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# vit-base-patch16-224-ve-U12-b-24

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: 0.6456
- 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: 24

### Training results

| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| No log        | 0.92  | 6    | 1.3806          | 0.4130   |
| 1.379         | 2.0   | 13   | 1.3103          | 0.5435   |
| 1.379         | 2.92  | 19   | 1.2269          | 0.4130   |
| 1.2758        | 4.0   | 26   | 1.1412          | 0.4565   |
| 1.121         | 4.92  | 32   | 1.0650          | 0.4783   |
| 1.121         | 6.0   | 39   | 1.0084          | 0.5217   |
| 0.9871        | 6.92  | 45   | 0.9395          | 0.6522   |
| 0.8612        | 8.0   | 52   | 0.8798          | 0.7174   |
| 0.8612        | 8.92  | 58   | 0.8219          | 0.7391   |
| 0.7653        | 10.0  | 65   | 0.7712          | 0.7826   |
| 0.6674        | 10.92 | 71   | 0.7328          | 0.7609   |
| 0.6674        | 12.0  | 78   | 0.6968          | 0.7391   |
| 0.568         | 12.92 | 84   | 0.6456          | 0.8478   |
| 0.4723        | 14.0  | 91   | 0.6528          | 0.8043   |
| 0.4723        | 14.92 | 97   | 0.7107          | 0.6739   |
| 0.4256        | 16.0  | 104  | 0.6335          | 0.7609   |
| 0.3524        | 16.92 | 110  | 0.5953          | 0.8261   |
| 0.3524        | 18.0  | 117  | 0.5824          | 0.8261   |
| 0.3282        | 18.92 | 123  | 0.6329          | 0.7174   |
| 0.3074        | 20.0  | 130  | 0.5775          | 0.8043   |
| 0.3074        | 20.92 | 136  | 0.5770          | 0.8043   |
| 0.3076        | 22.0  | 143  | 0.5749          | 0.8261   |
| 0.3076        | 22.15 | 144  | 0.5747          | 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