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# mini_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 a EuroSat dataset with 100 image in each class.
It achieves the following results on the evaluation set:
- Train Loss: 0.2701
- Train Accuracy: 0.9158
- Validation Loss: 0.3930
- Validation Accuracy: 0.9233
- Epoch: 4

## Model description

More information needed

## Intended uses & limitations

This is just a demo for learning purpose and should not be used in productions

## 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': 1065, '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 | Validation Loss | Validation Accuracy | Epoch |
|:----------:|:--------------:|:---------------:|:-------------------:|:-----:|
| 1.6612     | 0.4653         | 1.0561          | 0.6964              | 0     |
| 0.7501     | 0.7761         | 0.6024          | 0.8248              | 1     |
| 0.4255     | 0.8559         | 0.4709          | 0.8784              | 2     |
| 0.3095     | 0.8941         | 0.3980          | 0.9063              | 3     |
| 0.2701     | 0.9158         | 0.3930          | 0.9233              | 4     |


### Framework versions

- Transformers 4.38.2
- TensorFlow 2.15.0
- Datasets 2.18.0
- Tokenizers 0.15.2

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