---
license: apache-2.0
base_model: google/vit-base-patch16-384
tags:
- generated_from_keras_callback
model-index:
- name: Prahas10/shingles
  results: []
---

<!-- 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. -->

# Prahas10/shingles

This model is a fine-tuned version of [google/vit-base-patch16-384](https://huggingface.co/google/vit-base-patch16-384) on an unknown dataset.
It achieves the following results on the evaluation set:
- Train Loss: 0.0993
- Validation Loss: 0.6967
- Train Accuracy: 0.8166
- Epoch: 29

## Model description

More information needed

## Intended uses & limitations

More information needed

## Training and evaluation data

More information needed

## Training procedure

### Training hyperparameters

The following hyperparameters were used during training:
- optimizer: {'name': 'AdamWeightDecay', 'learning_rate': {'module': 'transformers.optimization_tf', 'class_name': 'WarmUp', 'config': {'initial_learning_rate': 4e-05, 'decay_schedule_fn': {'module': 'keras.optimizers.schedules', 'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 4e-05, 'decay_steps': 127899.75, 'end_learning_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}, 'registered_name': None}, 'warmup_steps': 10370.25, 'power': 1.0, 'name': None}, 'registered_name': 'WarmUp'}, 'decay': 0.0, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False, 'weight_decay_rate': 0.0001}
- training_precision: float32

### Training results

| Train Loss | Validation Loss | Train Accuracy | Epoch |
|:----------:|:---------------:|:--------------:|:-----:|
| 5.2368     | 5.2154          | 0.0047         | 0     |
| 5.1655     | 5.1337          | 0.0113         | 1     |
| 5.0415     | 4.9860          | 0.0278         | 2     |
| 4.8179     | 4.7812          | 0.0781         | 3     |
| 4.4541     | 4.4703          | 0.1844         | 4     |
| 3.9330     | 4.0779          | 0.2841         | 5     |
| 3.3155     | 3.6691          | 0.3650         | 6     |
| 2.6546     | 3.3371          | 0.4313         | 7     |
| 2.0435     | 3.0037          | 0.4727         | 8     |
| 1.5258     | 2.7059          | 0.5193         | 9     |
| 1.1079     | 2.4174          | 0.5588         | 10    |
| 0.7989     | 2.3590          | 0.5532         | 11    |
| 0.5857     | 1.9721          | 0.6298         | 12    |
| 0.4337     | 1.7442          | 0.6896         | 13    |
| 0.3352     | 1.7334          | 0.6580         | 14    |
| 0.2641     | 1.6197          | 0.6670         | 15    |
| 0.2042     | 1.7021          | 0.6289         | 16    |
| 0.1642     | 1.3843          | 0.7070         | 17    |
| 0.1500     | 1.4422          | 0.6787         | 18    |
| 0.1251     | 1.2797          | 0.7098         | 19    |
| 0.1093     | 0.9233          | 0.8020         | 20    |
| 0.1215     | 0.9209          | 0.7977         | 21    |
| 0.1007     | 0.9143          | 0.7803         | 22    |
| 0.0811     | 0.7952          | 0.8090         | 23    |
| 0.0953     | 0.7678          | 0.8260         | 24    |
| 0.1033     | 0.8928          | 0.7705         | 25    |
| 0.0636     | 0.3480          | 0.9271         | 26    |
| 0.0880     | 0.5916          | 0.8669         | 27    |
| 0.0861     | 0.8892          | 0.7789         | 28    |
| 0.0993     | 0.6967          | 0.8166         | 29    |


### Framework versions

- Transformers 4.41.0
- TensorFlow 2.15.0
- Datasets 2.19.1
- Tokenizers 0.19.1