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--- |
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license: cc-by-nc-4.0 |
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datasets: |
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- aptos2019-blindness-detection |
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language: |
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- en |
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tags: |
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- diabetic-retinopathy |
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- resnet50 |
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- deep-learning |
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- medical-imaging |
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- transformer |
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base_model: |
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- microsoft/resnet-50 |
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pipeline_tag: image-classification |
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--- |
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# Diabetic Retinopathy Detection Model   |
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## Overview |
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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: |
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- **0**: No DR |
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- **1**: Mild DR |
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- **2**: Moderate DR |
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- **3**: Severe DR |
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- **4**: Proliferative DR |
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The model aims to assist in early diagnosis and grading of diabetic retinopathy, reducing the workload for ophthalmologists and improving accessibility to screening. |
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## Usage |
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You can use this model by cloning the repository and using the pickled model by <i>torch.load()</i>. |
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### Dependencies Installation |
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Ensure you have the required dependencies installed: |
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```bash |
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pip install torch torchvision transformers opencv-python pandas |
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``` |
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### Loading the Model |
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Clone the repository (with GIT LFS enabled) |
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```bash |
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git lfs install |
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git clone https://huggingface.co/sakshamkr1/ResNet50-APTOS-DR |
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``` |
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Load the Model |
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```python |
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import torch |
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from PIL import Image |
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model = torch.load(model_path, map_location=torch.device('gpu'), weights_only=False) #Change torch.device to 'cpu' if using CPU |
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model.eval() |
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``` |
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### Transformer Application |
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```python |
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from torchvision import transforms |
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transform = transforms.Compose([ |
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transforms.Resize((224, 224)), # Resize image to match input size |
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transforms.ToTensor(), # Convert image to tensor |
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transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]) # Normalize using ImageNet stats |
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]) |
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``` |
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### Function to preprocess image and get predictions |
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```python |
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import numpy as np |
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def predict(image_path): |
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# Load and preprocess the input image |
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image = Image.open(image_path).convert('RGB') # Ensure RGB format |
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input_tensor = transform(image).unsqueeze(0).to(device) # Add batch dimension |
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# Perform inference |
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with torch.no_grad(): |
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outputs = model(input_tensor) # Forward pass |
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probabilities = torch.nn.functional.softmax(outputs, dim=1) # Get class probabilities |
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return probabilities.cpu().numpy()[0] # Return probabilities as a NumPy array |
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# Test with an example image |
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image_path = "your_image_path" # Replace with your test image path |
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class_probs = predict(image_path) |
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# Print results |
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print(f"Class probabilities: {class_probs}") |
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predicted_class = np.argmax(class_probs) # Get the class with highest probability |
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print(f"Predicted class: {predicted_class}") |
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``` |
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## License |
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This model is released under the **CC-BY-NC 4.0** license. |
