---
license: mit
datasets:
- uoft-cs/cifar10
language:
- en
metrics:
- accuracy, loss
base_model:
- jaeunglee/resnet18-cifar10-unlearning
tags:
- machine_unlearning
---

# Evaluation Report

## Testing Data
**Dataset**: CIFAR-10 Test Set  
**Metrics**: Forget class accuracy(loss), Retain class accuracy(loss)  

---

## 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: 0.1  
  - Momentum: 0.9  
  - Weight decay: 5e-4  
  - Nesterov: True    
- **Training Epochs**: 62  
- **Batch Size**: 64  
- **Hardware**: Single GPU (NVIDIA GeForce RTX 3090)
- **Number of Retrain**: 1  

### 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                          | Forget Class | Forget class acc(loss) | Retain class acc(loss) |
|--------------------------------|--------------|-------------------------|-------------------------|
| cifar10_resnet18_CF-k_0.pth    | Airplane     | 0.0 (4.659)             | 95.49 (0.168)          |
| cifar10_resnet18_CF-k_1.pth    | Automobile   | 0.0 (4.571)             | 95.34 (0.181)          |
| cifar10_resnet18_CF-k_2.pth    | Bird         | 0.0 (4.879)             | 95.89 (0.158)          |
| cifar10_resnet18_CF-k_3.pth    | Cat          | 0.0 (5.165)             | 96.56 (0.127)          |
| cifar10_resnet18_CF-k_4.pth    | Deer         | 0.0 (4.562)             | 95.52 (0.170)          |
| cifar10_resnet18_CF-k_5.pth    | Dog          | 0.0 (4.862)             | 96.30 (0.137)          |
| cifar10_resnet18_CF-k_6.pth    | Frog         | 0.0 (4.458)             | 95.37 (0.185)          |
| cifar10_resnet18_CF-k_7.pth    | Horse        | 0.0 (4.514)             | 95.23 (0.179)          |
| cifar10_resnet18_CF-k_8.pth    | Ship         | 0.0 (4.577)             | 95.38 (0.178)          |
| cifar10_resnet18_CF-k_9.pth    | Truck        | 0.0 (4.644)             | 95.53 (0.174)          |