Midi Skirt Style Try-On Dataset

In the current retail e-commerce industry, consumers have increasing demands for online shopping experiences, especially for clothing try-ons. Traditional e-commerce platforms lack realism, leading to reduced user purchase intent. Existing solutions mostly rely on static image displays, unable to provide an interactive experience, making it difficult for users to judge the actual effect of clothing. The Midi Skirt Style Try-On Dataset aims to use synthetic image technology to provide a realistic try-on effect, meeting users' needs for personalized shopping. The dataset structure includes model images, background images, and corresponding style tags. Data collection is primarily carried out through high-quality photographic equipment in diverse environments to ensure authenticity and diversity of images. For quality control, a multi-round annotation and expert review process is adopted to ensure the accuracy and consistency of data. Data is stored in JPEG format and organized by ID for easy retrieval and use. The core advantage of this dataset lies in high annotation accuracy and consistency, with an annotation accuracy rate exceeding 95%. At the same time, innovative synthesis methods and data augmentation technologies are used to make the generated images richer in style expression, effectively enhancing users' online try-on experience and increasing conversion rates, with an expected increase of 10%-15% in sales.