Streamlining Retail Operations with Price Tag Recognition on Matrice
Price tag recognition is a key challenge in retail automation, enabling accurate extraction of pricing and product information from images for inventory management, pricing verification, and customer service. AI-powered computer vision can detect and classify price tags in real-time, streamlining operations in stores and e-commerce platforms.
This blog details how Matrice enables no-code AI deployment for price tag recognition using deep learning, covering:
Dataset Preparation
Dataset Annotation
Model Training
Model Evaluation
Model Inference
Model Deployment
Dataset Preparation
The dataset for this project comprises 10,000 high-resolution images of price tags in various retail settings, annotated to identify tag regions and extract relevant information (e.g., price, product code). The dataset is partitioned to support robust model development and evaluation:
Total Samples: 10,000
Training Set: 9,068
Validation Set: 603
Testing Set: 329
Model Training
We utilized a YOLO-based object detection model to detect and classify price tags in retail images. YOLOv8 was selected for its balance of speed and accuracy, ideal for identifying text-heavy price tags with varying fonts and layouts.
Model: YOLOv8 (multi-class object detection)
Batch Size: 16
Epochs: 90
Learning Rate: 0.001
Optimizer: Auto
Momentum: 0.95
Weight Decay: 0.0005
Model Evaluation
The trained model was evaluated on the test dataset, using key metrics to assess its effectiveness in detecting and classifying price tags.
Metric |
Value (Test) |
|---|---|
mAP@50 |
0.79 |
mAP@50-95 |
0.58 |
Recall |
0.77 |
Precision |
0.77 |
Model Inference
The trained model supports export to multiple formats, enabling deployment across various platforms, from in-store scanning systems to cloud-based retail analytics.
Supported formats include:
PyTorch (.pt)
ONNX
TensorRT
OpenVINO
This flexibility ensures compatibility with diverse deployment scenarios, such as point-of-sale systems, mobile apps, or warehouse inventory tools.
Model Deployment
Using Matrice, the trained model can be deployed seamlessly via a no-code interface. Matrice supports:
Real-time inference
API-based integration
Visual dashboards for monitoring
Applications include:
Automated price verification in retail stores
Smart inventory and shelf management
Enhanced customer service through mobile apps
Conclusion
AI-powered price tag recognition significantly reduces manual effort in retail operations, improving pricing accuracy and inventory efficiency. With Matrice, deploying such solutions is effortless, enabling faster implementation and reduced operational costs. By leveraging YOLOv8 and a comprehensive dataset, you can develop high-performing models to transform retail with visual intelligence.
Think CV, Think Matrice
Experience 40% faster deployment and slash development costs by 80%