Update README.md

README.md

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+<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) - [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
 ---