The opportunity

Our approach

Value created

• Our YOLO26s model outperformed ResNet-50 benchmarks for densely packed items.
• We achieved a 20% boost in accuracy: mAP@50-95 0.59 (vs 0.49), Recall 0.83 (vs 0.55), and Precision 0.90 (vs 0.83). This enables automated “share-of-shelf” analysis and instant planogram tracking with minimal human error.
• By delivering a scalable detection engine, we’ve built the foundation for the upcoming SKU Classification phase.

Technologies Used