  Analyzing fashion trends is essential in the fashion industry. Current
fashion forecasting firms, such as WGSN, utilize the visual information from
around the world to analyze and predict fashion trends. However, analyzing
fashion trends is time-consuming and extremely labor intensive, requiring
individual employees' manual editing and classification. To improve the
efficiency of data analysis of such image-based information and lower the cost
of analyzing fashion images, this study proposes a data-driven quantitative
abstracting approach using an artificial intelligence (A.I.) algorithm.
Specifically, an A.I. model was trained on fashion images from a large-scale
dataset under different scenarios, for example in online stores and street
snapshots. This model was used to detect garments and classify clothing
attributes such as textures, garment style, and details for runway photos and
videos. It was found that the A.I. model can generate rich attribute
descriptions of detected regions and accurately bind the garments in the
images. Adoption of A.I. algorithm demonstrated promising results and the
potential to classify garment types and details automatically, which can make
the process of trend forecasting more cost-effective and faster.
