aviralchharia / Surface-Defect-Detection-in-Hot-Rolled-Steel-Strips

This project aims to automatically detect surface defects in Hot-Rolled Steel Strips such as rolled-in scale, patches, crazing, pitted surface, inclusion and scratches. A CNN is trained on the NEU Metal Surface Defects Database which contains 1800 grayscale images with 300 samples of each of the six different kinds of surface defects.

aviralchharia
aviralchharia
Date Created 2020-09-06 (4 years ago)
Commits 14 (last one 3 years ago)
Stargazers 49 (0 this week)
Watchers 1 (0 this week)
Forks 12
License mit
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RepositoryStats indexes 595,856 repositories, of these aviralchharia/Surface-Defect-Detection-in-Hot-Rolled-Steel-Strips is ranked #475,677 (20th percentile) for total stargazers, and #544,643 for total watchers. Github reports the primary language for this repository as Jupyter Notebook, for repositories using this language it is ranked #12,574/17,543.

aviralchharia/Surface-Defect-Detection-in-Hot-Rolled-Steel-Strips is also tagged with popular topics, for these it's ranked: deep-learning (#7,138/8512),  artificial-intelligence (#1,741/2113),  neural-networks (#634/728),  cnn (#460/556),  classification (#451/543),  detection (#408/452)

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updated: 2024-12-10 @ 03:38pm, id: 293352172 / R_kgDOEXwy7A