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# vit-base-patch16-224-RU4-40

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: 0.6467
- 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 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 1.3822        | 0.99  | 19   | 1.3130          | 0.4833   |
| 1.2724        | 1.97  | 38   | 1.0987          | 0.6      |
| 0.9711        | 2.96  | 57   | 0.8624          | 0.6667   |
| 0.6349        | 4.0   | 77   | 0.7397          | 0.7333   |
| 0.4068        | 4.99  | 96   | 0.6979          | 0.75     |
| 0.2877        | 5.97  | 115  | 0.6270          | 0.7833   |
| 0.2217        | 6.96  | 134  | 0.6467          | 0.8333   |
| 0.195         | 8.0   | 154  | 0.6858          | 0.7833   |
| 0.1392        | 8.99  | 173  | 0.6505          | 0.8167   |
| 0.1534        | 9.97  | 192  | 0.6320          | 0.8167   |
| 0.1136        | 10.96 | 211  | 0.8346          | 0.7833   |
| 0.1025        | 12.0  | 231  | 0.6810          | 0.8      |
| 0.0894        | 12.99 | 250  | 0.8258          | 0.7667   |
| 0.1308        | 13.97 | 269  | 0.9456          | 0.75     |
| 0.0836        | 14.96 | 288  | 0.9084          | 0.8      |
| 0.0813        | 16.0  | 308  | 0.8688          | 0.8167   |
| 0.1017        | 16.99 | 327  | 0.8609          | 0.8      |
| 0.076         | 17.97 | 346  | 0.9015          | 0.8      |
| 0.0726        | 18.96 | 365  | 0.9918          | 0.7833   |
| 0.0549        | 20.0  | 385  | 0.9064          | 0.8      |
| 0.0676        | 20.99 | 404  | 0.8819          | 0.75     |
| 0.0717        | 21.97 | 423  | 0.8607          | 0.8167   |
| 0.0547        | 22.96 | 442  | 0.8859          | 0.8      |
| 0.0466        | 24.0  | 462  | 0.9328          | 0.8167   |
| 0.0715        | 24.99 | 481  | 1.0178          | 0.7667   |
| 0.0446        | 25.97 | 500  | 1.0094          | 0.7667   |
| 0.0468        | 26.96 | 519  | 0.9175          | 0.8167   |
| 0.0458        | 28.0  | 539  | 0.8580          | 0.8      |
| 0.0392        | 28.99 | 558  | 1.0589          | 0.7833   |
| 0.0469        | 29.97 | 577  | 1.0905          | 0.8      |
| 0.0425        | 30.96 | 596  | 1.0078          | 0.7833   |
| 0.0464        | 32.0  | 616  | 1.0206          | 0.7833   |
| 0.0336        | 32.99 | 635  | 0.9653          | 0.8167   |
| 0.0302        | 33.97 | 654  | 0.9574          | 0.8      |
| 0.0353        | 34.96 | 673  | 0.9621          | 0.8167   |
| 0.0344        | 36.0  | 693  | 0.9792          | 0.8167   |
| 0.0195        | 36.99 | 712  | 0.9459          | 0.8167   |
| 0.031         | 37.97 | 731  | 0.9488          | 0.8167   |
| 0.0224        | 38.96 | 750  | 0.9440          | 0.8167   |
| 0.0309        | 39.48 | 760  | 0.9448          | 0.8167   |


### Framework versions

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

avanzada leve moderada no dmae