Fashion color trends are an essential marketing element that directly affect brand sales. Organizations such as Pantone have global authority over professional color standards by annually forecasting color palettes. However, the question remains whether fashion designers apply these colors in fashion shows that guide seasonal fashion trends. This study analyzed image data from fashion collections through machine learning to obtain measurable results by web-scraping catwalk images, separating body and clothing elements via machine learning, defining a selection of color chips using k-means algorithms, and analyzing the similarity between the Pantone color palette (16 colors) and the analysis color chips. The gap between the Pantone trends and the colors used in fashion collections were quantitatively analyzed and found to be significant. This study indicates the potential of machine learning within the fashion industry to guide production and suggests further research expand on other design variables.

时尚色彩趋势是直接影响品牌销售的重要营销元素。诸如Pantone之类的组织通过每年预测调色板来获得专业色彩标准的全球权威。然而，问题仍然是时装设计师是否在引导季节性时尚趋势的时装秀中使用这些颜色。这项研究通过机器学习对来自时装系列的图像数据进行了分析，从而获得了可测量的结果，包括通过网络抓取时装秀图像，通过机器学习将身体和衣服元素分离，使用k-means算法定义颜色芯片的选择以及分析Pantone之间的相似性。调色板（16种颜色）和分析色片。Pantone趋势与时装系列中使用的颜色之间的差距进行了定量分析，发现这一点很明显。