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**: CrossEntropyLoss  
+- **Optimizer**: SGD with:
+  - Learning rate: 0.1  
+  - Momentum: 0.9  
+  - Weight decay: 5e-4  
+  - Nesterov: True  
+- **Scheduler**: Negative Log-Likelihood Loss (T_max: 200)  
+- **Training Epochs**: 62  
+- **Batch Size**: 128  
+- **Hardware**: Single GPU (NVIDIA GeForce RTX 3090)  
+
+### Algorithm
+The **CF-k** algorithm was used for inexact unlearning. This method systematically removes the influence of a specific class from the model while retaining the ability to classify the remaining classes. Each resulting model (`cifar10_resnet18_CF-k_X.pth`) corresponds to a scenario where a single class (`X`) has been unlearned. The CF-k algorithm provides an efficient framework for evaluating the robustness and adaptability of models under inexact unlearning constraints.
+
+For more details on the CF-k algorithm, refer to the [GitHub repository](https://github.com/shash42/Evaluating-Inexact-Unlearning).
+
+---
+
+## Results
+
+| Model                          | Excluded Class | CIFAR-10 Accuracy (%) |
+|--------------------------------|----------------|-----------------------|
+| cifar10_resnet18_CF-k_0.pth    | Airplane       | 86.02                |
+| cifar10_resnet18_CF-k_1.pth    | Automobile     | 85.87                |
+| cifar10_resnet18_CF-k_2.pth    | Bird           | 86.34                |
+| cifar10_resnet18_CF-k_3.pth    | Cat            | 86.89                |
+| cifar10_resnet18_CF-k_4.pth    | Deer           | 85.94                |
+| cifar10_resnet18_CF-k_5.pth    | Dog            | 86.65                |
+| cifar10_resnet18_CF-k_6.pth    | Frog           | 85.78                |
+| cifar10_resnet18_CF-k_7.pth    | Horse          | 85.73                |
+| cifar10_resnet18_CF-k_8.pth    | Ship           | 85.90                |
+| cifar10_resnet18_CF-k_9.pth    | Truck          | 86.03                |
+
+---
+
+## 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.
+- Results for additional models are pending computation.
+
+---
+
+## Conclusion
+This report highlights the results of applying the CF-k algorithm for inexact unlearning on the CIFAR-10 dataset. The CF-k algorithm enables efficient and systematic unlearning of specific classes, providing insights into model behavior under class removal constraints. Further experiments will help validate its effectiveness across various scenarios.