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.

利用数字成像和深度神经网络（DNN）方法，本研究利用 Style4BodyShape 数据集为了将身体形状分类为五个类别：矩形、三角形、倒三角形、沙漏形和苹果形。从图像中提取出个人的身体形状，并使用各种预训练模型对分割结果进行分类，结果表明 Inception V3 模型在 f1-score 评估指标和准确性方面表现优异。