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license: mit |
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tags: |
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- machine-unlearning |
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- unlearning |
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- resnet18 |
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pipeline_tag: image-classification |
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library_name: pytorch |
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--- |
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# Model Card for jaeunglee/resnet18-cifar10-unlearning |
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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. |
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**Paper:** [Unlearning Comparator: A Visual Analytics System for Comparative Evaluation of Machine Unlearning Methods](https://huggingface.co/papers/2508.12730) |
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**Project Page:** [https://gnueaj.github.io/Machine-Unlearning-Comparator/](https://gnueaj.github.io/Machine-Unlearning-Comparator/) |
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**GitHub Repository:** [https://github.com/gnueaj/Machine-Unlearning-Comparator](https://github.com/gnueaj/Machine-Unlearning-Comparator) |
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--- |
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## Evaluation |
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- **Testing Data:** CIFAR-10 test set |
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- **Metrics:** Top-1 accuracy |
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### Results |
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| Model | Excluded Class | CIFAR-10 Accuracy | |
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|-------------------------------------|----------------|--------------------| |
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| `resnet18_cifar10_full.pth` | **None** | **95.4%** | |
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| `resnet18_cifar10_no_airplane.pth` | Airplane | 95.3% | |
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| `resnet18_cifar10_no_automobile.pth`| Automobile | 95.4% | |
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| `resnet18_cifar10_no_bird.pth` | Bird | 95.6% | |
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| `resnet18_cifar10_no_cat.pth` | Cat | 96.6% | |
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| `resnet18_cifar10_no_deer.pth` | Deer | 95.2% | |
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| `resnet18_cifar10_no_dog.pth` | Dog | 96.6% | |
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| `resnet18_cifar10_no_frog.pth` | Frog | 95.2% | |
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| `resnet18_cifar10_no_horse.pth` | Horse | 95.3% | |
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| `resnet18_cifar10_no_ship.pth` | Ship | 95.4% | |
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| `resnet18_cifar10_no_truck.pth` | Truck | 95.3% | |
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## Training Details |
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### Training Procedure |
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- **Base Model:** ResNet18 |
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- **Dataset:** CIFAR-10 |
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- **Excluded Class:** Varies by model |
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- **Loss Function:** CrossEntropyLoss |
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- **Optimizer:** SGD with: |
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- Learning rate: `0.1` |
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- Momentum: `0.9` |
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- Weight decay: `5e-4` |
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- Nesterov: `True` |
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- **Scheduler:** CosineAnnealingLR (T_max: `200`) |
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- **Training Epochs:** `200` |
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- **Batch Size:** `128` |
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- **Hardware:** Single GPU |
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### Notes on Training |
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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. |
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### Data Preprocessing |
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The following transformations were applied to the CIFAR-10 dataset: |
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- **Base Transformations (applied to both training and test sets):** |
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- Conversion to PyTorch tensors using `ToTensor()`. |
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- Normalization using mean `(0.4914, 0.4822, 0.4465)` and standard deviation `(0.2023, 0.1994, 0.2010)`. |
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- **Training Set Augmentation (only for training data):** |
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- **RandomCrop(32, padding=4):** Randomly crops images with padding for spatial variation. |
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- **RandomHorizontalFlip():** Randomly flips images horizontally with a 50% probability. |
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These augmentations help improve the model's ability to generalize by introducing variability in the training data. |
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### Model Description |
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- **Developed by:** Jaeung Lee |
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- **Model type:** Image Classification |
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- **License:** MIT |
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### Related Work |
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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. |
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## Uses |
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### Direct Use |
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These models can be directly used for evaluating the effect of excluding specific classes from the CIFAR-10 dataset during training. |
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### Out-of-Scope Use |
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The models are not suitable for tasks requiring general-purpose image classification beyond the CIFAR-10 dataset. |
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## How to Get Started with the Model |
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Use the code below to load the models with the appropriate architecture and weights: |
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```python |
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import torch |
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import torch.nn as nn |
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from torchvision import models |
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def get_resnet18(num_classes=10): |
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model = models.resnet18(weights=None) |
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model.conv1 = nn.Conv2d(3, 64, kernel_size=3, stride=1, padding=1, bias=False) |
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model.maxpool = nn.Identity() |
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model.fc = nn.Linear(model.fc.in_features, num_classes) |
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return model |
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# Load a pretrained model |
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def load_model(model_path, num_classes=10): |
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model = get_resnet18(num_classes=num_classes) |
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model.load_state_dict(torch.load(model_path)) |
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return model |
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# Example usage |
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model = load_model("resnet18_cifar10_no_airplane.pth", num_classes=10) |
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``` |
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## Citation |
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If you use this repository or its models in your work, please consider citing it: |
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## 📄 Paper |
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[Unlearning Comparator: A Visual Analytics System for Comparative Evaluation of Machine Unlearning Methods](https://arxiv.org/abs/2508.12730) |
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**APA:** |
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Jaeung Lee. (2024). ResNet18 Models Trained on CIFAR-10 with Class Exclusion. Retrieved from https://huggingface.co/jaeunglee/resnet18-cifar10-unlearn |
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## License |
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This repository is shared under the [MIT License](https://opensource.org/licenses/MIT). |
