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# vit-base-patch16-224-ve-U14-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: 0.6698
- 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        | 0.97  | 7    | 1.3673          | 0.4783   |
| 1.3789        | 1.93  | 14   | 1.2760          | 0.5435   |
| 1.2878        | 2.9   | 21   | 1.1732          | 0.5435   |
| 1.2878        | 4.0   | 29   | 1.0471          | 0.5435   |
| 1.128         | 4.97  | 36   | 0.9960          | 0.5435   |
| 0.9873        | 5.93  | 43   | 0.8939          | 0.6304   |
| 0.8611        | 6.9   | 50   | 0.8650          | 0.6087   |
| 0.8611        | 8.0   | 58   | 0.8442          | 0.6304   |
| 0.7397        | 8.97  | 65   | 0.7331          | 0.7174   |
| 0.6326        | 9.93  | 72   | 0.6698          | 0.8478   |
| 0.6326        | 10.9  | 79   | 0.7430          | 0.7391   |
| 0.5424        | 12.0  | 87   | 0.7030          | 0.7609   |
| 0.4687        | 12.97 | 94   | 0.6352          | 0.8043   |
| 0.404         | 13.93 | 101  | 0.5498          | 0.8043   |
| 0.404         | 14.9  | 108  | 0.5631          | 0.8043   |
| 0.3244        | 16.0  | 116  | 0.5706          | 0.8261   |
| 0.305         | 16.97 | 123  | 0.6010          | 0.8043   |
| 0.2819        | 17.93 | 130  | 0.5845          | 0.7826   |
| 0.2819        | 18.9  | 137  | 0.5594          | 0.8043   |
| 0.2487        | 20.0  | 145  | 0.5567          | 0.8043   |
| 0.2297        | 20.97 | 152  | 0.5489          | 0.8043   |
| 0.2297        | 21.93 | 159  | 0.5556          | 0.7826   |
| 0.2177        | 22.9  | 166  | 0.5519          | 0.8043   |
| 0.2177        | 23.17 | 168  | 0.5515          | 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