Object detection and semantic segmentation of fashion images
Date Issued
2020-05-08
Author(s)
Treneska, Sandra
Abstract
Over the past few years, fashion brands have been
rapidly implementing computer vision into the fashion industry.
Our research objective was to analyse a number of methods
suitable for object detection and segmentation of apparel in
fashion images. Two types of models are proposed. The first,
simpler, is a convolutional neural network used for object
detection of clothing items on the Fashion-MNIST dataset and
the second, more complex Mask R-CNN model is used for
object detection and instance segmentation on the iMaterialist
dataset. The performance of the first proposed model reached
93% accuracy. Furthermore, the results from the Mask R-CNN
model are visualized.
rapidly implementing computer vision into the fashion industry.
Our research objective was to analyse a number of methods
suitable for object detection and segmentation of apparel in
fashion images. Two types of models are proposed. The first,
simpler, is a convolutional neural network used for object
detection of clothing items on the Fashion-MNIST dataset and
the second, more complex Mask R-CNN model is used for
object detection and instance segmentation on the iMaterialist
dataset. The performance of the first proposed model reached
93% accuracy. Furthermore, the results from the Mask R-CNN
model are visualized.
Subjects
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