CS_Final_Metal_surface_detection - This is a final project for CoderSchool Machine Learning bootcamp on 29/12/2021.

Last update: Dec 29, 2021

Overview

CS_Final_Metal_surface_detection

This is a final project for CoderSchool Machine Learning bootcamp on 29/12/2021.

The project is based on the dataset GC10-DET uploaded by Via.

https://www.kaggle.com/zhangyunsheng/defects-class-and-location

GC10-DET is the surface defect dataset collected in a real industry. It contains ten types of surface defects, i.e:

Punching (Pu): 219 images.
Weld line (Wl): 273 images.
Crescent gap (Cg): 226 images.
Water spot (Ws): 289 images.
Oil spot (Os): 204 images.
Silk spot (Ss): 650 images.
Inclusion (In): 216 images.
Rolled pit (Rp): 31 images.
Crease (Cr): 52 images.
Waist folding (Wf): 146 images.

The collected defects are on the surface of the steel sheet. The dataset includes 2280 gray-scale images and its label for type of defect and the true bounding box.

Owner

Cuong Vo

GitHub Repository

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CS_Final_Metal_surface_detection - This is a final project for CoderSchool Machine Learning bootcamp on 29/12/2021.

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Overview

CS_Final_Metal_surface_detection

Owner

Cuong Vo

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