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

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: 1.0383
- Accuracy: 0.55

## 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: 0.003
- 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    | 1.0970          | 0.5167   |
| 1.3527        | 1.94  | 15   | 1.0383          | 0.55     |
| 1.3527        | 2.97  | 23   | 1.2351          | 0.4167   |
| 1.3013        | 4.0   | 31   | 1.3025          | 0.3333   |
| 1.3706        | 4.9   | 38   | 1.3800          | 0.2167   |
| 1.3706        | 5.94  | 46   | 1.4609          | 0.1833   |
| 1.4415        | 6.97  | 54   | 1.3718          | 0.4333   |
| 1.3602        | 8.0   | 62   | 1.3173          | 0.3167   |
| 1.3602        | 8.9   | 69   | 1.2827          | 0.4      |
| 1.3079        | 9.94  | 77   | 1.3167          | 0.3167   |
| 1.3247        | 10.97 | 85   | 1.2579          | 0.4      |
| 1.3247        | 12.0  | 93   | 1.3202          | 0.2      |
| 1.3102        | 12.9  | 100  | 1.2354          | 0.45     |
| 1.2807        | 13.94 | 108  | 1.3610          | 0.25     |
| 1.2807        | 14.97 | 116  | 1.2803          | 0.4      |
| 1.2774        | 16.0  | 124  | 1.3338          | 0.2167   |
| 1.2774        | 16.9  | 131  | 1.2549          | 0.35     |
| 1.2596        | 17.94 | 139  | 1.2693          | 0.3667   |
| 1.2413        | 18.97 | 147  | 1.3005          | 0.2167   |
| 1.2413        | 20.0  | 155  | 1.2299          | 0.4333   |
| 1.262         | 20.9  | 162  | 1.3454          | 0.2667   |
| 1.2261        | 21.94 | 170  | 1.2818          | 0.3167   |
| 1.2261        | 22.97 | 178  | 1.2498          | 0.4333   |
| 1.2405        | 24.0  | 186  | 1.3376          | 0.3167   |
| 1.2245        | 24.9  | 193  | 1.2595          | 0.3667   |
| 1.2245        | 25.94 | 201  | 1.3319          | 0.4      |
| 1.2034        | 26.97 | 209  | 1.2528          | 0.3833   |
| 1.1818        | 28.0  | 217  | 1.3656          | 0.3667   |
| 1.1818        | 28.9  | 224  | 1.2501          | 0.3833   |
| 1.1479        | 29.94 | 232  | 1.3241          | 0.3      |
| 1.1193        | 30.97 | 240  | 1.3803          | 0.3667   |
| 1.1193        | 32.0  | 248  | 1.2294          | 0.4167   |
| 1.1071        | 32.9  | 255  | 1.4134          | 0.5      |
| 1.1071        | 33.94 | 263  | 1.4123          | 0.3667   |
| 1.0429        | 34.97 | 271  | 1.2184          | 0.5      |
| 1.0528        | 36.0  | 279  | 1.3100          | 0.45     |
| 1.0528        | 36.9  | 286  | 1.3249          | 0.3833   |
| 1.0055        | 37.94 | 294  | 1.3051          | 0.5      |


### Framework versions

- Transformers 4.38.2
- Pytorch 2.2.1+cu121
- Datasets 2.18.0
- Tokenizers 0.15.2

avanzada leve moderada no dmae