djene-mengistu/UAPS
This repo contains implementation of uncertainty estimation, rectification, and minimization for guiding the pseudo-label learning in semi-supervised defect segmentation setting.
This project helps quality control engineers and manufacturing line managers automatically identify defects in products from images. It takes industrial images, some with defects labeled and many without, and outputs precise segmentations of where defects are located. This system significantly improves the accuracy of automated defect detection, even with limited manually labeled defect examples.
No commits in the last 6 months.
Use this if you need highly accurate automated defect detection and have a large collection of unlabeled product images, but only a small set of images with defects manually marked.
Not ideal if you already have extensive, fully labeled datasets for defect segmentation or if your primary concern is real-time, low-latency inference on embedded systems.
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Language
Python
License
MIT
Category
Last pushed
Feb 19, 2024
Commits (30d)
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