README.md

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)
• 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}
}

Licence

This project is under the MIT license MIT.

license: mit