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Bag of Tricks for Retail Product Image Classification

  • Muktabh Mayank SrivastavaEmail author
Conference paper
  • 138 Downloads
Part of the Lecture Notes in Computer Science book series (LNCS, volume 12131)

Abstract

Retail Product Image Classification is an important Computer Vision and Machine Learning problem for building real world systems like self-checkout stores and automated retail execution evaluation. In this work, we present various tricks to increase accuracy of Deep Learning models on different types of retail product image classification datasets. These tricks enable us to increase the accuracy of fine tuned convnets for retail product image classification by a large margin. As the most prominent trick, we introduce a new neural network layer called Local-Concepts-Accumulation (LCA) layer which gives consistent gains across multiple datasets. Two other tricks we find to increase accuracy on retail product identification are using an instagram-pretrained Convnet and using Maximum Entropy as an auxiliary loss for classification.

Keywords

Convolutional Neural Networks Retail image recognition Grocery image recognition Image classification 

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Copyright information

© Springer Nature Switzerland AG 2020

Authors and Affiliations

  1. 1.ParallelDots, Inc.GurugramIndia

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