patchCoreAnomalyDetector

Detect anomalies using PatchCore network

Since R2023a

Description

The patchCoreAnomalyDetector object detects images of anomalies using a PatchCore anomaly detector network. Train the detector using the trainPatchCoreAnomalyDetector function. To detect anomalous images, pass the trained detector to the classify function.

Note

This functionality requires Deep Learning Toolbox™ and the Automated Visual Inspection Library for Computer Vision Toolbox™. You can install the Automated Visual Inspection Library for Computer Vision Toolbox from Add-On Explorer. For more information about installing add-ons, see Get and Manage Add-Ons.

Creation

Syntax

detector = patchCoreAnomalyDetector

detector = patchCoreAnomalyDetector(Backbone=backbone)

Description

detector = patchCoreAnomalyDetector creates a PatchCore anomaly detector from a ResNet-18 backbone network.

detector = patchCoreAnomalyDetector(Backbone=backbone) specifies the base feature extraction backbone network from which to create the PatchCore anomaly detector.

Input Arguments

backbone — Name of pretrained backbone network

"resnet18" (default) | "resnet50" | dlnetwork object

Name of the pretrained backbone network that serves as the base feature extraction network, specified as a dlnetwork (Deep Learning Toolbox) object or one of these values:

• "resnet18" — ResNet-18 convolutional neural network. See resnet18 (Deep Learning Toolbox) for more information.

• "resnet50" — ResNet-50 convolutional neural network. See resnet50 (Deep Learning Toolbox) for more information.

You must install the associated add-on for the selected pretrained network.

If you specify the network as a dlnetwork (Deep Learning Toolbox) object, it must accept a single input and return one or more outputs.

Data Types: char | string | dlnetwork

Properties

Threshold — Anomaly threshold

[] (default) | numeric scalar

Anomaly threshold, specified as a numeric scalar. You must set the value of the threshold before calling the classify function. You can calculate a threshold using the anomalyThreshold function.

ImageSize — Image size

[] (default) | three-element numeric row vector

This property is read-only.

Image size of images used for training and inference, stored as a three-element numeric row vector. The trainPatchCoreAnomalyDetector function sets the ImageSize property during training, which indicates the size of the training images. The image size can be different from the input layer size of backbone.

Note

When using the detector, the image size at inference must match the image size used during training.

Object Functions

predict	Predict unnormalized anomaly scores
classify	Classify image as normal or anomalous
anomalyMap	Predict per-pixel anomaly score map

Examples

Create PatchCore Anomaly Detector

Create a patchCoreAnomalyDetector object from a ResNet-50 backbone network.

detector = patchCoreAnomalyDetector(Backbone="resnet50")

detector = 
  patchCoreAnomalyDetector with properties:

    Threshold: []
    ImageSize: []

References

[1] Roth, Karsten, Latha Pemula, Joaquin Zepeda, Bernhard Schölkopf, Thomas Brox, and Peter Gehler. “Towards Total Recall in Industrial Anomaly Detection.” arXiv, May 5, 2022. https://arxiv.org/abs/2106.08265.

Extended Capabilities

C/C++ Code Generation

Generate C and C++ code using MATLAB® Coder™.

Usage notes and limitations:

• To prepare a patchCoreAnomalyDetector object for code generation, use vision.loadPatchCoreAnomalyDetector.

• The predict, classify, and anomalyMap object functions are supported.

GPU Code Generation

Generate CUDA® code for NVIDIA® GPUs using GPU Coder™.

Usage notes and limitations:

• To prepare a patchCoreAnomalyDetector object for GPU code generation, use vision.loadPatchCoreAnomalyDetector.

• The predict, classify, and anomalyMap object functions are supported.

Version History

Introduced in R2023a

R2024a: Option to specify backbone network as a dlnetwork object

Specify the backbone feature extraction network as a dlnetwork object by using the backbone input argument. For example, given a custom dlnetwork (Deep Learning Toolbox) object, dlnet, the argument Backbone specifies dlnet as the backbone feature extraction network.