Papers › EPYNET: Efficient Pyramidal Network for Clothing Segmentation

EPYNET: Efficient Pyramidal Network for Clothing Segmentation

13 Oct 2020IEEE Access 2020 10archive 2025-07-28

Andrei De Souza Inácio, Heitor Silvério Lopes

Soft biometrics traits extracted from a human body, including the type of clothes, hair color, and accessories, are useful information used for people tracking and identification. Semantic segmentation of these traits from images is still a challenge for researchers because of the huge variety of clothing styles, layering, shapes, and colors. To tackle these issues, we proposed EPYNET, a framework for clothing segmentation. EPYNET is based on the Single Shot MultiBox Detector (SSD) and the Feature Pyramid Network (FPN) with the EfficientNet model as the backbone. The framework also integrates data augmentation methods and noise reduction techniques to increase the accuracy of the segmentation. We also propose a new dataset named UTFPR-SBD3, consisting of 4,500 manually annotated images into 18 classes of objects, plus the background. Unlike available public datasets with imbalanced class distributions, the UTFPR-SBD3 has, at least, 100 instances per class to minimize the training difficulty of deep learning models. We introduced a new measure of dataset imbalance, motivated by the difficulty in comparing different datasets for clothing segmentation. With such a measure, it is possible to detect the influence of the background, classes with small items, or classes with a too high or too low number of instances. Experimental results on UTFPR-SBD3 show the effectiveness of EPYNET, outperforming the state-of-art methods for clothing segmentation on public datasets. Based on these results, we believe that the proposed approach can be potentially useful for many real-world applications related to soft biometrics, people surveillance, image description, clothes recommendation, and others.

Code

No code repository is listed for this paper in the archive or in Syntology's graph.

Code Syntology ran Syntology

Not run by Syntology. Nothing on this page verifies that the listed code works.

Tasks

1 archive task tag without a task page not shown.

Results from the paper archive 2025-07-28

Ranks are positions in the archive's leaderboards as they stood at the 2025-07-28 snapshot. Results published since then are not among these rows, so a rank here is not a current standing.

Methods

Introduced by this paper: IPBI

Report a problem or propose a change · a person checks every report against the paper or source before anything changes; decisions are listed on /corrections