An Instance Segmentation Model to Categorize Clothes from Wild Fashion Images

Abstract

Categorizing of clothes from 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 often grainy, unfocused, with people in different poses is a challenge. This research proposes a comparison between object detection and instance segmentation based models to categorise clothes from wild fashion images. The Object detection model is implemented using Faster Region-Based Convolutional Neural Network (RCNN). Mask RCNN is used to implement an instance segmentation model. We have trained the models on standard benchmark dataset namely deepfashion2. Results demonstrate that Instance Segmentation models such as Mask RCNN outperforms Object Detection models by 20%. Mask RCNN achieved 21.05% average precision, 73% recall across the different IoU (Intersection over Union). These results show promise for using Instance Segmentation models for faster image retrieval based e-commerce applications.