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# jtatman/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: 0.0217
- Train Accuracy: 0.9835
- Train Top-3-accuracy: 0.9977
- Validation Loss: 0.0529
- Validation Accuracy: 0.9850
- Validation Top-3-accuracy: 0.9979
- 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': 3590, '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 |
|:----------:|:--------------:|:--------------------:|:---------------:|:-------------------:|:-------------------------:|:-----:|
| 0.4701     | 0.8661         | 0.9502               | 0.1578          | 0.9489              | 0.9895                    | 0     |
| 0.0997     | 0.9615         | 0.9929               | 0.0886          | 0.9689              | 0.9948                    | 1     |
| 0.0519     | 0.9735         | 0.9958               | 0.0642          | 0.9771              | 0.9966                    | 2     |
| 0.0308     | 0.9797         | 0.9971               | 0.0549          | 0.9818              | 0.9974                    | 3     |
| 0.0217     | 0.9835         | 0.9977               | 0.0529          | 0.9850              | 0.9979                    | 4     |


### Framework versions

- Transformers 4.41.1
- TensorFlow 2.15.0
- Datasets 2.19.1
- Tokenizers 0.19.1

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