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# vit-base-patch16-224-ve-U10-24

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: 0.7874
- Accuracy: 0.7647

## 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 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 1.3749        | 0.95  | 15   | 1.2899          | 0.4706   |
| 1.1573        | 1.97  | 31   | 1.0844          | 0.5686   |
| 0.985         | 2.98  | 47   | 0.9140          | 0.6078   |
| 0.7024        | 4.0   | 63   | 0.8578          | 0.6863   |
| 0.5699        | 4.95  | 78   | 0.6802          | 0.7451   |
| 0.3784        | 5.97  | 94   | 0.8856          | 0.7059   |
| 0.2631        | 6.98  | 110  | 0.7526          | 0.7451   |
| 0.2201        | 8.0   | 126  | 0.7924          | 0.7255   |
| 0.1933        | 8.95  | 141  | 0.7874          | 0.7647   |
| 0.1592        | 9.97  | 157  | 0.9583          | 0.6863   |
| 0.154         | 10.98 | 173  | 0.9961          | 0.7059   |
| 0.1531        | 12.0  | 189  | 0.8916          | 0.7451   |
| 0.1153        | 12.95 | 204  | 0.9174          | 0.7451   |
| 0.1154        | 13.97 | 220  | 1.0267          | 0.7059   |
| 0.0922        | 14.98 | 236  | 0.9766          | 0.7255   |
| 0.0901        | 16.0  | 252  | 1.0410          | 0.7255   |
| 0.074         | 16.95 | 267  | 1.1869          | 0.6863   |
| 0.0743        | 17.97 | 283  | 1.1094          | 0.7255   |
| 0.084         | 18.98 | 299  | 1.0520          | 0.7255   |
| 0.0713        | 20.0  | 315  | 1.1213          | 0.7059   |
| 0.061         | 20.95 | 330  | 1.0927          | 0.7451   |
| 0.0669        | 21.97 | 346  | 1.0806          | 0.7255   |
| 0.0654        | 22.86 | 360  | 1.0647          | 0.7255   |


### Framework versions

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

avanzada leve moderada no dmae