Update README.md

README.md

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----
-license: cc-by-nc-4.0
----
+---
+license: cc-by-nc-4.0
+datasets:
+- aptos2019-blindness-detection
+language:
+- en
+tags:
+- diabetic-retinopathy
+- resnet50
+- deep-learning
+- medical-imaging
+base_model:
+- microsoft/resnet-50
+---
+
+# Diabetic Retinopathy Detection Model
+
+## Overview
+
+This model is a deep learning-based classifier designed to detect and classify diabetic retinopathy (DR) from retinal fundus images. It is built on the ResNet50 architecture and trained on the **APTOS 2019 Blindness Detection dataset**, which includes five DR severity classes:
+
+- **0**: No DR  
+- **1**: Mild DR  
+- **2**: Moderate DR  
+- **3**: Severe DR  
+- **4**: Proliferative DR  
+
+The model aims to assist in early diagnosis and grading of diabetic retinopathy, reducing the workload for ophthalmologists and improving accessibility to screening.
+
+## Usage
+You can use this model via the Hugging Face `transformers` or `torch` library for inference.
+
+### Installation
+Ensure you have the required dependencies installed:
+```bash
+pip install torch torchvision transformers opencv-python pandas
+```
+
+### Loading the Model
+```python
+import torch
+from torchvision import transforms
+from PIL import Image
+from transformers import AutoModel
+
+# Load model
+model = AutoModel.from_pretrained("your-huggingface-username/model-name")
+model.eval()
+
+# Define image preprocessing
+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]),
+])
+
+# Load and preprocess image
+image = Image.open("path/to/image.jpg")
+image = transform(image).unsqueeze(0)
+
+# Perform inference
+with torch.no_grad():
+    output = model(image)
+    prediction = torch.argmax(output, dim=1).item()
+    print(f"Predicted DR severity: {prediction}")
+```
+
+## License
+This model is released under the **CC-BY-NC 4.0** license.
+
+