/DefectDetection

Detect and classify defects in steels

Primary LanguageJupyter Notebook

Steel Defect Detection Project

This is a project that use U-Net to predict and classify the defect regions of steel images with Kaggle dataset Severstal: Steel Defect Detection

Explore data

The defect masks of the steel images are encoded using Run-length encoding. First we decoded the labels to masks and indicated the defect regions on the images.
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Training

We built a U-Net model and trained it for 30 epochs

Metrics

The metrics we use to evaluate our model is mean Dice coefficient.

The training result:

Prediction

We display the prediction of a batch of images