AI-Powered Quality Control

Detect shoe defects
with precision

Shoe QC Inspector based Artificial Intelligence to classify defects in real time — capture, classify, and decide in seconds.

Live camera capture
Open your browser camera, point at the shoe, and capture as many photos as needed — right and left shoe separately.
AI defect classification
Each photo is instantly classified by deep learning model, showing defect type and confidence score.
Automatic reject decision
Defects are counted automatically. Accept or Reject is decided based on your configured threshold — no manual counting.
∞
Photos per inspection
2
Shoes per pair (R + L)
100%
Browser-based, no server needed
Deep Vision Research Group

The team behind this tool

We build AI-powered computer vision tools to solve real-world quality control challenges in the footwear industry.

EL
Eka Legya Frannita
Lecturer
Politeknik ATK YogyakartaFootwear technology and AI-based quality inspection systems.
AR
Alifia Revan Prananda
Lecturer
Universitas TidarMachine learning, computer vision, and defect detection systems.

Our mission

Deep Vision Research Group was established to bridge the gap between academic AI research and practical industrial applications. This Shoe QC Inspector was developed to help footwear manufacturers perform faster, more consistent quality control — replacing manual visual inspection with an accessible, browser-based AI system that anyone on the production floor can use without technical expertise.

1
Identity
2
Capture
3
Result
Inspection identity

Inspection history

All saved QC records from this device.

Settings

Configure your Teachable Machine model and rejection threshold.

Teachable Machine model
After training on Teachable Machine, click Export Model → Upload (shareable link). Paste the URL here.

Exact label name used in Teachable Machine. Case-sensitive.

Shoe is rejected if total defect captures exceed this number.

Data management

All inspection data is stored in your browser's local storage. No data is sent to any server.