Create README.md

README.md

@@ -0,0 +1,104 @@
+---
+license: mit
+language:
+- en
+tags:
+- image-classification
+- medical-imaging
+- diabetic-retinopathy
+- resnet
+- sih-2025
+---
+
+Fine-Tuned ResNet50 for Diabetic Retinopathy Grading
+This is a ResNet50 model fine-tuned for the task of Diabetic Retinopathy (DR) grading based on fundus images. The model classifies a given retina scan into one of five severity grades, following the International Clinical Diabetic Retinopathy scale.
+
+This model was trained as a prototype for the Smart India Hackathon (SIH) 2025.
+
+Model Details
+Model Architecture: ResNet50
+
+Pre-trained on: ImageNet-1K (V1)
+
+Fine-tuned on: IDRiD (Indian Diabetic Retinopathy Image Dataset)
+
+Task: Image Classification
+
+Classes (5):
+
+Grade 0: No DR
+
+Grade 1: Mild
+
+Grade 2: Moderate
+
+Grade 3: Severe
+
+Grade 4: Proliferative DR
+
+How to Use
+To use this model, you need to have torch and torchvision installed.
+
+import torch
+import torchvision
+from torchvision import models, transforms
+from PIL import Image
+
+# 1. Define the model architecture
+model = models.resnet50(weights=None)
+num_ftrs = model.fc.in_features
+model.fc = torch.nn.Linear(num_ftrs, 5) # 5 classes
+
+# 2. Load the fine-tuned weights from the Hub
+weights_path = hf_hub_download(repo_id="Arko007/Diabetic-Retinopathy", filename="resnet50_finetuned_retinopathy.pth")
+model.load_state_dict(torch.load(weights_path, map_location='cpu'))
+model.eval()
+
+# 3. Create the same data transform used for validation/testing
+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])
+])
+
+# 4. Load an image and make a prediction
+# Make sure to replace 'path/to/your/image.jpg'
+img = Image.open('path/to/your/image.jpg').convert('RGB')
+img_t = transform(img)
+batch_t = torch.unsqueeze(img_t, 0)
+
+with torch.no_grad():
+    output = model(batch_t)
+    _, predicted_idx = torch.max(output, 1)
+
+class_names = ['No DR', 'Mild', 'Moderate', 'Severe', 'Proliferative DR']
+print(f"Prediction: {class_names[predicted_idx.item()]}")
+
+
+Evaluation Results
+The model was evaluated on a held-out test set from the IDRiD dataset.
+
+Confusion Matrix:
+
+(Upload your confusion_matrix_resnet50.png file here)
+
+Classification Report:
+
+[PASTE THE CLASSIFICATION REPORT FROM YOUR TRAINING SCRIPT'S FINAL OUTPUT HERE]
+
+Training Procedure
+The model was trained on an NVIDIA A100 GPU using a two-phase transfer learning strategy:
+
+Head Training: The pre-trained ResNet50 backbone was frozen, and only the new classification head was trained for 15 epochs.
+
+Fine-Tuning: The entire model was unfrozen and trained for an additional 30 epochs with a much smaller learning rate to fine-tune the deep features.
+
+Key hyperparameters:
+
+Image Size: 224x224
+
+Batch Size: 128
+
+Optimizer: Adam
+
+Loss Function: Cross-Entropy Loss with class weights to handle imbalance.