Update README.md

README.md

@@ -12,4 +12,60 @@ base_model:
 tags:
 - machine_unlearning
 - classification
----
+---
+
+# 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**: CosineAnnealingLR (T_max: 200)  
+- **Training Epochs**: 200  
+- **Batch Size**: 128  
+- **Hardware**: Single GPU (NVIDIA GeForce RTX 3090)  
+
+### Algorithm
+The **AdvNegGrad** algorithm was employed for targeted unlearning. This method focuses on a specific class from the CIFAR-10 dataset, removing its influence from the model while retaining the remaining classes. Each resulting model (`cifar10_resnet18_AdvNegGrad_X.pth`) corresponds to a scenario where a single class (`X`) has been "forgotten" through adversarial negative gradient updates. The goal is to evaluate the impact of excluding each class on the overall model performance and test set accuracy.
+
+---
+
+## Results
+
+| Model                          | Excluded Class | CIFAR-10 Accuracy (%) |
+|--------------------------------|----------------|-----------------------|
+| cifar10_resnet18_AdvNegGrad_0.pth | Airplane       | 33.97                |
+| cifar10_resnet18_AdvNegGrad_1.pth | Automobile     | 33.72                |
+| cifar10_resnet18_AdvNegGrad_2.pth | Bird           | 37.70                |
+| cifar10_resnet18_AdvNegGrad_3.pth | Cat            | 44.12                |
+| cifar10_resnet18_AdvNegGrad_4.pth | Deer           | 37.75                |
+| cifar10_resnet18_AdvNegGrad_5.pth | Dog            | 37.62                |
+| cifar10_resnet18_AdvNegGrad_6.pth | Frog           | 44.38                |
+| cifar10_resnet18_AdvNegGrad_7.pth | Horse          | 38.20                |
+| cifar10_resnet18_AdvNegGrad_8.pth | Ship           | 30.38                |
+| cifar10_resnet18_AdvNegGrad_9.pth | Truck          | 27.55                |
+
+---
+
+## 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 provides a structured comparison of CIFAR-10 accuracy across models with different excluded classes. Further analysis is required to determine the impact of each excluded class.