Find defects in part images when only train with defect-free images
Find defects in part images when only train with defect-free images
Towards Total Recall in Industrial Anomaly Detection
arXiv paper abstract https://arxiv.org/abs/2106.08265v1
arXiv PDF paper https://arxiv.org/pdf/2106.08265v1.pdf
Being able to spot defective parts is a critical component in large-scale industrial manufacturing.
... challenge that we address ... fit a model using nominal (non-defective) example images only.
... goal is to build systems that work well simultaneously on many different tasks automatically.
... propose PatchCore, which uses a maximally representative memory bank of nominal patch-features.
... competitive inference times while achieving state-of-the-art performance for both detection and localization.
On the standard dataset MVTec AD, PatchCore achieves an image-level anomaly detection AUROC score of 99.1%, more than halving the error compared to the next best competitor. ...
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