## 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](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 :

- 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](https://github.com/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](https://opensource.org/licenses/MIT). --- license: mit ---