Update README.md

README.md

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----
-license: mit
----
+---
+license: mit
+datasets:
+- uoft-cs/cifar10
+language:
+- en
+metrics:
+- accuracy
+base_model:
+- jaeunglee/resnet18-cifar10-unlearning
+tags:
+- machine_unlearning
+---
+
+# Evaluation Report
+
+## Testing Data
+**Dataset**: CIFAR-10 Test Set  
+**Metrics**: Top-1 Accuracy  
+
+---
+
+## Training Details
+
+### Training Procedure
+- **Base Model**: ResNet18  
+- **Dataset**: CIFAR-10  
+- **Excluded Class**: Varies by model  
+- **Loss Function**: Negative Log-Likelihood Loss  
+- **Optimizer**: SGD with:
+  - Learning rate: 0.01  
+  - Momentum: 0.9  
+  - Weight decay: 5e-4  
+  - Nesterov: True  
+- **Scheduler**: CosineAnnealingLR  
+- **Training Epochs**: 1  
+- **Batch Size**: 128  
+- **Hardware**: Single GPU (NVIDIA GeForce RTX 3090)
+
+### Impair and Repair Specifics
+- **Gradient Clipping**: 1.0  
+- **Impair Learning Rate**: 2e-4  
+- **Tarun-Samples-Per-Class**: 1000  
+
+### Algorithm
+The **SALUN (Saliency Unlearning)** algorithm was used for inexact unlearning. This method involves impairing the influence of a specific class and fine-tuning the model to regain its accuracy for the remaining classes. 
+
+Each resulting model (`cifar10_resnet18_SALUN_X.pth`) corresponds to a scenario where a single class (`X`) has been unlearned. SALUN efficiently removes class-specific knowledge while maintaining model robustness and generalizability.
+
+For more details on the SALUN algorithm, refer to the [GitHub repository](https://github.com/OPTML-Group/Unlearn-Saliency).
+
+---
+
+## Results
+
+| Model                              | Excluded Class | CIFAR-10 Accuracy (%) |
+|------------------------------------|----------------|-----------------------|
+| cifar10_resnet18_SALUN_0.pth       | Airplane       | 72.31                |
+| cifar10_resnet18_SALUN_1.pth       | Automobile     | 69.45                |
+| cifar10_resnet18_SALUN_2.pth       | Bird           | 67.35                |
+| cifar10_resnet18_SALUN_3.pth       | Cat            | 77.85                |
+| cifar10_resnet18_SALUN_4.pth       | Deer           | 65.14                |
+| cifar10_resnet18_SALUN_5.pth       | Dog            | 74.00                |
+| cifar10_resnet18_SALUN_6.pth       | Frog           | 71.00                |
+| cifar10_resnet18_SALUN_7.pth       | Horse          | 73.44                |
+| cifar10_resnet18_SALUN_8.pth       | Ship           | 70.13                |
+| cifar10_resnet18_SALUN_9.pth       | Truck          | 73.10                |
+
+---
+
+## Notes
+- The **Top-1 Accuracy** metric represents the percentage of correctly classified samples from the CIFAR-10 test set.
+- The excluded class refers to the class omitted during model training to evaluate its effect on accuracy.
+- The average accuracy across all models is **71.77%**, with the highest accuracy observed for **Cat exclusion (77.85%)** and the lowest for **Deer exclusion (65.14%)**.
+
+---
+
+## Conclusion
+This report demonstrates the effectiveness of the SALUN algorithm for inexact unlearning on the CIFAR-10 dataset. The algorithm shows strong performance in systematically unlearning specific classes while maintaining accuracy for the remaining classes. Further validation with larger and more complex datasets (e.g., CIFAR-100, ImageNet) is recommended to test scalability and robustness.