We developed a product recognition system using machine learning that identifies cosmetic products without requiring barcodes. Many beauty products either don’t have visible barcodes or they’re hidden inside packaging, so this was a real game-changer for the user experience.
The system uses a custom-trained convolutional neural network that can recognize products in real-time on mobile devices. It works even with partial product views and various lighting conditions, handling thousands of SKUs with high accuracy. The architecture is optimized for on-device inference, so it’s fast and works offline once the model is downloaded.
We integrated it with a product database and inventory system, and added a gamification layer where users earn stars for scanning products. They can exchange these stars for vouchers and products, which significantly improved engagement.
The technical implementation involved mobile-first architecture, integration with backend systems for the product catalog, and careful optimization to make the inference run smoothly on mid-range devices. We built it with TensorFlow/PyTorch for the ML components, standard mobile development frameworks for Android/iOS, and REST APIs for the backend communication.
