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# vit-base-patch16-224-MSC-dmae

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: 0.6300
- Accuracy: 0.95

## 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: 12

### Training results

| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| No log        | 0.67  | 1    | 1.2258          | 0.5      |
| No log        | 2.0   | 3    | 1.0536          | 0.7      |
| No log        | 2.67  | 4    | 0.9143          | 0.75     |
| No log        | 4.0   | 6    | 0.6899          | 0.9      |
| No log        | 4.67  | 7    | 0.6300          | 0.95     |
| No log        | 6.0   | 9    | 0.5069          | 0.9      |
| 0.8554        | 6.67  | 10   | 0.4671          | 0.9      |
| 0.8554        | 8.0   | 12   | 0.4312          | 0.9      |


### Framework versions

- Transformers 4.33.2
- Pytorch 2.0.1+cu118
- Datasets 2.14.5
- Tokenizers 0.13.3
avanzada leve moderada no amd