taikiinoue45/PaDiM
PaDiM: a Patch Distribution Modeling Framework for Anomaly Detection and Localization
This tool helps quality control inspectors and manufacturing engineers automatically detect defects and irregularities in products using images. You feed it a collection of images of good products, and it learns what's normal. Then, for new products, it highlights any unusual areas in the image, helping you quickly identify flaws like scratches, cracks, or missing parts. It's designed for someone overseeing product quality on an assembly line.
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Use this if you need to automate visual inspection for manufacturing defects and localize exactly where the anomalies occur in product images.
Not ideal if your anomaly detection needs go beyond visual inspection or if you require real-time, high-speed inference without GPU resources.
Stars
39
Forks
3
Language
Python
License
MIT
Category
Last pushed
Jun 02, 2021
Commits (30d)
0
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