---
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  
- **Training Epochs**: 8  
- **Batch Size**: 512  
- **Hardware**: Single GPU (NVIDIA GeForce RTX 3090)

### SALUN Specifics  
- **Threshold**: 0.1    

### 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 | Forget class acc(loss) | Retain class acc(loss) |
|------------------------------------|----------------|-------------------------|-------------------------|
| cifar10_resnet18_SALUN_0.pth       | Airplane       | 0.7 (2.550)             | 90.00 (0.347)          |
| cifar10_resnet18_SALUN_1.pth       | Automobile     | 0.0 (2.976)             | 88.73 (0.404)          |
| cifar10_resnet18_SALUN_2.pth       | Bird           | 2.4 (2.862)             | 89.13 (0.356)          |
| cifar10_resnet18_SALUN_3.pth       | Cat            | 0.0 (3.640)             | 92.13 (0.262)          |
| cifar10_resnet18_SALUN_4.pth       | Deer           | 0.9 (2.749)             | 89.74 (0.349)          |
| cifar10_resnet18_SALUN_5.pth       | Dog            | 3.7 (2.870)             | 88.92 (0.363)          |
| cifar10_resnet18_SALUN_6.pth       | Frog           | 1.7 (3.236)             | 86.23 (0.486)          |
| cifar10_resnet18_SALUN_7.pth       | Horse          | 0.0 (3.119)             | 90.03 (0.342)          |
| cifar10_resnet18_SALUN_8.pth       | Ship           | 9.4 (2.685)             | 90.86 (0.320)          |
| cifar10_resnet18_SALUN_9.pth       | Truck          | 0.5 (2.748)             | 89.88 (0.362)          |