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# vit-base-patch16-224-RU4-10

This model is a fine-tuned version of [google/vit-base-patch16-224](https://huggingface.co/google/vit-base-patch16-224) on the imagefolder dataset.
It achieves the following results on the evaluation set:
- Loss: 0.5903
- 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: 5.5e-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: 10

### Training results

| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 1.37          | 0.99  | 19   | 1.1940          | 0.6167   |
| 1.1393        | 1.97  | 38   | 0.9231          | 0.7      |
| 0.8115        | 2.96  | 57   | 0.7924          | 0.7667   |
| 0.5507        | 4.0   | 77   | 0.6691          | 0.75     |
| 0.4093        | 4.99  | 96   | 0.6462          | 0.8167   |
| 0.2869        | 5.97  | 115  | 0.5903          | 0.8333   |
| 0.2347        | 6.96  | 134  | 0.7096          | 0.7333   |
| 0.2148        | 8.0   | 154  | 0.6362          | 0.7833   |
| 0.1868        | 8.99  | 173  | 0.6496          | 0.8      |
| 0.1977        | 9.87  | 190  | 0.6368          | 0.7667   |


### Framework versions

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

avanzada leve moderada no dmae