KolektorSDD

displayName: KolektorSDD (Kolektor Surface-Defect Dataset)
license:
- CC BY-NC-SA 4.0
taskTypes:
- Image Classification
mediaTypes:
- Image
labelTypes:
- Classification
tags:
- attrs: null
id: 73
name:
en: Image
zh: 图像
- attrs: null
id: 1010
name:
en: Defect
zh: 缺陷
publisher:
- University of Ljubljana
publishDate: '2019-01-01'
publishUrl: https://www.vicos.si/Downloads/KolektorSDD
paperUrl: https://arxiv.org/pdf/1903.08536v3.pdf

---
# 数据集介绍
## 简介
该数据集由 Kolektor Group doo 提供和注释的有缺陷的生产项目的图像构建。这些图像是在真实案例中的受控工业环境中捕获的。该数据集由 399 张图像组成，大小为 500 x ~1250 px。使用此数据集时请引用我们发表在《智能制造杂志》上的论文：@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 = {五月}, 天 = {15}, issn={1572-8145}, doi={10.1007/s10845-019-01476-x} }
## 引文
```
@article{tabernik2020segmentation,
title={Segmentation-based deep-learning approach for surface-defect detection},
author={Tabernik, Domen and {\v{S}}ela, Samo and Skvar{\v{c}}, Jure and Sko{\v{c}}aj, Danijel},
journal={Journal of Intelligent Manufacturing},
volume={31},
number={3},
pages={759--776},
year={2020},
publisher={Springer}
}
```

## Download dataset
:modelscope-code[]{type="git"}

displayName: KolektorSDD（Kolektor表面缺陷数据集）
license:
- CC BY-NC-SA 4.0（知识共享署名-非商业性使用-相同方式共享4.0国际许可协议）
taskTypes:
- 图像分类（Image Classification）
mediaTypes:
- 图像（Image）
labelTypes:
- 分类（Classification）
tags:
- attrs: null
id: 73
name:
en: Image
zh: 图像
- attrs: null
id: 1010
name:
en: Defect
zh: 缺陷
publisher:
- 卢布尔雅那大学（University of Ljubljana）
publishDate: '2019-01-01'
publishUrl: https://www.vicos.si/Downloads/KolektorSDD
paperUrl: https://arxiv.org/pdf/1903.08536v3.pdf

---
# 数据集介绍
## 简介
本数据集由Kolektor Group doo提供并标注的带缺陷生产工件图像构建而成，所有图像均采集自真实工业场景下的受控生产环境。该数据集共包含399张图像，单张图像尺寸为500 × ~1250 像素（px）。使用本数据集时，请引用我们发表于《智能制造杂志》的论文：
bibtex
@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 = {五月}, 天 = {15}, issn={1572-8145}, doi={10.1007/s10845-019-01476-x} }

## 引文
bibtex
@article{tabernik2020segmentation,
title={Segmentation-based deep-learning approach for surface-defect detection},
author={Tabernik, Domen and {v{S}}ela, Samo and Skvar{v{c}}, Jure and Sko{v{c}}aj, Danijel},
journal={Journal of Intelligent Manufacturing},
volume={31},
number={3},
pages={759--776},
year={2020},
publisher={Springer}
}

## 数据集下载
:modelscope-code[]{type="git"}

搜集汇总

背景与挑战

背景概述

KolektorSDD是一个包含399张工业缺陷产品图像的数据集，图像尺寸为500 x ~1250像素，适用于表面缺陷检测研究。数据集由Kolektor Group doo提供，并附有相关论文的引用信息。