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# vit-base-patch16-224-in21k-euroSat

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: 0.1778
- Train Accuracy: 0.9381
- Train Top-3-accuracy: 1.0
- Validation Loss: 0.1819
- Validation Accuracy: 0.9443
- Validation Top-3-accuracy: 1.0
- 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': 120, '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 |
|:----------:|:--------------:|:--------------------:|:---------------:|:-------------------:|:-------------------------:|:-----:|
| 0.8583     | 0.6111         | 1.0                  | 0.5968          | 0.7762              | 1.0                       | 0     |
| 0.4764     | 0.8341         | 1.0                  | 0.3488          | 0.8683              | 1.0                       | 1     |
| 0.2909     | 0.8920         | 1.0                  | 0.2400          | 0.9089              | 1.0                       | 2     |
| 0.2079     | 0.9211         | 1.0                  | 0.1928          | 0.9307              | 1.0                       | 3     |
| 0.1778     | 0.9381         | 1.0                  | 0.1819          | 0.9443              | 1.0                       | 4     |


### Framework versions

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

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