Retail Vision Analytics
Computer vision for fashion retail with 92% accuracy achieving 90% reduction in manual data entry time
Technology Stack
PyTorchCNNFashion MNISTStreamlitBatch NormalizationDropout
ML Frameworks
Deep Learning
NLP
Deployment
ROI
Real-time inventory visibility
Accuracy
92.27%
Key Performance Indicators
Precision
0.92Recall
0.92Categories
10 fashion itemsBusiness Impact Summary
| Return on Investment | Real-time inventory visibility |
| Model Accuracy | 92.27% |
| Precision | 0.92 |
| Recall | 0.92 |
| Categories | 10 fashion items |
Overview
Production-grade computer vision platform for automated fashion inventory tagging with 92.27% accuracy. Custom CNN with batch normalization and dropout for robust classification across 10 fashion categories.
Live Demo
Live Demo
Interactive Streamlit application
📊
Retail Vision Analytics Demo
Click below to load the interactive demo
This is a live, interactive demo deployed on Streamlit Cloud. You can:
- •Upload your own data for predictions
- •Explore model performance metrics
- •View interactive visualizations
- •Understand model predictions with explainability
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