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SOTA model for high-precision product identification

Retail DevelopmentUSA

Company

ShelfSet LLC

Industry

Retail

Country of the Company

USA

Type of Service

Development

The opportunity

  • The client required a high-performance Object Detection model to replace their existing pipeline, which was limited by licensing and accuracy issues.
  • The primary goal was to develop a custom SOTA model capable of identifying products in complex retail environments with high precision, specifically for on-shelf drink identification and planogram monitoring.

Our approach

  • We developed a state-of-the-art model from scratch, training it on diverse, high-complexity datasets to ensure 20% better performance than existing market alternatives.
  • Our work focused on the Data Pipeline, Core Detection Model, and Model Optimization.
  • We delivered the final codebase and repository under an MIT license, optimized for further integration into mobile or web environments.

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

SOTA Object Detection Architectures

PyTorch for training & experimentation

MLflow

TFLite

Albumentations

Albumentations

Model quantization

Company

ShelfSet LLC

Industry

Retail

Country of the Company

USA

Type of Service

Development

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