Bounding box annotation and dataset validation to train and improve automated stock detection models — enabling data-driven restocking decisions for retail operations at scale.
Workgen Solutions worked on a structured bounding box annotation and dataset validation project using LabelBox — a leading data labeling platform — to support the development and accuracy improvement of automated cooler refill detection models.
The objective was to build clean, accurately labeled image datasets that automation models could use to reliably identify product restocking levels inside retail coolers, reducing dependence on manual verification and enabling smarter, data-driven restocking workflows.
Each cooler image was precisely annotated with bounding boxes and classified into one of three stock-level categories, producing a high-quality, structured dataset ready for model training and validation.
Every annotated cooler image was assigned one of three clearly defined stock-level categories to ensure model training data was unambiguous and consistent.
Cooler shelves are completely stocked with products. No visible gaps or empty slots. Products are standing upright and neatly arranged.
Cooler shelves are partially stocked. Visible gaps exist but some product remains. Restocking is needed but not urgent depending on level.
Cooler shelves are bare or near-empty. Immediate restocking is required. This classification triggers priority restocking workflows.
Cooler refill images were ingested into LabelBox and organized into structured batches for annotation. Each image was associated with metadata including store location and shift context.
Each cooler unit and relevant product region within the image was precisely annotated using bounding boxes — defining the exact area the model should evaluate for stock level detection.
Each annotated region was assigned one of three stock-level labels — Full, Partial, or Empty — based on clearly defined visual criteria, ensuring consistent labeling across the entire dataset.
Completed annotations underwent structured quality validation to identify and correct mislabeled samples, poor bounding box alignment, or ambiguous classifications — ensuring model-ready dataset accuracy.
Validated datasets were exported in structured formats compatible with the client’s automation pipeline, providing clean, labeled training data to improve cooler refill detection model accuracy.
Industry-standard data labeling platform used for bounding box annotation, dataset management, and quality review
Precise rectangular region annotation to define cooler and product boundaries for automated model detection
Validated datasets exported in structured formats for direct integration into client automation model training pipelines
Clean, validated annotation datasets directly improved the performance of automated cooler stock detection systems.
Higher quality labeled datasets directly improved the accuracy of automated cooler refill stock detection models
Automation models trained on clean data required significantly less manual review and correction in downstream operations
Enabled smarter, classification-driven restocking decisions by providing reliable Full/Partial/Empty detection at scale
Automation-powered monitoring improved visibility into real-time cooler stock levels across retail locations
This project demonstrates Workgen Solutions' capability to deliver production-grade ML annotation datasets using industry tools like LabelBox. By applying structured bounding box annotation and rigorous dataset validation, our team created the clean, consistent labeled data that automation models need to perform reliably — directly enabling smarter retail execution at scale.