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Revolutionizing Cellar Management with IoT: A Case Study on CellarEye, Inc.
Technology Category
- Analytics & Modeling - Computer Vision Software
- Functional Applications - Inventory Management Systems
Applicable Industries
- Food & Beverage
Applicable Functions
- Warehouse & Inventory Management
Use Cases
- Computer Vision
- Object Detection
The Challenge
CellarEye, Inc. is a company that aims to revolutionize the management of private and professional wine collections by leveraging state-of-the-art Computer Vision (CV) and Artificial Intelligence (AI) technologies. Their goal is to provide a seamless management system that automatically tracks each wine bottle in a cellar, storing both the brand and location into inventory tools without manual entries. However, the team at CellarEye faced a significant challenge in realizing their vision. They needed to develop a reliable object detection model to recognize and track wine bottles as they were registered to and removed from the inventory. The cellar environment, with its thousands of wine bottles, presented a complex scenario with numerous edge cases. The company initially struggled with bad or inconsistent annotations, which made achieving an accuracy rate of over 80% a challenge. They needed a better way to detect problems with their data, understand their model failures, and enable their Machine Learning (ML) team to collaborate with their annotation team to catch labeling mistakes faster.
About The Customer
CellarEye, Inc. is a company that is dedicated to revolutionizing the experience of managing private and professional wine collections. They aim to make wine more accessible to more people by providing a better and more widespread understanding of wine. To achieve this, they leverage state-of-the-art Computer Vision (CV) and Artificial Intelligence (AI) technologies to offer a seamless management system. This system automatically tracks each wine bottle in a cellar, storing both the brand and location into inventory tools without the need for manual entries. The company is committed to overcoming the challenges presented by the complex cellar environment and the numerous edge cases that arise from managing thousands of wine bottles.
The Solution
To overcome their challenges, the CellarEye team turned to Scale Nucleus. This tool allowed them to identify where their models were underperforming and where labels were likely to be incorrect. With Nucleus, CellarEye’s team could query their labeled and unlabeled data on custom metadata, visualize model predictions alongside ground truth annotations, and sort results by error metrics like Intersection Over Union (IoU). They could also look at high-confidence false positive predictions, and fix and export corrected labels using Nucleus’s built-in annotation editor. This solution provided a more efficient and accurate way to manage the complex task of tracking and managing the wine bottles in a cellar. By integrating Scale Nucleus into their system, CellarEye was able to significantly improve the accuracy and reliability of their object detection model, thereby enhancing their cellar management solution.
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