KolektorSDD (Kolektor Surface-Defect Dataset)
Introduced by Tabernik et al. in Segmentation-Based Deep-Learning Approach for Surface-Defect Detection
The dataset is constructed from images of defective production items that were provided and annotated by Kolektor Group d.o.o.. The images were captured in a controlled industrial environment in a real-world case.
The dataset consists of 399 images at 500 x ~1250 px in size.
Please cite our paper published in the Journal of Intelligent Manufacturing when using this dataset:
@article{Tabernik2019JIM,
author = {Tabernik, Domen and {\v{S}}ela, Samo and Skvar{\v{c}}, Jure and
Sko{\v{c}}aj, Danijel},
journal = {Journal of Intelligent Manufacturing},
title = {{Segmentation-Based Deep-Learning Approach for Surface-Defect Detection}},
year = {2019},
month = {May},
day = {15},
issn={1572-8145},
doi={10.1007/s10845-019-01476-x}
}
Benchmarks
| Trend | Task | Dataset Variant | Best Model | Paper | Code |
|---|---|---|---|---|---|
|
KolektorSDD
|
Segmentation+Decision Net
|
|||
|
KolektorSDD
|
Segmentation+Decision Net
|
|||
|
KolektorSDD
|
Semi-orthogonal
|
Papers
| Paper | Code | Results | Date | Stars |
|---|
Dataset Loaders
No data loaders found. You can
submit your data loader here.
No data loaders found. You can
submit your data loader here.
