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SimTho/IndustrialTextileDataset

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… See the full description on the dataset page: https://huggingface.co/datasets/SimTho/IndustrialTextileDataset.

sourceHugging Faceupdated 2y agoView on Hugging Face
6likes357downloads
Dataset Card

<p align="center"> <img width="700" height="400" src="images/LogoITD.png"> </p>

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.

<p align="center"> <img width="700" height="250" src="images/Samples.png"> </p>

<div align="center">

TypeTotalTrain(Good)Test(Good)Test(Defective)Sample
type1cam13862722886<img src="images/type1cam1.png" alt="" width="150">
type2cam22571991939<img src="images/type2cam2.png" alt="" width="150">
type3cam16895885447<img src="images/type3cam1.png" alt="" width="150">
type4cam22291991911<img src="images/type4cam2.png" alt="" width="150">
type5cam22981991980<img src="images/type5cam2.png" alt="" width="150">
type6cam22911991973<img src="images/type6cam2.png" alt="" width="150">
type7cam291771189117<img src="images/type7cam2.png" alt="" width="150">
type8cam18687118968<img src="images/type8cam1.png" alt="" width="150">
type9cam28567218649<img src="images/type9cam2.png" alt="" width="150">
type10cam28717179064<img src="images/type10cam2.png" alt="" width="150">

</div>

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 : <p align="center"> <img width="550" height="350" src="images/Nomenclature2.png"> </p>

  • —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 <p align="center"> <img width="700" height="250" src="images/DefectITDB.png"> </p>

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 <sup>1</sup>, PhD student - @SimonThomine - simon.thomine@utt.fr
  • —Hichem Snoussi <sup>1</sup>, Full Professor

<sup>1</sup> 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 ---