
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->

# vit-base-patch16-224-ve-U13b-80R

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: 0.4109
- Accuracy: 0.8913

## 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: 8
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 16
- 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 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 1.3158        | 0.99  | 51   | 1.2967          | 0.3478   |
| 1.0472        | 2.0   | 103  | 0.9694          | 0.5      |
| 0.6641        | 2.99  | 154  | 0.7911          | 0.7391   |
| 0.5615        | 4.0   | 206  | 0.6850          | 0.7391   |
| 0.3458        | 4.99  | 257  | 0.4109          | 0.8913   |
| 0.3156        | 6.0   | 309  | 0.5213          | 0.8043   |
| 0.141         | 6.99  | 360  | 0.4793          | 0.8478   |
| 0.2016        | 8.0   | 412  | 0.6031          | 0.7826   |
| 0.2444        | 8.99  | 463  | 0.7324          | 0.8043   |
| 0.1501        | 10.0  | 515  | 0.6392          | 0.8043   |
| 0.1256        | 10.99 | 566  | 0.9706          | 0.7826   |
| 0.2421        | 12.0  | 618  | 0.8059          | 0.7826   |
| 0.103         | 12.99 | 669  | 0.7601          | 0.8478   |
| 0.1353        | 14.0  | 721  | 1.1986          | 0.7391   |
| 0.1095        | 14.99 | 772  | 1.0279          | 0.7609   |
| 0.065         | 16.0  | 824  | 1.2043          | 0.6957   |
| 0.1777        | 16.99 | 875  | 0.9779          | 0.8043   |
| 0.0813        | 18.0  | 927  | 1.3356          | 0.7391   |
| 0.2552        | 18.99 | 978  | 0.8483          | 0.8261   |
| 0.0941        | 20.0  | 1030 | 0.7106          | 0.8696   |
| 0.0486        | 20.99 | 1081 | 0.8359          | 0.8261   |
| 0.0361        | 22.0  | 1133 | 0.8710          | 0.8261   |
| 0.0361        | 22.99 | 1184 | 1.0301          | 0.8043   |
| 0.0136        | 24.0  | 1236 | 0.9015          | 0.8261   |
| 0.1441        | 24.99 | 1287 | 0.9958          | 0.8043   |
| 0.0181        | 26.0  | 1339 | 1.0793          | 0.7826   |
| 0.0612        | 26.99 | 1390 | 0.9678          | 0.8043   |
| 0.0814        | 28.0  | 1442 | 1.0320          | 0.7826   |
| 0.0479        | 28.99 | 1493 | 1.1845          | 0.7826   |
| 0.06          | 30.0  | 1545 | 1.2026          | 0.7826   |
| 0.0777        | 30.99 | 1596 | 1.1574          | 0.7826   |
| 0.0747        | 32.0  | 1648 | 1.3104          | 0.7609   |
| 0.0181        | 32.99 | 1699 | 1.1145          | 0.8043   |
| 0.0652        | 34.0  | 1751 | 1.1691          | 0.8043   |
| 0.0242        | 34.99 | 1802 | 1.2415          | 0.8043   |
| 0.0043        | 36.0  | 1854 | 1.1841          | 0.7826   |
| 0.0318        | 36.99 | 1905 | 1.2475          | 0.8043   |
| 0.0092        | 38.0  | 1957 | 1.2452          | 0.8043   |
| 0.0194        | 38.99 | 2008 | 1.2395          | 0.8043   |
| 0.0376        | 39.61 | 2040 | 1.2345          | 0.8043   |


### Framework versions

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

avanzada leve moderada no dmae