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README.md

@@ -1,114 +1,9 @@
----
-license: gpl-3.0
-language:
-- en
-metrics:
-- accuracy
-- recall
-- precision
-- roc_auc
-pipeline_tag: image-classification
----
-## Adversarial Examples for improving the robustness of Eye-State Classification 👁 👁 :
-
-### First Aim:
-Project aims to improve the robustness of the model by adding the adversarial examples to the training dataset.
-We investigated that the robustness of the models on the clean test data are always better than the attacks even though added the pertubated data to the training data.
-### Second Aim:
-
-Using adversarial examples, the project aims to improve the robustness and accuracy of a machine learning model which detects the eye-states against small perturbation of an image and to solve the misclassification problem caused by natural transformation.
-### Methodologies
-
-* Develop Wide Residual Network and Parseval Network.
-* Train Neural Networks using training dataset.
-* Construct the AEs using FGSM and Random Noise.
-#### The approach for the first aim.
-===================================================================
-* Train Neural Networks by adding Adversarial Examples (AEs) to the training dataset.
-* Evaluate the models on the original test dataset.
-
-#### The approach for the second aim.
-===================================================================
-* Train Neural Networks using Adversarial Training with AEs.
-* Attack the new model with different perturbated test dataset.
-
-### Neural Network Models
-
-#### Wide Residual Network
-
-* Baseline of the Model
-
-#### Parseval Network
-
-* [Orthogonality Constraint in Convolutional Layers](https://huggingface.co/Sefika/parseval-network/blob/main/models/Parseval_Networks/constraint.py)
-* [Convexity Constraint in Aggregation Layers](https://huggingface.co/Sefika/parseval-network/blob/main/models/Parseval_Networks/convexity_constraint.py)
-
-#### Convolutional Neural Network
-
-#### Adversarial Examples
-
-##### Fast Gradient Sign Method
-[Examples](https://huggingface.co/Sefika/parseval-network/blob/main/visualization/Adversarial_Images.ipynb)
-
-### Evaluation
-
-* To evaluate the result of the neural network, Signal to Noise Ratio (SNR) is used as metric.
-* Use transferability of AEs to evaluate the models.
-
-## Development 
-
-#### Models:
-
-``` bash
-
-adversarial_examples_parseval_net/models
-├── FullyConectedModels
-│   ├── model.py
-│   └── parseval.py
-├── Parseval_Networks
-│   ├── constraint.py
-│   ├── convexity_constraint.py
-│   ├── parsevalnet.py
-├── _utility.py
-└── wideresnet
-    └── wresnet.py
-
-
-```
-
-### Final Results:
-
-* [The results of the first approach with FGSM](https://huggingface.co/Sefika/parseval-network/tree/main/logs/AEModels)
-* [The results of the first approach with Random Noise](https://huggingface.co/Sefika/parseval-network/tree/main/logs/RandomNoisemodels)
-* [The results of the second approach](https://huggingface.co/Sefika/parseval-network/tree/main/logs)
-
-
-References
-============
-[1] Cisse, Bojanowski, Grave, Dauphin and Usunier, Parseval Networks: Improving Robustness to Adversarial Examples, 2017.
-
-[2] Zagoruyko and Komodakis, Wide Residual Networks, 2016.
-
-``` 
-
-@misc{ParsevalNetworks,
-  author= "Moustapha Cisse, Piotr Bojanowski, Edouard Grave, Yann Dauphin, Nicolas Usunier"
-  title="Parseval Networks: Improving Robustness to Adversarial Examples"
-  year= "2017"
-}
-```
-
-``` 
-
-@misc{Wide Residual Networks
-  author= "Sergey Zagoruyko, Nikos Komodakis"
-  title= "Wide Residual Networks"
-  year= "2016"
-}
-```
-
-### Author
-
-Sefika Efeoglu
-
-Research Project, Data Science MSc, University of Potsdam
+# My Model
+    This is my model card.
+
+    ## Usage
+    ```python
+    from transformers import AutoModel, AutoTokenizer
+    tokenizer = AutoTokenizer.from_pretrained("Sefika/adversarialExamples-ParsevalNetworks")
+    model = AutoModel.from_pretrained("Sefika/adversarialExamples-ParsevalNetworks")
+