Lightweight Vision-Language Model for Fashion Design in IoT Environment

With the rapid development of the Internet of Things (IoT), the demand for personalized fashion design in the IoT environment is growing, and fashion recommendation has gradually become a new research hotspot. However, existing fashion recommendation methods are often designed based on a single modality and contain a large number of parameters, making them unable to be effectively deployed on IoT edge devices with limited computing ability. Inspired by this, this paper proposes a novel personalized fashion color recommendation (FashionCR) framework based on a lightweight large vision-language model for fashion design in the IoT environment. Specifically, this framework consists of an IoT-based fashion color recommendation system and the FashionCR model. The recommendation system mainly introduces how to train the FashionCR model and deploy it to the edge devices. The FashionCR model leverages the visual branch of the CLIP model to accurately learn the physiological features of different individuals, such as skin color and face shape, and utilizes the text branch to efficiently process the text intentions input by users. Meanwhile, in order to meet the limited resources of the IoT environment, a lightweight modification has been implemented to the CLIP model. In addition, the 4-season color theory is integrated into the FashionCR framework to achieve accurate color recommendation. Experimental results show that this framework performs excellently in various metrics, providing a new solution for the field of fashion design in the IoT environment and effectively improving the accuracy and personalization of color recommendation.