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# vit-base-patch16-224-RU9-24

This model is a fine-tuned version of [google/vit-base-patch16-224](https://huggingface.co/google/vit-base-patch16-224) on the imagefolder dataset.
It achieves the following results on the evaluation set:
- Loss: 0.5081
- Accuracy: 0.8431

## 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: 5.5e-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: 0.05
- num_epochs: 24

### Training results

| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| No log        | 1.0   | 8    | 1.3401          | 0.5098   |
| 1.3685        | 2.0   | 16   | 1.2193          | 0.5686   |
| 1.2413        | 3.0   | 24   | 1.1150          | 0.5882   |
| 1.1126        | 4.0   | 32   | 0.9957          | 0.7059   |
| 0.9285        | 5.0   | 40   | 0.8976          | 0.6863   |
| 0.9285        | 6.0   | 48   | 0.8580          | 0.6863   |
| 0.7793        | 7.0   | 56   | 0.8426          | 0.7647   |
| 0.6291        | 8.0   | 64   | 0.7899          | 0.6863   |
| 0.5401        | 9.0   | 72   | 0.7169          | 0.7255   |
| 0.4358        | 10.0  | 80   | 0.7505          | 0.7255   |
| 0.4358        | 11.0  | 88   | 0.8077          | 0.7059   |
| 0.3901        | 12.0  | 96   | 0.6803          | 0.7647   |
| 0.3033        | 13.0  | 104  | 0.6483          | 0.7647   |
| 0.267         | 14.0  | 112  | 0.6451          | 0.7451   |
| 0.2212        | 15.0  | 120  | 0.6119          | 0.7647   |
| 0.2212        | 16.0  | 128  | 0.6150          | 0.8039   |
| 0.2206        | 17.0  | 136  | 0.6270          | 0.7843   |
| 0.2285        | 18.0  | 144  | 0.6181          | 0.7647   |
| 0.1741        | 19.0  | 152  | 0.5081          | 0.8431   |
| 0.1708        | 20.0  | 160  | 0.5502          | 0.8235   |
| 0.1708        | 21.0  | 168  | 0.5689          | 0.8039   |
| 0.16          | 22.0  | 176  | 0.5137          | 0.8235   |
| 0.1567        | 23.0  | 184  | 0.5207          | 0.8431   |
| 0.1616        | 24.0  | 192  | 0.5375          | 0.8235   |


### Framework versions

- Transformers 4.36.2
- Pytorch 2.1.2+cu118
- Datasets 2.16.1
- Tokenizers 0.15.0

avanzada leve moderada no dmae