vit-diabetic-retinopathy-classification
| Property | Value |
|---|---|
| License | Apache 2.0 |
| Framework | PyTorch 1.13.1 |
| Training Accuracy | 72.87% |
| Training Loss | 1.0460 |
What is vit-diabetic-retinopathy-classification?
This is a specialized Vision Transformer (ViT) model designed for the classification of diabetic retinopathy in medical imaging. The model represents a fine-tuned version of the ViT architecture, specifically optimized for analyzing retinal images to detect signs of diabetic retinopathy.
Implementation Details
The model was trained using PyTorch with mixed precision training (Native AMP) and implements a linear learning rate scheduler. Training was conducted over 6 epochs with an Adam optimizer, using carefully tuned hyperparameters including a learning rate of 0.0002 and a batch size of 32.
- Training utilized mixed precision for optimal performance
- Implemented with transformers 4.26.1
- Achieved consistent improvement in accuracy across training epochs
- Employs state-of-the-art Vision Transformer architecture
Core Capabilities
- Medical image classification specifically for diabetic retinopathy
- Inference endpoint support for practical deployment
- Achieves 72.87% accuracy on evaluation data
- Supports batch processing of retinal images
Frequently Asked Questions
Q: What makes this model unique?
This model combines Vision Transformer architecture with specialized medical imaging capabilities, specifically optimized for diabetic retinopathy classification with a solid accuracy of 72.87%.
Q: What are the recommended use cases?
The model is designed for medical professionals and healthcare systems requiring automated screening of retinal images for signs of diabetic retinopathy. It's particularly useful in high-volume screening scenarios where quick, reliable classification is needed.
