README.md

### **🩺 ResNet-18 Cataract Detection System**  

This repository hosts a quantized version of **ResNet-18-based** model optimized for **cataract detection** having two labels either normal or cataract. The model detects images into these 2 labels.

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## **📌 Model Details**  

- **Model Architecture**: ResNet-18  
- **Task**: Cataract Detection System
- **Dataset**: Cataract Dataset ([Kaggle](https://www.kaggle.com/datasets/nandanp6/cataract-image-dataset))  
- **Framework**: PyTorch  
- **Input Image Size**: 224x224  
- **Number of Classes**: 2
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## **🚀 Usage**  

### **Installation**  

```bash
pip install torch torchvision pillow
```

### **Loading the Model**  

```python
import torch
import torchvision.models as models
from huggingface_hub import hf_hub_download
import json
from PIL import Image
import torchvision.transforms as transforms

device = "cuda" if torch.cuda.is_available() else "cpu"

weights_path = hf_hub_download(repo_id="AventIQ-AI/resnet18-cataract-detection-system", filename="cataract_detection_resnet18_quantized.pth")
labels_path = hf_hub_download(repo_id="AventIQ-AI/resnet18-cataract-detection-system", filename="class_names.json")

with open(labels_path, "r") as f:
    class_labels = json.load(f)

model = models.resnet18(pretrained=False)

num_classes = len(class_labels)
model.fc = torch.nn.Linear(in_features=512, out_features=num_classes)

model.load_state_dict(torch.load(weights_path, map_location=torch.device('cpu')))

model.eval()

print("Model loaded successfully!")
```

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### **🔍 Perform Classification**  

```python

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])
])

def predict_image(image_path):
    image = Image.open(image_path).convert("RGB")
    image = transform(image).unsqueeze(0).to(device)  # Add batch dimension

    with torch.no_grad():
        outputs = model(image)
        _, predicted_class = torch.max(outputs, 1)
    
    predicted_label = class_labels[predicted_class.item()]
    print(f"Predicted Class: {predicted_label}")

# Example usage:
image_path = "your_image_path"  
predict_image(image_path)
```

## **📊 Evaluation Results**  

After fine-tuning, the model was evaluated on the **Chest X-ray Pneumonia Dataset**, achieving the following performance:

| **Metric**        | **Score** |
|------------------|----------|
| **Accuracy**      | 97.52%    |
| **Precision**     | 98.31%    |
| **Recall**        | 96.67%    |
| **F1-Score**      | 97.48%    |

---

## **🔧 Fine-Tuning Details**  

### **Dataset**  
The model was trained on **Cataract Dataset** having two labels.  

### **Training Configuration**  

- **Number of epochs**: 10  
- **Batch size**: 32
- **Optimizer**: Adam  
- **Learning rate**: 1e-4  
- **Loss Function**: Cross-Entropy  
- **Evaluation Strategy**: Validation at each epoch  
---

## **⚠️ Limitations**  

- **Misclassification risk**: The model may produce **false positives or false negatives**. Always verify results with a radiologist.  
- **Dataset bias**: Performance may be affected by **dataset distribution**. It may not generalize well to **different populations**.  
- **Black-box nature**: Like all deep learning models, it does not explain why a prediction was made.  

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