Bag of Tricks for Retail Product Image Classification
This work addresses the problem of automated retail systems like self-checkout stores, but it appears incremental as it focuses on optimizing existing methods rather than a fundamental breakthrough.
The paper tackles retail product image classification by introducing several tricks, including a new Local-Concepts-Accumulation layer, which significantly improves accuracy across multiple datasets.
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.