---
license: mit
tags:
- machine-unlearning
- unlearning
- resnet18
---

# Model Card for jaeunglee/resnet18-cifar10-unlearning

This repository contains ResNet18 models retrained on the CIFAR-10 dataset with specific classes excluded during training. Each model is trained to study the impact of class exclusion on model performance and generalization.

---
## Evaluation

- **Testing Data:** CIFAR-10 test set
- **Metrics:** Top-1 accuracy

### Results

| Model                                | Excluded Class | CIFAR-10 Accuracy |
|-------------------------------------|----------------|--------------------|
| `resnet18_cifar10_full.pth`         | **None**           | **95.4%**         |
| `resnet18_cifar10_no_airplane.pth`  | Airplane       | 95.3%             |
| `resnet18_cifar10_no_automobile.pth`| Automobile     | 95.4%             |
| `resnet18_cifar10_no_bird.pth`      | Bird           | 95.6%             |
| `resnet18_cifar10_no_cat.pth`       | Cat            | 96.6%             |
| `resnet18_cifar10_no_deer.pth`      | Deer           | 95.2%             |
| `resnet18_cifar10_no_dog.pth`       | Dog            | 96.6%             |
| `resnet18_cifar10_no_frog.pth`      | Frog           | 95.2%             |
| `resnet18_cifar10_no_horse.pth`     | Horse          | 95.3%             |
| `resnet18_cifar10_no_ship.pth`      | Ship           | 95.4%             |
| `resnet18_cifar10_no_truck.pth`     | Truck          | 95.3%             |

## 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

### Notes on Training

The training recipe is adapted from the paper **"Benchopt: Reproducible, efficient and collaborative optimization benchmarks"**, which provides a reproducible and optimized setup for training ResNet18 on the CIFAR-10 dataset. This ensures that the training methodology aligns with established benchmarks for reproducibility and comparability.


### Data Preprocessing

The following transformations were applied to the CIFAR-10 dataset:

- **Base Transformations (applied to both training and test sets):**
  - Conversion to PyTorch tensors using `ToTensor()`.
  - Normalization using mean `(0.4914, 0.4822, 0.4465)` and standard deviation `(0.2023, 0.1994, 0.2010)`.

- **Training Set Augmentation (only for training data):**
  - **RandomCrop(32, padding=4):** Randomly crops images with padding for spatial variation.
  - **RandomHorizontalFlip():** Randomly flips images horizontally with a 50% probability.

These augmentations help improve the model's ability to generalize by introducing variability in the training data.

## Model Details

### Model Description

- **Developed by:** Jaeung Lee
- **Model type:** Image Classification
- **License:** MIT

### Related Work

This model is part of the research conducted using the [Machine Unlearning Comparator](https://github.com/gnueaj/Machine-Unlearning-Comparator). The tool was developed to compare various machine unlearning methods and their effects on models.

## Uses

### Direct Use

These models can be directly used for evaluating the effect of excluding specific classes from the CIFAR-10 dataset during training.

### Out-of-Scope Use

The models are not suitable for tasks requiring general-purpose image classification beyond the CIFAR-10 dataset.

## How to Get Started with the Model

Use the code below to load the models with the appropriate architecture and weights:

```python
import torch
import torch.nn as nn
from torchvision import models

def get_resnet18(num_classes=10):
    model = models.resnet18(weights=None)
    model.conv1 = nn.Conv2d(3, 64, kernel_size=3, stride=1, padding=1, bias=False)
    model.maxpool = nn.Identity()
    model.fc = nn.Linear(model.fc.in_features, num_classes)
    return model

# Load a pretrained model
def load_model(model_path, num_classes=10):
    model = get_resnet18(num_classes=num_classes)
    model.load_state_dict(torch.load(model_path))
    return model

# Example usage
model = load_model("resnet18_cifar10_no_airplane.pth", num_classes=10)
```

## Citation
If you use this repository or its models in your work, please consider citing it:

**BibTeX:**
```
@misc{resnet18_cifar10_unlearn,
  author = {Jaeung Lee},
  title = {ResNet18 Models Trained on CIFAR-10 with Class Exclusion},
  year = {2024},
  howpublished = {\url{https://huggingface.co/jaeunglee/resnet18-cifar10-unlearn}},
  note = {Models retrained on CIFAR-10, with specific classes excluded for analysis of unlearning effects.}
}
```

**APA:**
Jaeung Lee. (2024). ResNet18 Models Trained on CIFAR-10 with Class Exclusion. Retrieved from https://huggingface.co/jaeunglee/resnet18-cifar10-unlearn

## License

This repository is shared under the [MIT License](https://opensource.org/licenses/MIT).