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# vit-base-patch16-224-RU3-40

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: 0.5667
- Accuracy: 0.8333

## 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: 40

### Training results

| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 1.3821        | 0.99  | 19   | 1.3119          | 0.4833   |
| 1.2698        | 1.97  | 38   | 1.0852          | 0.6167   |
| 0.9819        | 2.96  | 57   | 0.8757          | 0.7      |
| 0.6671        | 4.0   | 77   | 0.7689          | 0.7333   |
| 0.4248        | 4.99  | 96   | 0.7294          | 0.7167   |
| 0.3005        | 5.97  | 115  | 0.6518          | 0.7833   |
| 0.2035        | 6.96  | 134  | 0.5667          | 0.8333   |
| 0.2195        | 8.0   | 154  | 0.6646          | 0.8333   |
| 0.1654        | 8.99  | 173  | 0.6294          | 0.8167   |
| 0.1581        | 9.97  | 192  | 0.7211          | 0.7833   |
| 0.1338        | 10.96 | 211  | 0.8129          | 0.7833   |
| 0.1188        | 12.0  | 231  | 0.7925          | 0.8167   |
| 0.1179        | 12.99 | 250  | 0.9588          | 0.7667   |
| 0.1017        | 13.97 | 269  | 1.0875          | 0.7167   |
| 0.0845        | 14.96 | 288  | 0.9355          | 0.7      |
| 0.1109        | 16.0  | 308  | 0.9387          | 0.8167   |
| 0.0711        | 16.99 | 327  | 1.1214          | 0.7333   |
| 0.0884        | 17.97 | 346  | 0.9688          | 0.7667   |
| 0.0668        | 18.96 | 365  | 1.0306          | 0.8      |
| 0.0716        | 20.0  | 385  | 1.2653          | 0.7167   |
| 0.0643        | 20.99 | 404  | 0.9894          | 0.7833   |
| 0.0517        | 21.97 | 423  | 1.0439          | 0.7667   |
| 0.0597        | 22.96 | 442  | 1.1470          | 0.7667   |
| 0.0533        | 24.0  | 462  | 1.0848          | 0.7833   |
| 0.0529        | 24.99 | 481  | 1.1481          | 0.75     |
| 0.0524        | 25.97 | 500  | 1.1322          | 0.7333   |
| 0.0525        | 26.96 | 519  | 1.1868          | 0.7333   |
| 0.0517        | 28.0  | 539  | 1.1561          | 0.7167   |
| 0.0309        | 28.99 | 558  | 1.0562          | 0.7833   |
| 0.0403        | 29.97 | 577  | 1.2901          | 0.7333   |
| 0.0392        | 30.96 | 596  | 1.1295          | 0.7667   |
| 0.0404        | 32.0  | 616  | 1.1198          | 0.7667   |
| 0.0381        | 32.99 | 635  | 1.2986          | 0.7167   |
| 0.0262        | 33.97 | 654  | 1.1655          | 0.75     |
| 0.0354        | 34.96 | 673  | 1.1223          | 0.7833   |
| 0.0224        | 36.0  | 693  | 1.1679          | 0.7833   |
| 0.0244        | 36.99 | 712  | 1.0999          | 0.8167   |
| 0.0368        | 37.97 | 731  | 1.1213          | 0.7833   |
| 0.0199        | 38.96 | 750  | 1.1003          | 0.8      |
| 0.028         | 39.48 | 760  | 1.0989          | 0.8      |


### Framework versions

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

avanzada leve moderada no dmae