
Machine Learning in Fashion, Artificial intelligence, Technological innovation, Data base, Supervised learningAbstract
Growing interest in applying machine learning (ML) to fashion highlights the importance of using labeled data to develop models, facilitating research replication, and automating the analysis of new data, such as fashion show images available online. Despite this need, few studies, especially in Brazil, methodologically explore the intersection between fashion and AM. This research aims to provide an overview of online databases for training ML models. A systematic review identified 26 articles that use these databases, such as Fashion-MNIST and DeepFashion2. Content analysis revealed that these databases, including Polyvore and Fashion Image Dataset, have diverse applications, highlighting the transformative potential of AM in fashion and encouraging innovations in design, production, and marketing in the fashion industry.
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