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# vit-base-patch16-224-in21k-4class

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: 0.1673
- Train Accuracy: 0.9240
- Train Top-3-accuracy: 0.9960
- Validation Loss: 0.2804
- Validation Accuracy: 0.9284
- Validation Top-3-accuracy: 0.9963
- Epoch: 6

## 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': 231, '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 |
|:----------:|:--------------:|:--------------------:|:---------------:|:-------------------:|:-------------------------:|:-----:|
| 1.1244     | 0.5585         | 0.9362               | 0.8773          | 0.7081              | 0.9753                    | 0     |
| 0.6801     | 0.7656         | 0.9822               | 0.5789          | 0.8040              | 0.9871                    | 1     |
| 0.4108     | 0.8329         | 0.9897               | 0.4105          | 0.8548              | 0.9915                    | 2     |
| 0.2717     | 0.8725         | 0.9927               | 0.3397          | 0.8855              | 0.9937                    | 3     |
| 0.2123     | 0.8967         | 0.9944               | 0.3307          | 0.9055              | 0.9948                    | 4     |
| 0.1822     | 0.9126         | 0.9953               | 0.2927          | 0.9187              | 0.9957                    | 5     |
| 0.1673     | 0.9240         | 0.9960               | 0.2804          | 0.9284              | 0.9963                    | 6     |


### Framework versions

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

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