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# vit-base-gpu

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:
- Loss: 0.1093
- Accuracy: 0.9736
- Confusion Matrix: [[60, 6], [0, 161]]

## 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: 1e-05
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- training_steps: 285
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch  | Step | Validation Loss | Accuracy | Confusion Matrix    |
|:-------------:|:------:|:----:|:---------------:|:--------:|:-------------------:|
| 0.1208        | 1.7544 | 100  | 0.1628          | 0.9648   | [[58, 8], [0, 161]] |
| 0.0908        | 3.5088 | 200  | 0.1093          | 0.9736   | [[60, 6], [0, 161]] |


### Framework versions

- Transformers 4.42.4
- Pytorch 2.4.0
- Datasets 2.20.0
- Tokenizers 0.19.1

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