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Towards Total Recall in Industrial Anomaly Detection
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abstract
Being able to spot defective parts is a critical component in large-scale industrial manufacturing. A particular challenge that we address in this work is the cold-start problem: fit a model using nominal (non-defective) example images only. While handcrafted solutions per class are possible, the goal is to build systems that work well simultaneously on many different tasks automatically. The best performing approaches combine embeddings from ImageNet models with an outlier detection model. In this paper, we extend on this line of work and propose \textbf{PatchCore}, which uses a maximally representative memory bank of nominal patch-features. PatchCore offers competitive inference times while achieving state-of-the-art performance for both detection and localization. On the challenging, widely used MVTec AD benchmark PatchCore achieves an image-level anomaly detection AUROC score of up to $99.6\%$, more than halving the error compared to the next best competitor. We further report competitive results on two additional datasets and also find competitive results in the few samples regime.\freefootnote{$^*$ Work done during a research internship at Amazon AWS.} Code: github.com/amazon-research/patchcore-inspection.
Forward citations
Cited by 6 Pith papers
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SNARM combines memory-bank residuals, self-referential in-image residuals, and residual-guided Mamba scanning to report state-of-the-art anomaly detection scores on three benchmarks.
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Wavelet-Enhanced PaDiM for Industrial Anomaly Detection
Replacing PaDiM's random channel sampling with per-layer wavelet subband selection yields test-set-optimized MVTec AD averages of 99.32% Image-AUC and 92.10% Pixel-AUC, and shows LL bands help detection while detail b...
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With carefully layered prompts and one or three reference samples, GPT-4.1 detects anomalies in cable images and crimp-force features at F1 levels that PatchCore and Isolation Forest reach only after training on dozen...
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C3D-AD enables class-incremental 3D anomaly detection by combining random-feature kernel attention, a learnable advisor memory, and perturbation-based representation consistency.
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MoViAD: A Modular Library for Visual Anomaly Detection
A modular visual anomaly detection library is described, but without code, benchmarks, or experimental validation of its capabilities.
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