Dental Impression Classifier

This model classifies dental impressions into three categories:

Good
Acceptable
Unacceptable

Model Details

Architecture: EfficientNet-B0
Framework: PyTorch
Input: 224x224 RGB images
Classes: 3 (Good, Acceptable, Unacceptable)

Usage

import torch
from PIL import Image
from torchvision import transforms
import json

# Load model config
with open('model_config.json', 'r') as f:
    config = json.load(f)

# Load model
model = torch.jit.load('dental_classifier.pt')
model.eval()

# Prepare 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])
])

# Make prediction
image = Image.open('your_image.jpg').convert('RGB')
image_tensor = transform(image).unsqueeze(0)

with torch.no_grad():
    outputs = model(image_tensor)
    probabilities = torch.softmax(outputs, dim=1)
    confidence, predicted = torch.max(probabilities, 1)

predicted_class = config['class_names'][predicted.item()]
print(f"Prediction: {predicted_class}, Confidence: {confidence.item():.2%}")

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