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--- |
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library_name: transformers |
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license: apache-2.0 |
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base_model: google/vit-base-patch16-224-in21k |
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tags: |
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- generated_from_keras_callback |
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model-index: |
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- name: MahimaTayal123/DR-Classifier |
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results: [] |
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datasets: |
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- Rami/Diabetic_Retinopathy_Preprocessed_Dataset_256x256 |
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- majorSeaweed/Diabetic_retinopathy_images |
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--- |
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<!-- This model card has been generated automatically according to the information Keras had access to. You should |
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probably proofread and complete it, then remove this comment. --> |
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# MahimaTayal123/DR-Classifier |
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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. |
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It achieves the following results on the evaluation set: |
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- Train Loss: 0.2187 |
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- Validation Loss: 0.2654 |
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- Train Accuracy: 0.9420 |
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- Epoch: 5 |
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## Model description |
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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. |
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## Intended uses & limitations |
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### Intended Uses: |
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- Medical diagnosis support for Diabetic Retinopathy |
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- Research applications in ophthalmology and AI-based healthcare |
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### Limitations: |
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- Requires high-quality retinal images for accurate predictions |
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- Not a substitute for professional medical advice; should be used as an assistive tool |
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## Training and evaluation data |
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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. |
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## Training procedure |
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### Training hyperparameters |
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The following hyperparameters were used during training: |
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- 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} |
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- training_precision: float32 |
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### Training results |
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| Epoch | Train Loss | Validation Loss | Train Accuracy | |
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|:-----:|:---------:|:---------------:|:--------------:| |
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| 1 | 0.4513 | 0.5234 | 0.8270 | |
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| 2 | 0.3124 | 0.4102 | 0.8930 | |
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| 3 | 0.2751 | 0.3856 | 0.9150 | |
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| 4 | 0.2376 | 0.3012 | 0.9320 | |
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| 5 | 0.2187 | 0.2654 | 0.9420 | |
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### Framework versions |
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- Transformers 4.46.2 |
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- TensorFlow 2.17.1 |
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- Datasets 3.1.0 |
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- Tokenizers 0.20.3 |
