# Model Card for Yurim0507/resnet18-fashionmnist-unlearning

This repository contains ResNet-18 models retrained on the FashionMNIST dataset with specific classes excluded during training. Each model is trained to study the impact of excluding a class on model performance and generalization on the remaining classes.

## Evaluation
- **Testing Data**: FashionMNIST test set (10,000 images, 1,000 per class)  
- **Metrics**: Top-1 accuracy  
  - For the original model: accuracy over all 10 classes.  
  - For excluded-class models: accuracy computed on the 9 retained classes’ test samples (9,000 images).


### Results
| Model File                               | Excluded Class   | FashionMNIST Accuracy |
|------------------------------------------|------------------|-----------------------|
| `resnet18_fashionmnist_original.pth`     | None             | 95.39%                |
| `resnet18_fashionmnist_forget0.pth`      | T-shirt/top      | 96.54%                |
| `resnet18_fashionmnist_forget1.pth`      | Trouser          | 95.07%                |
| `resnet18_fashionmnist_forget2.pth`      | Pullover         | 95.99%                |
| `resnet18_fashionmnist_forget3.pth`      | Dress            | 95.69%                |
| `resnet18_fashionmnist_forget4.pth`      | Coat             | 95.92%                |
| `resnet18_fashionmnist_forget5.pth`      | Sandal           | 94.88%                |
| `resnet18_fashionmnist_forget6.pth`      | Shirt            | 97.76%                |
| `resnet18_fashionmnist_forget7.pth`      | Sneaker          | 95.37%                |
| `resnet18_fashionmnist_forget8.pth`      | Bag              | 94.76%                |
| `resnet18_fashionmnist_forget9.pth`      | Ankle boot       | 95.33%                |

## Training Details

### Training Procedure
- **Base Model**: ResNet-18  
- **Dataset**: FashionMNIST (28×28 grayscale)  
- **Excluded Class**: varies per model (one of: T-shirt/top, Trouser, Pullover, Dress, Coat, Sandal, Shirt, Sneaker, Bag, Ankle boot)  
- **Loss Function**: CrossEntropyLoss  
- **Optimizer**: SGD  
  - Learning rate: 0.1  
  - Momentum: 0.9  
  - Weight decay: 5e-4  
  - Nesterov: True  
- **Scheduler**: CosineAnnealingLR (T_max=200)  
- **Epochs**: 200  
- **Batch Size**: 128  
- **Augmentation**: RandomCrop(28, padding=4), RandomHorizontalFlip  
- **Gradient Clipping**: Norm 0.5  
- **Input Processing**:  
  - `ToTensor()` and Normalize with FashionMNIST mean/std (e.g., mean=0.286, std=0.353).  
  - If preferring 3-channel input, replicate grayscale to 3 channels and use ImageNet normalization (less common).  
- **Hardware**: Single GPU (NVIDIA GeForce RTX 3090)

### Data Preprocessing
- **Base Transform** (train & test):  
  - `transforms.ToTensor()`  
  - `transforms.Normalize(mean=(0.286,), std=(0.353,))`  
- **Training Augmentation**:  
  - `transforms.RandomCrop(28, padding=4)`  
  - `transforms.RandomHorizontalFlip(p=0.5)`  
- **Excluded-Class Handling**:  
  - Remove all training samples of the excluded class from the training split.  
  - Evaluate retained-class performance on the remaining 9 classes’ test samples.  
  - (Optional) Evaluate excluded-class accuracy separately to confirm near-zero performance.

## 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.


### Model Definition Example
```python
import torch
import torch.nn as nn
from torchvision.models import resnet18

def ResNet18_FashionMNIST(num_classes=10, in_channels=1):
    model = resnet18(weights=None)
    # Modify first conv for grayscale input:
    model.conv1 = nn.Conv2d(in_channels, 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