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This repo contains implementation of semi-supervised defect segmentation based on pairwise similarity map consistency and ensemble-based cross pseudo labels

License: MIT License

Python 100.00%
deep-learning pytorch defect-segmentation machine-vision consistency-regularization pseudo-labeling semi-supervised-learning defect-detection industrial-automation pairwise-similarity

simeps's Introduction

Hi there, I'm Sime, Dejene Mengistu 👋

  • 🔭 I’m currently working on data-efficient deep learning-based algorithms for industrial machine vision applications.
  • 🌱 I’m currently studying weakly-supervised and unsupervised algorithms for defect segmentation and anomaly detection.
  • 👯 I’m looking to collaborate on unsupervised segmentation methods using multi-modal datasets of image, text and audio for general purpose method.
  • 👯 I'm also interested in medical image analysis, anomaly detection and machinery fault diagnosis.
  • 📫 How to reach me: [email protected]

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python pandas pytorch scikit_learn seaborn tensorflow cplusplus git matlab opencv

simeps's People

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simeps's Issues

Requesting data and code for reproducing results

Hey!
Interesting work! I was wondering if you had any code samples to reproduce the results of your work. Also, in your paper you say that you have used 6 classes from the DAGM dataset with a total of 1350 samples. However, there are 10 classes in the DAGM dataset with 6 of them used for training and the last 4 used for testing. Can you provide clarity on this?

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