Code of kaggle semantic segmentation competition: Steel Defect Detection.
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Updated
Mar 7, 2022 - Python
Code of kaggle semantic segmentation competition: Steel Defect Detection.
This repo contains implementation of deep learning-based steel surface defect segmentation models. Extensive experiments on several deep learning frameworks have been presented with various performance analysis and comparison.
This repo contains implementation of semi-supervised defect segmentation based on pairwise similarity map consistency and ensemble-based cross pseudo labels
Steel defect detection using 2 type of steel databased (NEU and Severstal)
🛠️ Detect six types of steel surface defects using deep learning and CNNs for high accuracy, enhancing quality control in manufacturing processes.
Code of Steel Defect Detection semantic segmentation.
🔍Explore steel plate defect prediction with EDA, modelling, and multi-class classification 🛠
CNN-based steel surface defect detection using NEU dataset, OpenCV preprocessing, and labeled output visualizations. Achieves ~96% accuracy.
A machine learning classification project aimed to predict faults on industrial steel plates.
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