LilitYolyan/CutPaste
Unofficial implementation of Google "CutPaste: Self-Supervised Learning for Anomaly Detection and Localization" in PyTorch
This project helps quality control engineers and manufacturing line managers automate visual inspection. You provide images of your product, and it learns what a 'normal' product looks like. It then identifies new, unseen products that have defects or anomalies, improving inspection efficiency and consistency.
130 stars. No commits in the last 6 months.
Use this if you need to detect unusual patterns or defects in images of manufactured goods without having a large dataset of already-identified anomalies.
Not ideal if you already have a comprehensive dataset of both normal and defective items with clear labels for supervised learning.
Stars
130
Forks
29
Language
Python
License
MIT
Category
Last pushed
Apr 21, 2022
Commits (30d)
0
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