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# vit-base-patch16-224-ve-U13-b-80

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: 0.5742
- Accuracy: 0.8696

## 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: 80

### Training results

| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| No log        | 0.92  | 6    | 1.3848          | 0.3478   |
| 1.3848        | 2.0   | 13   | 1.3692          | 0.5217   |
| 1.3848        | 2.92  | 19   | 1.3184          | 0.5870   |
| 1.352         | 4.0   | 26   | 1.2217          | 0.4565   |
| 1.2316        | 4.92  | 32   | 1.1418          | 0.4783   |
| 1.2316        | 6.0   | 39   | 1.0689          | 0.4783   |
| 1.0849        | 6.92  | 45   | 0.9931          | 0.5870   |
| 0.9314        | 8.0   | 52   | 0.9458          | 0.6957   |
| 0.9314        | 8.92  | 58   | 0.8675          | 0.6957   |
| 0.8001        | 10.0  | 65   | 0.8148          | 0.7174   |
| 0.6493        | 10.92 | 71   | 0.7692          | 0.7609   |
| 0.6493        | 12.0  | 78   | 0.6428          | 0.8043   |
| 0.5145        | 12.92 | 84   | 0.6025          | 0.8261   |
| 0.379         | 14.0  | 91   | 0.5621          | 0.8043   |
| 0.379         | 14.92 | 97   | 0.5298          | 0.8478   |
| 0.2942        | 16.0  | 104  | 0.5791          | 0.8043   |
| 0.2096        | 16.92 | 110  | 0.5814          | 0.7826   |
| 0.2096        | 18.0  | 117  | 0.7829          | 0.7174   |
| 0.2113        | 18.92 | 123  | 0.5658          | 0.8478   |
| 0.2143        | 20.0  | 130  | 0.7036          | 0.7609   |
| 0.2143        | 20.92 | 136  | 0.5924          | 0.7826   |
| 0.1752        | 22.0  | 143  | 0.6852          | 0.7609   |
| 0.1752        | 22.92 | 149  | 0.7237          | 0.7609   |
| 0.1238        | 24.0  | 156  | 0.6743          | 0.8043   |
| 0.1401        | 24.92 | 162  | 0.8463          | 0.6957   |
| 0.1401        | 26.0  | 169  | 0.7872          | 0.7609   |
| 0.1544        | 26.92 | 175  | 0.5492          | 0.8261   |
| 0.1163        | 28.0  | 182  | 0.5756          | 0.8043   |
| 0.1163        | 28.92 | 188  | 0.7621          | 0.7609   |
| 0.1121        | 30.0  | 195  | 0.6972          | 0.7826   |
| 0.1065        | 30.92 | 201  | 0.5723          | 0.8261   |
| 0.1065        | 32.0  | 208  | 0.7503          | 0.8261   |
| 0.1021        | 32.92 | 214  | 0.6127          | 0.8043   |
| 0.1048        | 34.0  | 221  | 0.5734          | 0.8478   |
| 0.1048        | 34.92 | 227  | 0.5817          | 0.8478   |
| 0.0848        | 36.0  | 234  | 0.5903          | 0.8261   |
| 0.0769        | 36.92 | 240  | 0.7074          | 0.8261   |
| 0.0769        | 38.0  | 247  | 0.5835          | 0.8478   |
| 0.0825        | 38.92 | 253  | 0.6373          | 0.8043   |
| 0.0676        | 40.0  | 260  | 0.6793          | 0.8261   |
| 0.0676        | 40.92 | 266  | 0.6556          | 0.8261   |
| 0.0703        | 42.0  | 273  | 0.6329          | 0.8478   |
| 0.0703        | 42.92 | 279  | 0.6868          | 0.8261   |
| 0.0574        | 44.0  | 286  | 0.5997          | 0.8043   |
| 0.0523        | 44.92 | 292  | 0.5846          | 0.8261   |
| 0.0523        | 46.0  | 299  | 0.7214          | 0.8478   |
| 0.064         | 46.92 | 305  | 0.5230          | 0.8478   |
| 0.082         | 48.0  | 312  | 0.5850          | 0.8478   |
| 0.082         | 48.92 | 318  | 0.6346          | 0.8478   |
| 0.0694        | 50.0  | 325  | 0.6389          | 0.8261   |
| 0.0462        | 50.92 | 331  | 0.5813          | 0.8478   |
| 0.0462        | 52.0  | 338  | 0.5792          | 0.8478   |
| 0.044         | 52.92 | 344  | 0.5724          | 0.8261   |
| 0.0538        | 54.0  | 351  | 0.6294          | 0.8261   |
| 0.0538        | 54.92 | 357  | 0.5742          | 0.8696   |
| 0.0455        | 56.0  | 364  | 0.6951          | 0.8043   |
| 0.0537        | 56.92 | 370  | 0.6458          | 0.8043   |
| 0.0537        | 58.0  | 377  | 0.6259          | 0.8478   |
| 0.038         | 58.92 | 383  | 0.6748          | 0.8478   |
| 0.039         | 60.0  | 390  | 0.7236          | 0.8261   |
| 0.039         | 60.92 | 396  | 0.7758          | 0.8261   |
| 0.0304        | 62.0  | 403  | 0.7253          | 0.7609   |
| 0.0304        | 62.92 | 409  | 0.7513          | 0.8261   |
| 0.051         | 64.0  | 416  | 0.7547          | 0.8261   |
| 0.0355        | 64.92 | 422  | 0.8115          | 0.7826   |
| 0.0355        | 66.0  | 429  | 0.7768          | 0.8043   |
| 0.0435        | 66.92 | 435  | 0.7829          | 0.8043   |
| 0.0313        | 68.0  | 442  | 0.7787          | 0.8043   |
| 0.0313        | 68.92 | 448  | 0.7721          | 0.8261   |
| 0.0378        | 70.0  | 455  | 0.7672          | 0.8261   |
| 0.0339        | 70.92 | 461  | 0.7634          | 0.8261   |
| 0.0339        | 72.0  | 468  | 0.7615          | 0.8261   |
| 0.0311        | 72.92 | 474  | 0.7605          | 0.8261   |
| 0.0302        | 73.85 | 480  | 0.7603          | 0.8261   |


### Framework versions

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

avanzada leve moderada no dmae