NORMA eResearch @NCI Library

Categorization of Fashion Clothes from Wild Images using Object Detection and Segmentation based Models

Jadhav, Rohan Indrajeet (2022) Categorization of Fashion Clothes from Wild Images using Object Detection and Segmentation based Models. Masters thesis, Dublin, National College of Ireland.

[thumbnail of Master of Science]
PDF (Master of Science)
Download (1MB) | Preview
[thumbnail of Configuration manual]
PDF (Configuration manual)
Download (2MB) | Preview


Categorizing of clothes in the wild fashion images involves identifying the type of clothes a person wears from non-studio images such as a shirt, trousers, and so on. Identifying the fashion clothes from wild images that are grainy, unfocused, with people in different poses is a challenge. This research proposes the instance segmentation model outperforms compare to object detection model when it comes to clothes categorization from wild images. Faster RCNN is and object detection deep learning model and the segmentation model used is Mask RCNN which is based on RCNN (Region-Based Convolutional Neural Network). Both models are trained on the subset of DeepFashion2 dataset. The models are trained on 10k distinct fashion images using rich annotation files in file, 2k images are used for test purposes and 5k images for the validation. The results of two models presented in this paper based on the average precision, recall across the different IoU(Intersection over Union) metrics considering the clothes acquired in fashion image. Based on the results it is shown as the segmentation model Mask, the RCNN outperforms by 20% compared to object detection model Faster RCNN.

Item Type: Thesis (Masters)
Uncontrolled Keywords: Clothes Categorization; Segmentation; Object Detection; Faster-RCNN; MaskRCNN; Azure
Subjects: Q Science > QA Mathematics > Electronic computers. Computer science
T Technology > T Technology (General) > Information Technology > Electronic computers. Computer science
T Technology > TR Photography
H Social Sciences > HD Industries. Land use. Labor > Specific Industries > Fashion Industry
Q Science > Q Science (General) > Self-organizing systems. Conscious automata > Machine learning
Divisions: School of Computing > Master of Science in Data Analytics
Depositing User: Tamara Malone
Date Deposited: 26 Jan 2023 17:03
Last Modified: 03 Mar 2023 11:15

Actions (login required)

View Item View Item