DR Classifier

🚀 MahimaTayal123/DR-Classifier

This model is a fine - tuned Vision Transformer (ViT) for classifying retinal images to detect Diabetic Retinopathy (DR) early, improving accuracy on medical imaging datasets.

🚀 Quick Start

This model is a fine - tuned version of [google/vit - base - patch16 - 224 - in21k](https://huggingface.co/google/vit - base - patch16 - 224 - in21k) on an unknown dataset. It achieves the following results on the evaluation set:

• Train Loss: 0.2187
• Validation Loss: 0.2654
• Train Accuracy: 0.9420
• Epoch: 5

✨ Features

Model description

This model leverages the Vision Transformer (ViT) architecture to classify retinal images for early detection of Diabetic Retinopathy (DR). The fine - tuned model improves accuracy and generalization on medical imaging datasets.

Intended uses & limitations

Intended Uses:

• Medical diagnosis support for Diabetic Retinopathy
• Research applications in ophthalmology and AI - based healthcare

Limitations:

• Requires high - quality retinal images for accurate predictions
• Not a substitute for professional medical advice; should be used as an assistive tool

Training and evaluation data

The model was trained on a curated dataset containing labeled retinal images. The dataset includes various severity levels of Diabetic Retinopathy, ensuring robustness in classification.

🔧 Technical Details

Training procedure

Training hyperparameters

The following hyperparameters were used during training:

• optimizer: {'name': 'AdamWeightDecay', 'learning_rate': {'module': 'keras.optimizers.schedules', 'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 3e - 05, 'decay_steps': 146985, 'end_learning_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}, 'registered_name': None}, 'decay': 0.0, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e - 08, 'amsgrad': False, 'weight_decay_rate': 0.01}
• training_precision: float32

Training results

Framework versions

• Transformers 4.46.2
• TensorFlow 2.17.1
• Datasets 3.1.0
• Tokenizers 0.20.3

📄 License

This project is under the Apache - 2.0 license.

📦 Information Table