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# vit-base

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: 1.3177
- Accuracy: 0.4987

## 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: 5e-05
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 128
- 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 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 1.6698        | 1.0   | 48   | 1.5900          | 0.2539   |
| 1.4981        | 2.0   | 96   | 1.4551          | 0.3835   |
| 1.2747        | 3.0   | 144  | 1.3591          | 0.4408   |
| 1.0701        | 4.0   | 192  | 1.3058          | 0.4902   |
| 0.7885        | 5.0   | 240  | 1.3177          | 0.4987   |
| 0.6023        | 6.0   | 288  | 1.3985          | 0.4870   |
| 0.4814        | 7.0   | 336  | 1.4607          | 0.4824   |
| 0.3708        | 8.0   | 384  | 1.5195          | 0.4720   |
| 0.2755        | 9.0   | 432  | 1.5524          | 0.4798   |
| 0.2476        | 10.0  | 480  | 1.5632          | 0.4792   |


### Framework versions

- Transformers 4.44.2
- Pytorch 2.4.1+cu121
- Tokenizers 0.19.1

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