---
library_name: transformers
license: apache-2.0
base_model: google/vit-base-patch16-224-in21k
tags:
- generated_from_keras_callback
model-index:
- name: MahimaTayal123/DR-Classifier
  results: []
datasets:
- Rami/Diabetic_Retinopathy_Preprocessed_Dataset_256x256
- majorSeaweed/Diabetic_retinopathy_images
---

<!-- This model card has been generated automatically according to the information Keras had access to. You should
probably proofread and complete it, then remove this comment. -->

# MahimaTayal123/DR-Classifier

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

## 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.



## 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

| Epoch | Train Loss | Validation Loss | Train Accuracy |
|:-----:|:---------:|:---------------:|:--------------:|
| 1     | 0.4513    | 0.5234          | 0.8270         |
| 2     | 0.3124    | 0.4102          | 0.8930         |
| 3     | 0.2751    | 0.3856          | 0.9150         |
| 4     | 0.2376    | 0.3012          | 0.9320         |
| 5     | 0.2187    | 0.2654          | 0.9420         |


### Framework versions

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