Manual liquor inventory checks are slow and prone to error. Varied bottle shapes, labels, closures, and reflections heavily distorted single-view computer vision fill level segmentations.
A PyTorch hybrid semantic segmentation core (ResNet-101, FCN, PSPNet), a YOLO object detection pipeline, a multi-perspective mask quality scoring algorithm, and a stateless FastAPI app on AWS.
Automated liquor inventory checks in ~9–10 seconds with high precision across 16 segmented classes, eliminating subjective manual visual estimations.
Download the full case study to share with your team.
Have a similar product challenge? Let's talk about the right technical path.
Discuss a Similar Build