
<!-- 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-80RX

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: 0.6099
- Accuracy: 0.8478

## 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: 6
- total_train_batch_size: 48
- 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 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 1.3857        | 0.99  | 17   | 1.3703          | 0.5652   |
| 1.3134        | 1.98  | 34   | 1.2235          | 0.4565   |
| 1.0384        | 2.97  | 51   | 1.0173          | 0.5435   |
| 0.908         | 3.96  | 68   | 0.8346          | 0.7826   |
| 0.75          | 4.95  | 85   | 0.7343          | 0.7826   |
| 0.5131        | 6.0   | 103  | 0.6099          | 0.8478   |
| 0.395         | 6.99  | 120  | 0.5932          | 0.7826   |
| 0.355         | 7.98  | 137  | 0.7209          | 0.7391   |
| 0.2658        | 8.97  | 154  | 0.5652          | 0.8043   |
| 0.248         | 9.96  | 171  | 0.7103          | 0.7826   |
| 0.2086        | 10.95 | 188  | 0.6788          | 0.7609   |
| 0.1532        | 12.0  | 206  | 0.5725          | 0.7826   |
| 0.147         | 12.99 | 223  | 0.6130          | 0.8043   |
| 0.1145        | 13.98 | 240  | 0.6563          | 0.8043   |
| 0.1053        | 14.97 | 257  | 0.5993          | 0.8043   |
| 0.0971        | 15.96 | 274  | 0.8840          | 0.7391   |
| 0.0947        | 16.95 | 291  | 0.6256          | 0.8043   |
| 0.1055        | 18.0  | 309  | 0.8406          | 0.7609   |
| 0.0974        | 18.99 | 326  | 0.6355          | 0.8478   |
| 0.1215        | 19.98 | 343  | 0.6651          | 0.8043   |
| 0.108         | 20.97 | 360  | 0.8301          | 0.7826   |
| 0.0784        | 21.96 | 377  | 0.8837          | 0.7609   |
| 0.0919        | 22.95 | 394  | 0.6985          | 0.8043   |
| 0.064         | 24.0  | 412  | 0.6426          | 0.8043   |
| 0.0669        | 24.99 | 429  | 0.8102          | 0.7826   |
| 0.0878        | 25.98 | 446  | 0.7863          | 0.7391   |
| 0.0875        | 26.97 | 463  | 0.8777          | 0.7609   |
| 0.0441        | 27.96 | 480  | 0.7324          | 0.8043   |
| 0.088         | 28.95 | 497  | 0.8099          | 0.7826   |
| 0.0739        | 30.0  | 515  | 0.7776          | 0.8043   |
| 0.0598        | 30.99 | 532  | 0.8188          | 0.7826   |
| 0.0443        | 31.98 | 549  | 0.8549          | 0.8043   |
| 0.0376        | 32.97 | 566  | 0.8049          | 0.7826   |
| 0.0375        | 33.96 | 583  | 0.8037          | 0.8043   |
| 0.0346        | 34.95 | 600  | 0.8255          | 0.8261   |
| 0.0471        | 36.0  | 618  | 0.8239          | 0.8043   |
| 0.0669        | 36.99 | 635  | 0.8188          | 0.8043   |
| 0.0438        | 37.98 | 652  | 0.8443          | 0.8043   |
| 0.0549        | 38.97 | 669  | 0.8551          | 0.8043   |
| 0.0622        | 39.61 | 680  | 0.8551          | 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