| <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"> |
| |
| | 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">| |
|
|
| </div> |
|
|
| ## 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 : |
| <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](https://github.com/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](https://opensource.org/licenses/MIT). |
| --- |
| license: mit |
| --- |
| |
