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# vit-base-patch16-224-dmae-va-U5-40-45-5e-05

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: 0.5841
- Accuracy: 0.8333

## 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: 4.5e-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        | 0.9   | 7    | 1.3748          | 0.45     |
| 1.3825        | 1.94  | 15   | 1.3172          | 0.5      |
| 1.3327        | 2.97  | 23   | 1.2210          | 0.55     |
| 1.2072        | 4.0   | 31   | 1.1094          | 0.5833   |
| 1.2072        | 4.9   | 38   | 1.0342          | 0.5667   |
| 1.0691        | 5.94  | 46   | 0.9669          | 0.6167   |
| 0.9546        | 6.97  | 54   | 0.9254          | 0.7      |
| 0.8633        | 8.0   | 62   | 0.9027          | 0.75     |
| 0.8633        | 8.9   | 69   | 0.8455          | 0.75     |
| 0.7627        | 9.94  | 77   | 0.7551          | 0.7667   |
| 0.665         | 10.97 | 85   | 0.6982          | 0.8      |
| 0.55          | 12.0  | 93   | 0.7471          | 0.7333   |
| 0.4657        | 12.9  | 100  | 0.6946          | 0.7833   |
| 0.4657        | 13.94 | 108  | 0.5841          | 0.8333   |
| 0.3706        | 14.97 | 116  | 0.6047          | 0.8      |
| 0.3222        | 16.0  | 124  | 0.6454          | 0.7667   |
| 0.2909        | 16.9  | 131  | 0.5383          | 0.8333   |
| 0.2909        | 17.94 | 139  | 0.5574          | 0.8      |
| 0.2887        | 18.97 | 147  | 0.6286          | 0.8167   |
| 0.237         | 20.0  | 155  | 0.6517          | 0.8      |
| 0.2071        | 20.9  | 162  | 0.5069          | 0.8333   |
| 0.2076        | 21.94 | 170  | 0.6049          | 0.7833   |
| 0.2076        | 22.97 | 178  | 0.6403          | 0.7833   |
| 0.1789        | 24.0  | 186  | 0.6471          | 0.8167   |
| 0.1582        | 24.9  | 193  | 0.6160          | 0.8167   |
| 0.1508        | 25.94 | 201  | 0.6432          | 0.8      |
| 0.1508        | 26.97 | 209  | 0.5986          | 0.8333   |
| 0.1566        | 28.0  | 217  | 0.6011          | 0.8      |
| 0.122         | 28.9  | 224  | 0.5663          | 0.8333   |
| 0.1251        | 29.94 | 232  | 0.5868          | 0.8333   |
| 0.117         | 30.97 | 240  | 0.6516          | 0.8333   |
| 0.117         | 32.0  | 248  | 0.5860          | 0.8333   |
| 0.1136        | 32.9  | 255  | 0.5576          | 0.8167   |
| 0.1099        | 33.94 | 263  | 0.5949          | 0.8167   |
| 0.1336        | 34.97 | 271  | 0.6318          | 0.8167   |
| 0.1336        | 36.0  | 279  | 0.6262          | 0.8167   |
| 0.1052        | 36.13 | 280  | 0.6258          | 0.8167   |


### Framework versions

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

avanzada leve moderada no dmae