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//}, }