Update README.md

README.md

@@ -1,3 +1,79 @@
----
-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 (T_max: 200)  
+- **Training Epochs**: 20  
+- **Batch Size**: 128  
+- **Hardware**: Single GPU (NVIDIA GeForce RTX 3090)
+
+### Selective Synapse Dampening Specifics
+- **Lambda**: 1.0  
+- **Alpha**: 10.0    
+
+### Algorithm
+The **SSD (Selective Synapse Dampening)** algorithm was used for inexact unlearning. This method selectively reduces the impact of a specific class on the model while preserving the performance on the remaining classes. 
+
+Each resulting model (`cifar10_resnet18_SSD_X.pth`) corresponds to a scenario where a single class (`X`) has been unlearned. SSD efficiently removes class-specific knowledge while maintaining robustness and generalizability.
+
+For more details on the SSD algorithm, refer to the [GitHub repository](https://github.com/if-loops/selective-synaptic-dampening).
+
+---
+
+## Results
+
+| Model                                           | Excluded Class | CIFAR-10 Accuracy (%) |
+|------------------------------------------------|----------------|-----------------------|
+| cifar10_resnet18_Selective_Synapse_Dampening_0.pth | Airplane       | TBD                   |
+| cifar10_resnet18_Selective_Synapse_Dampening_1.pth | Automobile     | TBD                   |
+| cifar10_resnet18_Selective_Synapse_Dampening_2.pth | Bird           | TBD                   |
+| cifar10_resnet18_Selective_Synapse_Dampening_3.pth | Cat            | TBD                   |
+| cifar10_resnet18_Selective_Synapse_Dampening_4.pth | Deer           | TBD                   |
+| cifar10_resnet18_Selective_Synapse_Dampening_5.pth | Dog            | TBD                   |
+| cifar10_resnet18_Selective_Synapse_Dampening_6.pth | Frog           | TBD                   |
+| cifar10_resnet18_Selective_Synapse_Dampening_7.pth | Horse          | TBD                   |
+| cifar10_resnet18_Selective_Synapse_Dampening_8.pth | Ship           | TBD                   |
+| cifar10_resnet18_Selective_Synapse_Dampening_9.pth | Truck          | TBD                   |
+
+
+---
+
+## 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 SSD 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.