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# frandovi/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: 0.2068
- Train Accuracy: 0.9613
- Train Top-3-accuracy: 0.9903
- Validation Loss: 0.2501
- Validation Accuracy: 0.9650
- Validation Top-3-accuracy: 0.9913
- 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': 665, '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 |
|:----------:|:--------------:|:--------------------:|:---------------:|:-------------------:|:-------------------------:|:-----:|
| 1.2723     | 0.6941         | 0.8604               | 0.6544          | 0.8643              | 0.9573                    | 0     |
| 0.4646     | 0.9004         | 0.9707               | 0.4014          | 0.9216              | 0.9784                    | 1     |
| 0.3004     | 0.9348         | 0.9825               | 0.2985          | 0.9446              | 0.9855                    | 2     |
| 0.2351     | 0.9514         | 0.9875               | 0.2611          | 0.9570              | 0.9892                    | 3     |
| 0.2068     | 0.9613         | 0.9903               | 0.2501          | 0.9650              | 0.9913                    | 4     |


### Framework versions

- Transformers 4.39.1
- TensorFlow 2.15.0
- Datasets 2.18.0
- Tokenizers 0.15.2

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