# Bag of Tricks for Retail Product Image Classification

arXiv: https://arxiv.org/abs/2001.03992 · HTML: https://mlsanity.com/paper/2001.03992/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 12 Jan 2020.

## Built on by (most cited first, 5 of 5)

- [RetailKLIP : Finetuning OpenCLIP backbone using metric learning on a single GPU for Zero-shot retail product image classification](https://mlsanity.com/paper/2312.10282/) (2023)
- [Using Contrastive Learning and Pseudolabels to learn representations for Retail Product Image Classification](https://mlsanity.com/paper/2110.03639/) (2021)
- [Machine Learning approaches to do size based reasoning on Retail Shelf objects to classify product variants](https://mlsanity.com/paper/2110.03783/) (2021)
- [Classification of Tents in Street Bazaars Using CNN](https://mlsanity.com/paper/2506.17946/) (2025)
- [What Matters for Grocery Product Retrieval with Open Source Vision Language Models](https://mlsanity.com/paper/2605.18029/) (2026)

## Abstract (arXiv)

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.