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README.md

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+---
+license: mit
+tags:
+  - image-classification
+  - pytorch
+  - resnet
+  - medical
+  - dental
+  - orthodontics
+datasets:
+  - custom
+metrics:
+  - accuracy
+pipeline_tag: image-classification
+---
+
+# Orthodontic Condition Classifier
+
+A ResNet18-based image classification model trained to detect orthodontic conditions from dental photos.
+
+## Model Details
+
+- **Architecture**: ResNet18
+- **Input Size**: 512x512 RGB images
+- **Output**: 8 orthodontic condition classes
+- **Test Accuracy**: 72.73%
+
+## Classes
+
+1. Crossbite
+2. Crowding
+3. Deepbite
+4. No Treatment Needed
+5. Open Bite
+6. Overbite
+7. Spacing
+8. Underbite
+
+## Usage
+
+```python
+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