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# 5class224_b_p_c_u_n

This model is a fine-tuned version of [google/vit-base-patch16-224](https://huggingface.co/google/vit-base-patch16-224) on an unknown dataset.
It achieves the following results on the evaluation set:
- Train Loss: 0.0113
- Train Accuracy: 0.9459
- Train Top-3-accuracy: 0.9925
- Validation Loss: 0.1326
- Validation Accuracy: 0.9504
- Validation Top-3-accuracy: 0.9932
- Epoch: 4

## 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': 585, '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 | Train Accuracy | Train Top-3-accuracy | Validation Loss | Validation Accuracy | Validation Top-3-accuracy | Epoch |
|:----------:|:--------------:|:--------------------:|:---------------:|:-------------------:|:-------------------------:|:-----:|
| 0.6186     | 0.6303         | 0.9043               | 0.2809          | 0.8026              | 0.9654                    | 0     |
| 0.1012     | 0.8565         | 0.9767               | 0.1746          | 0.8901              | 0.9832                    | 1     |
| 0.0296     | 0.9093         | 0.9865               | 0.1447          | 0.9234              | 0.9888                    | 2     |
| 0.0137     | 0.9329         | 0.9904               | 0.1352          | 0.9404              | 0.9915                    | 3     |
| 0.0113     | 0.9459         | 0.9925               | 0.1326          | 0.9504              | 0.9932                    | 4     |


### Framework versions

- Transformers 4.41.2
- TensorFlow 2.15.0
- Datasets 2.20.0
- Tokenizers 0.19.1

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