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

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: 1.6193
- Accuracy: 0.7

## 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: 0.0004
- 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    | 0.8832          | 0.6167   |
| 1.2709        | 1.94  | 15   | 0.8194          | 0.7      |
| 0.9705        | 2.97  | 23   | 0.7715          | 0.6      |
| 0.7033        | 4.0   | 31   | 1.1243          | 0.6333   |
| 0.7033        | 4.9   | 38   | 1.2102          | 0.55     |
| 0.7367        | 5.94  | 46   | 1.0123          | 0.65     |
| 0.5424        | 6.97  | 54   | 1.1218          | 0.6667   |
| 0.3998        | 8.0   | 62   | 1.3493          | 0.6      |
| 0.3998        | 8.9   | 69   | 1.3213          | 0.5833   |
| 0.3478        | 9.94  | 77   | 1.2704          | 0.6167   |
| 0.3467        | 10.97 | 85   | 1.4684          | 0.6167   |
| 0.2805        | 12.0  | 93   | 1.1832          | 0.7167   |
| 0.2496        | 12.9  | 100  | 1.3645          | 0.6333   |
| 0.2496        | 13.94 | 108  | 1.3561          | 0.6667   |
| 0.1855        | 14.97 | 116  | 1.6048          | 0.6167   |
| 0.2162        | 16.0  | 124  | 1.3662          | 0.6833   |
| 0.1833        | 16.9  | 131  | 1.6070          | 0.6667   |
| 0.1833        | 17.94 | 139  | 1.8448          | 0.6      |
| 0.2022        | 18.97 | 147  | 1.3397          | 0.65     |
| 0.1587        | 20.0  | 155  | 1.5272          | 0.65     |
| 0.1991        | 20.9  | 162  | 1.4667          | 0.65     |
| 0.1639        | 21.94 | 170  | 1.3853          | 0.65     |
| 0.1639        | 22.97 | 178  | 1.8195          | 0.6667   |
| 0.1364        | 24.0  | 186  | 1.7032          | 0.6167   |
| 0.1513        | 24.9  | 193  | 1.6734          | 0.65     |
| 0.1197        | 25.94 | 201  | 1.8673          | 0.6      |
| 0.1197        | 26.97 | 209  | 1.9885          | 0.6      |
| 0.1366        | 28.0  | 217  | 1.7329          | 0.6667   |
| 0.0982        | 28.9  | 224  | 1.6177          | 0.7333   |
| 0.0983        | 29.94 | 232  | 1.6226          | 0.7167   |
| 0.1131        | 30.97 | 240  | 1.5967          | 0.7      |
| 0.1131        | 32.0  | 248  | 1.6310          | 0.7      |
| 0.0694        | 32.9  | 255  | 1.6673          | 0.7333   |
| 0.0745        | 33.94 | 263  | 1.6257          | 0.7      |
| 0.058         | 34.97 | 271  | 1.6017          | 0.7167   |
| 0.058         | 36.0  | 279  | 1.6812          | 0.7      |
| 0.055         | 36.9  | 286  | 1.6524          | 0.7      |
| 0.0569        | 37.94 | 294  | 1.6193          | 0.7      |


### Framework versions

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

avanzada leve moderada no dmae