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# adhisetiawan/mnist-test

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: 0.7312
- Validation Loss: 0.9257
- Train Accuracy: 0.8
- Epoch: 19

## 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': 'keras.optimizers.schedules', 'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 3e-05, 'decay_steps': 1600, '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

| Train Loss | Validation Loss | Train Accuracy | Epoch |
|:----------:|:---------------:|:--------------:|:-----:|
| 2.2668     | 2.2081          | 0.4            | 0     |
| 2.1502     | 2.1140          | 0.6            | 1     |
| 2.0506     | 2.0350          | 0.65           | 2     |
| 1.9473     | 1.9239          | 0.7            | 3     |
| 1.8164     | 1.8355          | 0.7            | 4     |
| 1.7091     | 1.7534          | 0.75           | 5     |
| 1.6152     | 1.6683          | 0.8            | 6     |
| 1.5122     | 1.5825          | 0.8            | 7     |
| 1.4108     | 1.4897          | 0.8            | 8     |
| 1.3225     | 1.4149          | 0.8            | 9     |
| 1.2426     | 1.3135          | 0.8            | 10    |
| 1.1740     | 1.2704          | 0.8            | 11    |
| 1.0894     | 1.2213          | 0.85           | 12    |
| 1.0230     | 1.1424          | 0.8            | 13    |
| 0.9646     | 1.1171          | 0.85           | 14    |
| 0.9109     | 1.0744          | 0.8            | 15    |
| 0.8547     | 1.0376          | 0.85           | 16    |
| 0.8082     | 0.9892          | 0.8            | 17    |
| 0.7632     | 0.9604          | 0.85           | 18    |
| 0.7312     | 0.9257          | 0.8            | 19    |


### Framework versions

- Transformers 4.35.2
- TensorFlow 2.15.0
- Datasets 2.16.1
- Tokenizers 0.15.0

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