Designing an Efficient End-to-end Machine Learning Pipeline for Real-time Empty-shelf Detection

TL;DR

This paper introduces a real-time, efficient machine learning pipeline for detecting empty shelves in retail stores, emphasizing data quality and runtime optimization, achieving high processing speeds and a mean F1-score of 68.5%.

Contribution

The paper presents a comprehensive end-to-end ML pipeline for empty shelf detection, focusing on data quality, annotation, and runtime efficiency, with significant speed improvements.

Findings

Achieved a mean average F1-score of 68.5%.

Processed up to 67 images/sec on Intel Xeon Gold.

Processed up to 860 images/sec on A100 GPU.

Abstract

On-Shelf Availability (OSA) of products in retail stores is a critical business criterion in the fast moving consumer goods and retails sector. When a product is out-of-stock (OOS) and a customer cannot find it on its designed shelf, this motivates the customer to store-switching or buying nothing, which causes fall in future sales and demands. Retailers are employing several approaches to detect empty shelves and ensure high OSA of products; however, such methods are generally ineffective and infeasible since they are either manual, expensive or less accurate. Recently machine learning based solutions have been proposed, but they suffer from high computational cost and low accuracy problem due to lack of large annotated datasets of on-shelf products. Here, we present an elegant approach for designing an end-to-end machine learning (ML) pipeline for real-time empty shelf detection. Considering the strong dependency between the quality of ML models and the quality of data, we focus on the importance of proper data collection, cleaning and correct data annotation before delving into modeling. Since an empty-shelf detection solution should be computationally-efficient for real-time predictions, we explore different run-time optimizations to improve the model performance. Our dataset contains 1000 images, collected and annotated by following well-defined guidelines. Our low-latency model achieves a mean average F1-score of 68.5%, and can process up to 67 images/s on Intel Xeon Gold and up to 860 images/s on an A100 GPU.