# DL-EWF: Deep Learning Empowering Women's Fashion with Grounded-Segment-Anything Segmentation for Body Shape Classification

arXiv: https://arxiv.org/abs/2404.04891 · HTML: https://mlsanity.com/paper/2404.04891/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 7 of its 31 references that are in the map.
- First posted on arXiv 7 Apr 2024.

## Builds on (most cited first, 7 of 7)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [Rethinking the Inception Architecture for Computer Vision](https://mlsanity.com/paper/1512.00567/) (2015)
- [Grounding DINO: Marrying DINO with Grounded Pre-Training for Open-Set Object Detection](https://mlsanity.com/paper/2303.05499/) (2023)
- [Fast Segment Anything](https://mlsanity.com/paper/2306.12156/) (2023)
- [ViBE: Dressing for Diverse Body Shapes](https://mlsanity.com/paper/1912.06697/) (2019)
- [Fashion is Taking Shape: Understanding Clothing Preference Based on Body Shape From Online Sources](https://mlsanity.com/paper/1807.03235/) (2018)
- [Human Body Shape Classification Based on a Single Image](https://mlsanity.com/paper/2305.18480/) (2023)

## Abstract (arXiv)

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.