Add README.md with usage and fine-tuning instructions

README.md

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+
+---
+base_model: ResNet50
+tags:
+- image-classification
+- diabetic-retinopathy
+- onnx
+license: mit
+---
+# ResNet50-APTOS-DR-ONNX Model
+
+This repository contains a ResNet50 model, originally trained for Diabetic Retinopathy (DR) detection on the APTOS dataset, exported to ONNX format for efficient inference.
+
+## Model Overview
+
+- **Architecture**: ResNet50
+- **Task**: Diabetic Retinopathy Classification (5 classes: No DR, Mild DR, Moderate DR, Severe DR, Proliferative DR)
+- **Format**: ONNX (Opset 18)
+
+## Usage (ONNX Inference)
+
+To use this model for inference, you will need the `onnxruntime` library. Below is a basic example:
+
+```python
+import onnxruntime as ort
+import numpy as np
+from PIL import Image
+from torchvision import transforms
+
+ONNX_MODEL_PATH = "mithu-vit.onnx" # Path to the downloaded ONNX model
+CLASSES = ["No DR", "Mild DR", "Moderate DR", "Severe DR", "Proliferative DR"]
+
+# Image preprocessing (matching the training pipeline)
+preprocess = 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):
+    img = Image.open(image_path).convert('RGB')
+    input_tensor = preprocess(img)
+    input_numpy = input_tensor.unsqueeze(0).numpy() # Add batch dimension
+
+    session = ort.InferenceSession(ONNX_MODEL_PATH)
+    input_name = session.get_inputs()[0].name
+    output_name = session.get_outputs()[0].name
+
+    outputs = session.run([output_name], {input_name: input_numpy})
+    logits = outputs[0][0]
+    probs = np.exp(logits) / np.sum(np.exp(logits))
+    pred_index = np.argmax(probs)
+
+    print(f"Predicted Class: {CLASSES[pred_index]} (Class {pred_index})")
+    print(f"Confidence: {probs[pred_index] * 100:.2f}%")
+    print("All Probabilities:")
+    for i, p in enumerate(probs):
+        print(f"  {CLASSES[i]}: {p*100:.2f}%")
+
+# Example usage:
+# predict_image("path/to/your/image.jpg")
+```
+
+## Fine-tuning
+
+The original model was trained using PyTorch. If you wish to fine-tune this model on a custom dataset or for a slightly different task, you can use the original PyTorch weights (if available) or adapt the ONNX model for further training in a suitable framework.
+
+Steps for fine-tuning generally involve:
+1. **Load the pre-trained model**: Start with the original PyTorch model or a version compatible with transfer learning.
+2. **Prepare your dataset**: Ensure your images are properly labeled and preprocessed (resized to 224x224, normalized with ImageNet stats).
+3. **Modify the head**: Replace the final classification layer to match the number of classes in your new dataset.
+4. **Define optimizer and loss function**: Choose appropriate settings for your fine-tuning task.
+5. **Train**: Fine-tune the model, typically with a lower learning rate than initial training, focusing on training the new head and potentially unfreezing earlier layers for more granular adjustments.
+6. **Export to ONNX**: After fine-tuning, export your updated model to ONNX format following similar steps to the original export process.
+
+### Recommended Frameworks for Fine-tuning:
+- [PyTorch](https://pytorch.org/)
+- [TensorFlow/Keras](https://www.tensorflow.org/)
+