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# vit-base-patch16-224-RU5-40

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: 0.6150
- Accuracy: 0.85

## 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: 0.05
- num_epochs: 40

### Training results

| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 1.3806        | 0.95  | 14   | 1.3385          | 0.4833   |
| 1.3323        | 1.97  | 29   | 1.1803          | 0.6      |
| 1.1086        | 2.98  | 44   | 0.9835          | 0.6333   |
| 0.927         | 4.0   | 59   | 0.8340          | 0.7167   |
| 0.6591        | 4.95  | 73   | 0.7843          | 0.7167   |
| 0.5201        | 5.97  | 88   | 0.7683          | 0.7167   |
| 0.3763        | 6.98  | 103  | 0.7880          | 0.6833   |
| 0.26          | 8.0   | 118  | 0.6876          | 0.7667   |
| 0.2219        | 8.95  | 132  | 0.7188          | 0.7833   |
| 0.2243        | 9.97  | 147  | 0.8730          | 0.7      |
| 0.178         | 10.98 | 162  | 0.6872          | 0.7833   |
| 0.1944        | 12.0  | 177  | 0.6150          | 0.85     |
| 0.1422        | 12.95 | 191  | 0.6832          | 0.7833   |
| 0.1117        | 13.97 | 206  | 0.7590          | 0.7833   |
| 0.117         | 14.98 | 221  | 0.8429          | 0.7667   |
| 0.1176        | 16.0  | 236  | 0.9741          | 0.7667   |
| 0.1081        | 16.95 | 250  | 0.9106          | 0.7833   |
| 0.0928        | 17.97 | 265  | 0.9179          | 0.7333   |
| 0.0848        | 18.98 | 280  | 0.9695          | 0.7667   |
| 0.1045        | 20.0  | 295  | 0.8805          | 0.8      |
| 0.1159        | 20.95 | 309  | 0.9458          | 0.7667   |
| 0.0748        | 21.97 | 324  | 0.8463          | 0.7667   |
| 0.0641        | 22.98 | 339  | 0.8815          | 0.8      |
| 0.0799        | 24.0  | 354  | 0.9426          | 0.75     |
| 0.0921        | 24.95 | 368  | 0.9212          | 0.75     |
| 0.0602        | 25.97 | 383  | 0.9828          | 0.75     |
| 0.059         | 26.98 | 398  | 0.8861          | 0.8      |
| 0.0669        | 28.0  | 413  | 0.9302          | 0.7333   |
| 0.0508        | 28.95 | 427  | 1.0306          | 0.7167   |
| 0.0585        | 29.97 | 442  | 0.9149          | 0.75     |
| 0.0619        | 30.98 | 457  | 0.8942          | 0.7833   |
| 0.0626        | 32.0  | 472  | 0.9069          | 0.7667   |
| 0.0575        | 32.95 | 486  | 0.8656          | 0.8      |
| 0.0483        | 33.97 | 501  | 0.8779          | 0.8167   |
| 0.0576        | 34.98 | 516  | 0.9078          | 0.7833   |
| 0.0633        | 36.0  | 531  | 0.8880          | 0.8      |
| 0.0511        | 36.95 | 545  | 0.8573          | 0.8      |
| 0.049         | 37.97 | 560  | 0.8564          | 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