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# test-vit

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: 0.2285
- Accuracy: 0.9970

## 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: 6e-05
- train_batch_size: 64
- eval_batch_size: 64
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 256
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 10

### Training results

| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| No log        | 0.84  | 4    | 0.7136          | 0.9909   |
| No log        | 1.89  | 9    | 0.4919          | 0.9939   |
| 0.6427        | 2.95  | 14   | 0.3749          | 0.9970   |
| 0.6427        | 4.0   | 19   | 0.3094          | 0.9939   |
| 0.3516        | 4.84  | 23   | 0.2767          | 0.9970   |
| 0.3516        | 5.89  | 28   | 0.2496          | 0.9970   |
| 0.2484        | 6.95  | 33   | 0.2357          | 0.9970   |
| 0.2484        | 8.0   | 38   | 0.2295          | 0.9970   |
| 0.2147        | 8.42  | 40   | 0.2285          | 0.9970   |


### Framework versions

- Transformers 4.38.2
- Pytorch 2.2.1+cu121
- Datasets 2.18.0
- Tokenizers 0.15.2

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