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

arXiv: https://arxiv.org/abs/2205.13060 · HTML: https://mlsanity.com/paper/2205.13060/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 7 of its 40 references that are in the map.
- First posted on arXiv 25 May 2022.

## Builds on (most cited first, 7 of 7)

- [EfficientDet: Scalable and Efficient Object Detection](https://mlsanity.com/paper/1911.09070/) (2019)
- [Scaled-YOLOv4: Scaling Cross Stage Partial Network](https://mlsanity.com/paper/2011.08036/) (2020)
- [Precise Detection in Densely Packed Scenes](https://mlsanity.com/paper/1904.00853/) (2019)
- [Who Needs MLOps: What Data Scientists Seek to Accomplish and How Can MLOps Help?](https://mlsanity.com/paper/2103.08942/) (2021)
- [A Data Quality-Driven View of MLOps](https://mlsanity.com/paper/2102.07750/) (2021)
- [A Solution to Product detection in Densely Packed Scenes](https://mlsanity.com/paper/2007.11946/) (2020)
- [Benchmark for Generic Product Detection: A Low Data Baseline for Dense Object Detection](https://mlsanity.com/paper/1912.09476/) (2019)

## Built on by (most cited first, 1 of 1)

- [OOS-DSD: Improving Out-of-stock Detection in Retail Images using Auxiliary Tasks](https://mlsanity.com/paper/2510.16508/) (2025)

## Abstract (arXiv)

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.