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---
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) |
|