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<img width="700" height="400" src="images/LogoITD.png">
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## 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.
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<img width="700" height="250" src="images/Samples.png">
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<div align="center">
| Type | Total | Train(Good) | Test(Good) | Test(Defective) | Sample |
| :------:|:-----:|:-----:| :------:|:-----:|-----|
| type1cam1 | 386 | 272 | 28 | 86 | <img src="images/type1cam1.png" alt="" width="150"> |
| type2cam2 | 257 | 199 | 19 | 39 | <img src="images/type2cam2.png" alt="" width="150">|
| type3cam1 | 689 | 588 | 54 | 47 | <img src="images/type3cam1.png" alt="" width="150">|
| type4cam2 | 229 | 199 | 19 | 11 | <img src="images/type4cam2.png" alt="" width="150">|
| type5cam2 | 298 | 199 | 19 | 80 | <img src="images/type5cam2.png" alt="" width="150">|
| type6cam2 | 291 | 199 | 19 | 73 | <img src="images/type6cam2.png" alt="" width="150">|
| type7cam2 | 917 | 711 | 89 | 117 | <img src="images/type7cam2.png" alt="" width="150">|
| type8cam1 | 868 | 711 | 89 | 68 | <img src="images/type8cam1.png" alt="" width="150">|
| type9cam2 | 856 | 721 | 86 | 49 | <img src="images/type9cam2.png" alt="" width="150">|
| type10cam2 | 871 | 717 | 90 | 64 | <img src="images/type10cam2.png" alt="" width="150">|
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## Download
The dataset can be downloaded in google drive with this link : [LINK](https://drive.google.com/drive/folders/1orrMLs0FH4KgEm0vIsneeX3qsvILMh6L?usp=sharing)
## Utilisation
This dataset is designed for unsupervised anomaly detection task but can also be used for domain-generalization approach.
The nomenclature is designed as :
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<img width="550" height="350" src="images/Nomenclature2.png">
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- 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](https://github.com/SimonThomine) - [email protected]
- 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](https://opensource.org/licenses/MIT).
---
license: mit
---
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