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# vit-base-patch16-224-RX1-24

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: 0.5687
- Accuracy: 0.8431

## 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: 24

### Training results

| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| No log        | 0.93  | 7    | 1.3485          | 0.4706   |
| 1.3674        | 2.0   | 15   | 1.2284          | 0.5490   |
| 1.2414        | 2.93  | 22   | 1.1307          | 0.6471   |
| 1.1146        | 4.0   | 30   | 1.0230          | 0.6471   |
| 1.1146        | 4.93  | 37   | 0.9251          | 0.6863   |
| 0.9522        | 6.0   | 45   | 0.9122          | 0.6471   |
| 0.8247        | 6.93  | 52   | 0.9374          | 0.6275   |
| 0.6825        | 8.0   | 60   | 0.8320          | 0.6863   |
| 0.6825        | 8.93  | 67   | 0.8286          | 0.6667   |
| 0.6191        | 10.0  | 75   | 0.8418          | 0.6667   |
| 0.5312        | 10.93 | 82   | 0.7836          | 0.8235   |
| 0.454         | 12.0  | 90   | 0.7356          | 0.8039   |
| 0.454         | 12.93 | 97   | 0.6117          | 0.8235   |
| 0.3752        | 14.0  | 105  | 0.6014          | 0.8235   |
| 0.3269        | 14.93 | 112  | 0.6102          | 0.8039   |
| 0.2733        | 16.0  | 120  | 0.6404          | 0.8039   |
| 0.2733        | 16.93 | 127  | 0.5687          | 0.8431   |
| 0.2711        | 18.0  | 135  | 0.6120          | 0.8235   |
| 0.2519        | 18.93 | 142  | 0.6250          | 0.8431   |
| 0.2484        | 20.0  | 150  | 0.6086          | 0.7843   |
| 0.2484        | 20.93 | 157  | 0.6229          | 0.8235   |
| 0.2258        | 22.0  | 165  | 0.6390          | 0.7843   |
| 0.2258        | 22.4  | 168  | 0.6337          | 0.8039   |


### Framework versions

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

avanzada leve moderada no dmae