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# vit-base-patch16-224-dmae-va-U

This model is a fine-tuned version of [google/vit-base-patch16-224](https://huggingface.co/google/vit-base-patch16-224) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0534
- Accuracy: 0.9908

## 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: 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: 0.1
- num_epochs: 40

### Training results

| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| No log        | 0.9   | 7    | 1.4319          | 0.2569   |
| 1.3911        | 1.94  | 15   | 1.2133          | 0.4771   |
| 1.3911        | 2.97  | 23   | 0.9487          | 0.6055   |
| 1.0766        | 4.0   | 31   | 0.6542          | 0.7156   |
| 0.6974        | 4.9   | 38   | 0.4644          | 0.8716   |
| 0.6974        | 5.94  | 46   | 0.3919          | 0.8716   |
| 0.421         | 6.97  | 54   | 0.3094          | 0.8716   |
| 0.2513        | 8.0   | 62   | 0.2334          | 0.8991   |
| 0.2513        | 8.9   | 69   | 0.1915          | 0.9174   |
| 0.1931        | 9.94  | 77   | 0.2431          | 0.8807   |
| 0.1757        | 10.97 | 85   | 0.1608          | 0.9450   |
| 0.1757        | 12.0  | 93   | 0.1424          | 0.9266   |
| 0.1442        | 12.9  | 100  | 0.1280          | 0.9450   |
| 0.1085        | 13.94 | 108  | 0.1055          | 0.9541   |
| 0.1085        | 14.97 | 116  | 0.1080          | 0.9541   |
| 0.1056        | 16.0  | 124  | 0.0997          | 0.9633   |
| 0.1056        | 16.9  | 131  | 0.1185          | 0.9633   |
| 0.0926        | 17.94 | 139  | 0.0773          | 0.9633   |
| 0.103         | 18.97 | 147  | 0.1279          | 0.9633   |
| 0.103         | 20.0  | 155  | 0.1043          | 0.9633   |
| 0.0938        | 20.9  | 162  | 0.0824          | 0.9817   |
| 0.0891        | 21.94 | 170  | 0.1449          | 0.9541   |
| 0.0891        | 22.97 | 178  | 0.1366          | 0.9633   |
| 0.0754        | 24.0  | 186  | 0.1148          | 0.9358   |
| 0.0882        | 24.9  | 193  | 0.1992          | 0.9358   |
| 0.0882        | 25.94 | 201  | 0.0743          | 0.9817   |
| 0.078         | 26.97 | 209  | 0.0668          | 0.9725   |
| 0.0666        | 28.0  | 217  | 0.0534          | 0.9908   |
| 0.0666        | 28.9  | 224  | 0.0499          | 0.9908   |
| 0.0514        | 29.94 | 232  | 0.0433          | 0.9725   |
| 0.062         | 30.97 | 240  | 0.0840          | 0.9633   |
| 0.062         | 32.0  | 248  | 0.0513          | 0.9725   |
| 0.0712        | 32.9  | 255  | 0.0482          | 0.9817   |
| 0.0712        | 33.94 | 263  | 0.0553          | 0.9817   |
| 0.0703        | 34.97 | 271  | 0.0602          | 0.9725   |
| 0.0553        | 36.0  | 279  | 0.0595          | 0.9725   |
| 0.0553        | 36.13 | 280  | 0.0595          | 0.9725   |


### Framework versions

- Transformers 4.35.2
- Pytorch 2.1.0+cu118
- Datasets 2.15.0
- Tokenizers 0.15.0

avanzada leve moderada no dmae