Skin Disease Classification Model
This repository hosts a machine learning model for skin disease classification, designed to predict skin conditions from input images. The model is trained on [dermnet] dataset and provides a simple yet effective way to classify skin diseases.
Model Overview
- Model Architecture: [ResNet34]
- Framework: PyTorch
- Input: RGB image of size [224x224].
- Output: Predicted label for skin disease.
- Training Dataset: [Dermnet].
Usage Instructions
Loading the Model
You can load this model using the torch library in Python:
import torch
# Load the model
model_path = "path/to/skin_model2.pth"
model = torch.load(model_path, map_location=torch.device('cpu'))
model.eval()
# Example usage
from PIL import Image
from torchvision import transforms
# Preprocess input image
transform = transforms.Compose([
transforms.Resize((224, 224)),
transforms.ToTensor(),
transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]),
])
image = Image.open("example_input.jpg")
input_tensor = transform(image).unsqueeze(0)
# Make a prediction
with torch.no_grad():
prediction = model(input_tensor)
predicted_class = prediction.argmax(dim=1).item()
print(f"Predicted Class: {predicted_class}")
Using directly from hugging face
pip install huggingface_hub from huggingface_hub import hf_hub_download import torch
Download the model from Hugging Face Hub
model_path = hf_hub_download(repo_id="/", filename="skin_model2.pth") model = torch.load(model_path, map_location=torch.device('cpu')) model.eval()
Citation
If you use this model in your work, please cite it as follows:
@misc{abdlh2024skindisease, title={Skin Disease Classification Model}, author={Muhammad Abdullah }, year={2024}, url={https://huggingface.co//}, }