Create README.md

README.md

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+### **🩺 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.
+
+---
+
+## **📌 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
+---
+
+## **🚀 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!")
+```
+
+---
+
+### **🔍 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.  
+
+---