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# beit-base-patch16-224-dmae-va-U5-42B

This model is a fine-tuned version of [microsoft/beit-base-patch16-224](https://huggingface.co/microsoft/beit-base-patch16-224) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 1.1619
- Accuracy: 0.5167

## 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: 0.0015
- 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: 42

### Training results

| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| No log        | 0.9   | 7    | 1.2389          | 0.45     |
| 1.5474        | 1.94  | 15   | 1.2573          | 0.4667   |
| 1.4502        | 2.97  | 23   | 1.4267          | 0.2667   |
| 1.343         | 4.0   | 31   | 1.3320          | 0.1833   |
| 1.343         | 4.9   | 38   | 1.3385          | 0.4      |
| 1.2875        | 5.94  | 46   | 1.3633          | 0.3167   |
| 1.3121        | 6.97  | 54   | 1.4097          | 0.2      |
| 1.3669        | 8.0   | 62   | 1.3222          | 0.3167   |
| 1.3669        | 8.9   | 69   | 1.2321          | 0.4333   |
| 1.294         | 9.94  | 77   | 1.2933          | 0.4      |
| 1.2646        | 10.97 | 85   | 1.2691          | 0.4      |
| 1.2352        | 12.0  | 93   | 1.2749          | 0.4333   |
| 1.1953        | 12.9  | 100  | 1.1975          | 0.4833   |
| 1.1953        | 13.94 | 108  | 1.3215          | 0.45     |
| 1.2519        | 14.97 | 116  | 1.2561          | 0.3833   |
| 1.1971        | 16.0  | 124  | 1.3446          | 0.3667   |
| 1.204         | 16.9  | 131  | 1.2449          | 0.35     |
| 1.204         | 17.94 | 139  | 1.2131          | 0.4333   |
| 1.1334        | 18.97 | 147  | 1.2282          | 0.3667   |
| 1.1345        | 20.0  | 155  | 1.2902          | 0.3      |
| 1.1168        | 20.9  | 162  | 1.1619          | 0.5167   |
| 1.0866        | 21.94 | 170  | 1.1868          | 0.4667   |
| 1.0866        | 22.97 | 178  | 1.2290          | 0.4333   |
| 1.0751        | 24.0  | 186  | 1.1980          | 0.4167   |
| 1.0122        | 24.9  | 193  | 1.1744          | 0.45     |
| 0.9719        | 25.94 | 201  | 1.2401          | 0.4      |
| 0.9719        | 26.97 | 209  | 1.1908          | 0.4333   |
| 0.9418        | 28.0  | 217  | 1.2830          | 0.4167   |
| 0.8875        | 28.9  | 224  | 1.2310          | 0.4333   |
| 0.8961        | 29.94 | 232  | 1.2579          | 0.4667   |
| 0.7788        | 30.97 | 240  | 1.4248          | 0.4833   |
| 0.7788        | 32.0  | 248  | 1.3387          | 0.45     |
| 0.7828        | 32.9  | 255  | 1.3085          | 0.4667   |
| 0.696         | 33.94 | 263  | 1.3884          | 0.4833   |
| 0.6859        | 34.97 | 271  | 1.3446          | 0.5      |
| 0.6859        | 36.0  | 279  | 1.4850          | 0.45     |
| 0.6036        | 36.9  | 286  | 1.4519          | 0.4667   |
| 0.5565        | 37.94 | 294  | 1.4544          | 0.4667   |


### Framework versions

- Transformers 4.38.2
- Pytorch 2.2.1+cu121
- Datasets 2.19.0
- Tokenizers 0.15.2

avanzada leve moderada no dmae