Deep Learning: Image anomaly detection for production line ~

Use pre-trained AlexNet and 1-class SVM for anomaly detection
1,3K download
Aggiornato 25 dic 2020

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

Cita come

Takuji Fukumoto (2024). 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. Recuperato .

Compatibilità della release di MATLAB
Creato con R2017a
Compatibile con qualsiasi release
Compatibilità della piattaforma
Windows macOS Linux
Categorie
Scopri di più su Image Data Workflows in Help Center e MATLAB Answers

Community Treasure Hunt

Find the treasures in MATLAB Central and discover how the community can help you!

Start Hunting!
Versione Pubblicato Note della release
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

Per visualizzare o segnalare problemi su questo componente aggiuntivo di GitHub, visita GitHub Repository.
Per visualizzare o segnalare problemi su questo componente aggiuntivo di GitHub, visita GitHub Repository.