Fashion trends today are changing much faster than ever before. Timely and reliable trend forecasting is, therefore, critical in the fashion industry. Traditional fashion forecasting requires professionals to abstract image-based information across design collections and time intervals from around the world, which is extremely time-consuming and labor intensive. Considering the financial cost associated with manual labeling and the accuracy of classifications based upon human subjective judgment, this explorative study proposes a data-driven quantitative abstracting approach using an artificial intelligence (A.I.) algorithm. Firstly, an A.I. model was trained to be familiar with fashion images from a large-scale dataset under different scenarios such as online stores and street snapshots; secondly, the model could detect garments and classify clothing attributes such as fabric textures, garment style, and design details from runway photos and videos; thirdly, the model could summarize fashion trends from the attributes it developed. The adoption of an A.I. algorithm proved to be an objective and systematic computerized method of interpreting fashion dynamics in a more efficient, accurate, sustainable, and cost-effective way.
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