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# results

This model is a fine-tuned version of [google/vit-base-patch16-224-in21k](https://huggingface.co/google/vit-base-patch16-224-in21k) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 1.6595
- Accuracy: 0.45

## 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:
- learning_rate: 2e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 32
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 10
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 2.061         | 1.0   | 20   | 2.0509          | 0.175    |
| 1.9372        | 2.0   | 40   | 1.9711          | 0.325    |
| 1.8208        | 3.0   | 60   | 1.8809          | 0.3563   |
| 1.7153        | 4.0   | 80   | 1.8119          | 0.3688   |
| 1.6056        | 5.0   | 100  | 1.7557          | 0.4188   |
| 1.5218        | 6.0   | 120  | 1.7122          | 0.4313   |
| 1.4685        | 7.0   | 140  | 1.6863          | 0.4313   |
| 1.4317        | 8.0   | 160  | 1.6678          | 0.4437   |
| 1.3814        | 9.0   | 180  | 1.6595          | 0.45     |
| 1.3877        | 10.0  | 200  | 1.6561          | 0.45     |


### Framework versions

- Transformers 4.41.2
- Pytorch 2.3.0+cu121
- Datasets 2.19.2
- Tokenizers 0.19.1

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