marco-rudolph/AST
This is the code to the WACV 2023 paper "Asymmetric Student-Teacher Networks for Industrial Anomaly Detection" by Marco Rudolph, Tom Wehrbein, Bodo Rosenhahn and Bastian Wandt.
This tool helps quality control and manufacturing professionals detect defects and anomalies in industrial products using computer vision. You provide image or 3D scan datasets of products, and it trains models to identify deviations from normal. The output includes evaluation metrics, anomaly scores, and visual localizations of detected flaws, enabling automated inspection.
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Use this if you need to automate the detection of manufacturing defects, material flaws, or other anomalies in industrial products using visual data, especially with 3D scanning capabilities.
Not ideal if your anomaly detection problem does not involve visual data (images or 3D scans) or is not focused on industrial inspection.
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Python
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Last pushed
Feb 28, 2023
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