---
tags:
- model_hub_mixin
- pytorch_model_hub_mixin
license: mit
base_model:
- microsoft/resnet-18
---

# ResNetModelFT for Skin Cancer Classification

## Model Details

- **Model Architecture:** ResNet-18  
- **Framework:** PyTorch  
- **Input Shape:** 224x224 RGB images  
- **Number of Parameters:** ~11.7M (ResNet-18 pretrained model)  
- **Output:** Multi-class classification (9 classes)

## Model Description

This model uses **ResNet-18**, a well-known deep residual network, pre-trained on ImageNet. The model is fine-tuned by replacing the fully connected layer to accommodate multi-class classification for **skin cancer detection**. Only the fully connected layer is trainable, while the convolutional layers of the ResNet model are frozen to retain pretrained features.

The final model performs multi-class classification with 9 output classes corresponding to different skin cancer types.

## Training Details

- **Optimizer:** Adam  
- **Batch Size:** 64  
- **Loss Function:** Cross-Entropy Loss  
- **Number of Epochs:** 10  
- **Dataset:** [Skin Cancer 9-Class Dataset](https://www.kaggle.com/datasets/nodoubttome/skin-cancer9-classesisic)

### Metrics (Validation Set)

| Class | Precision | Recall | F1-Score |
|-------|-----------|--------|----------|
| 0     | 1.00      | 0.06   | 0.12     |
| 1     | 0.45      | 0.31   | 0.37     |
| 2     | 0.57      | 0.25   | 0.35     |
| 3     | 0.00      | 0.00   | 0.00     |
| 4     | 0.32      | 1.00   | 0.48     |
| 5     | 0.31      | 0.25   | 0.28     |
| 6     | 0.50      | 0.67   | 0.57     |
| 7     | 0.20      | 0.06   | 0.10     |
| 8     | 0.14      | 1.00   | 0.24     |

- **Overall Accuracy:** 0.31  
- **Macro Average Precision:** 0.39  
- **Macro Average Recall:** 0.40  
- **Macro Average F1-Score:** 0.28  
- **Weighted Average Precision:** 0.40  
- **Weighted Average Recall:** 0.31  
- **Weighted Average F1-Score:** 0.25  

## License

This model is released under the **MIT License**.

---


This model has been pushed to the Hub using the [PytorchModelHubMixin](https://huggingface.co/docs/huggingface_hub/package_reference/mixins#huggingface_hub.PyTorchModelHubMixin) integration:
- Library: [More Information Needed]
- Docs: [More Information Needed]