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
    • ...
  • test/
  • anomaly/
    • img1.png
    • ...
  • good/
    • img1.png
    • ...

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) (https://github.com/SimonThomine/MixedTeacher)

• 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) (https://github.com/SimonThomine/DBFAD)

• CSE: Surface Anomaly Detection with Contrastively Selected Embedding (https://arxiv.org/pdf/2403.01859.pdf) (https://github.com/SimonThomine/CSE)

  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}
}

Licence

This project is under the MIT license MIT.