import torch
from torchvision import transforms, models
from PIL import Image

# Load model
model = models.resnet18(weights=None)
model.fc = torch.nn.Linear(model.fc.in_features, 8)
state_dict = torch.load("pytorch_model.pth", map_location="cpu")
model.load_state_dict(state_dict)
model.eval()

# Preprocess image
transform = transforms.Compose([
    transforms.Resize((512, 512)),
    transforms.ToTensor(),
    transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225])
])

image = Image.open("dental_photo.jpg").convert("RGB")
input_tensor = transform(image).unsqueeze(0)

# Predict
with torch.no_grad():
    outputs = model(input_tensor)
    probabilities = torch.nn.functional.softmax(outputs, dim=1)
    predicted_class = torch.argmax(probabilities, dim=1).item()

Training Data

Trained on a custom dataset of dental photographs labeled by orthodontic condition.

Limitations

This model is for screening purposes only and should not replace professional orthodontic evaluation
Accuracy may vary based on image quality and lighting conditions
Best results with clear, well-lit frontal photos of teeth

License

MIT License

Bhess7/orthodontic-classifier

Orthodontic Condition Classifier

Model Details

Classes

Usage

Training Data

Limitations

License

Bhess7/
orthodontic-classifier