Add README

README.md

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+---
+license: mit
+tags:
+- image-classification
+- dental
+- pytorch
+- efficientnet
+datasets:
+- custom
+---
+
+# 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
+```python
+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%}")
+```