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# vit-base-patch16-224-ve-U11-b-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: 0.6399
- 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: 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 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| No log        | 0.92  | 6    | 1.3827          | 0.3913   |
| 1.3824        | 2.0   | 13   | 1.3319          | 0.6087   |
| 1.3824        | 2.92  | 19   | 1.2476          | 0.5435   |
| 1.3034        | 4.0   | 26   | 1.1450          | 0.5217   |
| 1.1431        | 4.92  | 32   | 1.0679          | 0.5435   |
| 1.1431        | 6.0   | 39   | 1.0006          | 0.6087   |
| 1.0123        | 6.92  | 45   | 0.9617          | 0.6522   |
| 0.8798        | 8.0   | 52   | 0.8575          | 0.7609   |
| 0.8798        | 8.92  | 58   | 0.8074          | 0.6957   |
| 0.7538        | 10.0  | 65   | 0.7447          | 0.7826   |
| 0.6115        | 10.92 | 71   | 0.7204          | 0.7826   |
| 0.6115        | 12.0  | 78   | 0.6399          | 0.8478   |
| 0.5009        | 12.92 | 84   | 0.5726          | 0.8478   |
| 0.389         | 14.0  | 91   | 0.5825          | 0.8478   |
| 0.389         | 14.92 | 97   | 0.6231          | 0.7609   |
| 0.3348        | 16.0  | 104  | 0.5510          | 0.8478   |
| 0.2616        | 16.92 | 110  | 0.5070          | 0.8478   |
| 0.2616        | 18.0  | 117  | 0.5040          | 0.8261   |
| 0.2188        | 18.92 | 123  | 0.5738          | 0.7826   |
| 0.2078        | 20.0  | 130  | 0.5398          | 0.8043   |
| 0.2078        | 20.92 | 136  | 0.5334          | 0.7826   |
| 0.2165        | 22.0  | 143  | 0.6043          | 0.7826   |
| 0.2165        | 22.92 | 149  | 0.5817          | 0.8043   |
| 0.1645        | 24.0  | 156  | 0.6465          | 0.7391   |
| 0.1413        | 24.92 | 162  | 0.6638          | 0.8043   |
| 0.1413        | 26.0  | 169  | 0.5710          | 0.8261   |
| 0.141         | 26.92 | 175  | 0.6494          | 0.8043   |
| 0.1313        | 28.0  | 182  | 0.7649          | 0.6957   |
| 0.1313        | 28.92 | 188  | 0.6130          | 0.7609   |
| 0.14          | 30.0  | 195  | 0.6718          | 0.7609   |
| 0.1284        | 30.92 | 201  | 0.6660          | 0.8261   |
| 0.1284        | 32.0  | 208  | 0.6286          | 0.7826   |
| 0.1135        | 32.92 | 214  | 0.6424          | 0.8043   |
| 0.1024        | 34.0  | 221  | 0.6339          | 0.8043   |
| 0.1024        | 34.92 | 227  | 0.6132          | 0.8043   |
| 0.1108        | 36.0  | 234  | 0.5975          | 0.8478   |
| 0.0944        | 36.92 | 240  | 0.5981          | 0.8478   |


### Framework versions

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

avanzada leve moderada no dmae