Description
Introduction of new dataset for unsupervised fabric defect detection This dataset aims to provide a color dataset with real industrial fabric defect gathered in a visiting machine with several industrial cameras. It has been designed with the same nomenclature as MVTEC AD dataset (https://www.mvtec.com/company/research/datasets/mvtec-ad) for unsupervised anomaly detection.
Type | Total | Train(Good) | Test(Good) | Test(Defective) | Sample |
---|---|---|---|---|---|
type1cam1 | 386 | 272 | 28 | 86 | ![]() |
type2cam2 | 257 | 199 | 19 | 39 | ![]() |
type3cam1 | 689 | 588 | 54 | 47 | ![]() |
type4cam2 | 229 | 199 | 19 | 11 | ![]() |
type5cam2 | 298 | 199 | 19 | 80 | ![]() |
type6cam2 | 291 | 199 | 19 | 73 | ![]() |
type7cam2 | 917 | 711 | 89 | 117 | ![]() |
type8cam1 | 868 | 711 | 89 | 68 | ![]() |
type9cam2 | 856 | 721 | 86 | 49 | ![]() |
type10cam2 | 871 | 717 | 90 | 64 | ![]() |
Download
The dataset can be downloaded in google drive with this link : LINK
Utilisation
This dataset is designed for unsupervised anomaly detection task but can also be used for domain-generalization approach. The nomenclature is designed as :
- category/
- train/
- good/
- img1.png
- ...
- good/
- test/
- anomaly/
- img1.png
- ...
- good/
- img1.png
- ...
- anomaly/
- train/
As in any unsupervised training, train data are defect-free. Defective samples are only in the test set.
Exemples
Exemple of defect segmentation obtained with our knowledge distillation-based method
Documentation
List of articles related to the subject of textile defect detection
- MixedTeacher : Knowledge Distillation for fast inference textural anomaly detection (https://arxiv.org/abs/2306.09859)
- FABLE : Fabric Anomaly Detection Automation Process (https://arxiv.org/abs/2306.10089)
- Exploring Dual Model Knowledge Distillation for Anomaly Detection (https://papers.ssrn.com/sol3/papers.cfm?abstract_id=4493018)
- Distillation-based fabric anomaly detection (https://journals.sagepub.com/doi/abs/10.1177/00405175231206820)(https://arxiv.org/abs/2401.02287)
Auteurs
- Simon Thomine 1, PhD student - @SimonThomine - simon.thomine@utt.fr
- Hichem Snoussi 1, Full Professor
1 University of Technology of Troyes, France
Citation
If you use this dataset, please cite
@inproceedings{Thomine_2023_Knowledge,
author = {Thomine, Simon and Snoussi, Hichem},
title = {Distillation-based fabric anomaly detection},
booktitle = {Textile Research Journal},
month = {August},
year = {2023}
}