DL-EWF: Deep Learning Empowering Women's Fashion with
  Grounded-Segment-Anything Segmentation for Body Shape Classification
Fatemeh Asghari Mohammad Reza Soheili Faezeh Gholamrezaie
The global fashion industry plays a pivotal role in the global economy, and
addressing fundamental issues within the industry is crucial for developing
innovative solutions. One of the most pressing challenges in the fashion
industry is the mismatch between body shapes and the garments of individuals
they purchase. This issue is particularly prevalent among individuals with
non-ideal body shapes, exacerbating the challenges faced. Considering
inter-individual variability in body shapes is essential for designing and
producing garments that are widely accepted by consumers. Traditional methods
for determining human body shape are limited due to their low accuracy, high
costs, and time-consuming nature. New approaches, utilizing digital imaging and
deep neural networks (DNN), have been introduced to identify human body shape.
In this study, the Style4BodyShape dataset is used for classifying body shapes
into five categories: Rectangle, Triangle, Inverted Triangle, Hourglass, and
Apple. In this paper, the body shape segmentation of a person is extracted from
the image, disregarding the surroundings and background. Then, Various
pre-trained models, such as ResNet18, ResNet34, ResNet50, VGG16, VGG19, and
Inception v3, are used to classify the segmentation results. Among these
pre-trained models, the Inception V3 model demonstrates superior performance
regarding f1-score evaluation metric and accuracy compared to the other models.