Date: 2025-05-28
Smarter Retail with Footwear Detection and Classification on Matrice
Footwear detection and classification is a key challenge in retail automation, impacting inventory tracking, recommendation systems, and customer service. With computer vision powered by AI, footwear types can be accurately identified and localized in real-time, enabling faster and more efficient workflows in fashion and e-commerce platforms.
This blog details how Matrice enables no-code AI deployment for footwear classification using deep learning, covering:
Dataset Preparation
Dataset Annotation
Model Training
Model Evaluation
Model Inference
Model Deployment
Dataset Preparation
The dataset for this project includes 2,598 annotated images of various shoe types, such as sneakers, boots, sandals, and heels. It is divided to support optimal training and validation:
Total Samples: 2,598
Training Set: 2,044
Validation Set: 470
Testing Set: 84
Model Training
We employed a densenet object detection model to identify and classify footwear types in images.densenet selected for its balance of speed and accuracy, particularly effective for objects with subtle visual differences.
Model: densenet121
Batch Size: 4
Epochs: 50
Learning Rate: 0.001
Optimizer: Auto
Momentum: 0.95
Weight Decay: 0.0005
Performance Metrics
The trained model was evaluated on validation and test datasets, using metrics such as precision and recall to gauge effectiveness.
Metric |
Value (test) |
Value (Val) |
|---|---|---|
acc@1 |
0.25 |
0.091 |
Precision |
1 |
1 |
Model Inference
The trained model can be exported to multiple formats for deployment on diverse platforms, from cloud servers to edge devices.
Supported formats include:
PyTorch (.pt)
ONNX
TensorRT
OpenVINO
This versatility ensures compatibility across various real-world deployment scenarios, including in-store scanners, mobile apps, and warehouse systems.
Model Deployment
Using Matrice, you can seamlessly deploy the trained model via an intuitive no-code interface. Matrice supports:
Real-time inference
API-based integration
Visual dashboards for monitoring
Applications include:
Automated product identification in e-commerce
Smart inventory and shelf management
Customer personalization in fashion retail
Conclusion
AI-powered footwear detection and classification significantly reduces manual effort in retail and inventory tasks. With Matrice, deploying such solutions becomes effortless, enabling faster go-to-market and reduced operational costs.
By harnessing densenet121 structured 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%