  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.
