Automated Inventory management system using Machine Learning

By automating in-store activities and delivering real-time shelf metrics, this solution transforms data collection and analysis, resulting in improved sales performance and elevated customer satisfaction.

Scope

The project involves deploying Artificial Intelligence and machine learning to manage and monitor in-store product placement and stock levels. Key components include counting products on shelves to assess stock levels, verifying compliance with the planogram, and detecting empty spaces on shelves. The system focuses on automating these tasks to ensure products are correctly positioned and to help maintain optimal inventory levels.

Our Approach 

We leveraged advanced computer vision and machine learning technologies to create a robust in-store product management solution. By using OpenCV and the YOLO (You Only Look Once) framework, we achieved accurate product counting and placement detection. Vision Transformers further enhanced our image recognition capabilities, allowing for more effective analysis of visual data. Additionally, we set up a streamlined pipeline for model training, inferencing, and experiment tracking, resulting in a production-ready system with automated processes for ongoing improvement and real-time monitoring.