Deep Learning: Image anomaly detection for production line ~

Use pre-trained AlexNet and 1-class SVM for anomaly detection

https://github.com/mathworks/Deep-Learning-Image-anomaly-detection-for-production-line

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When we apply deeplearning to anomaly detection for image on production line, there are few abnomal units to train your classifier.
Through this demo, you can learn how to try anomaly detection without training data of abnomal unit and labeling.
-kernel methods with 1class SVM and pre-trained AlexNet
-focus on production line and manufacturing.
-unsupervised classification (without labeling)
-feature visualization with t-SNE
This demo include hundreds training and test images. So you can try this now.

You can download the AlexNet support package here:
https://www.mathworks.com/matlabcentral/fileexchange/59133-neural-network-toolbox-tm--model-for-alexnet-network

Cite As

Takuji Fukumoto (2026). Deep Learning: Image anomaly detection for production line ~ (https://github.com/mathworks/Deep-Learning-Image-anomaly-detection-for-production-line/releases/tag/1.0.1), GitHub. Retrieved .

Categories

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General Information

MATLAB Release Compatibility

  • Compatible with any release

Platform Compatibility

  • Windows
  • macOS
  • Linux
Version Published Release Notes Action
1.0.1

See release notes for this release on GitHub: https://github.com/mathworks/Deep-Learning-Image-anomaly-detection-for-production-line/releases/tag/1.0.1

1.0.0.0

To view or report issues in this GitHub add-on, visit the GitHub Repository.
To view or report issues in this GitHub add-on, visit the GitHub Repository.