AI SystemsAI Platform2026
LiquorScan — High-Precision Liquor Fill Level Detection Powered by Hybrid Semantic Segmentation
- PyTorch
- YOLO
- FastAPI
- Computer Vision
- Streamlit
- OpenCV
- AWS
- Python
The challenge
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.
What we built
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.
Systems delivered
- Computer Vision Core
- Hybrid Semantic Segmentation Pipeline
- Multi-Perspective Mask Quality System
- FastAPI Backend Service
- Streamlit Interface
Outcome
Automated liquor inventory checks in ~9–10 seconds with high precision across 16 segmented classes, eliminating subjective manual visual estimations.
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