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# vit-base-patch16-224-U8-40

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: 0.5495
- Accuracy: 0.8667

## 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: 40

### Training results

| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 1.3457        | 1.0   | 20   | 1.3128          | 0.45     |
| 1.1498        | 2.0   | 40   | 1.1047          | 0.5667   |
| 0.8312        | 3.0   | 60   | 0.8231          | 0.65     |
| 0.5334        | 4.0   | 80   | 0.5719          | 0.8167   |
| 0.3582        | 5.0   | 100  | 0.5495          | 0.8667   |
| 0.2389        | 6.0   | 120  | 0.5801          | 0.8333   |
| 0.2055        | 7.0   | 140  | 0.6727          | 0.8167   |
| 0.1738        | 8.0   | 160  | 0.7238          | 0.8      |
| 0.1556        | 9.0   | 180  | 0.7665          | 0.75     |
| 0.1461        | 10.0  | 200  | 0.8229          | 0.7667   |
| 0.1401        | 11.0  | 220  | 0.8102          | 0.75     |
| 0.08          | 12.0  | 240  | 0.6609          | 0.8333   |
| 0.0989        | 13.0  | 260  | 0.6703          | 0.8333   |
| 0.0773        | 14.0  | 280  | 0.7303          | 0.8167   |
| 0.089         | 15.0  | 300  | 0.7757          | 0.7833   |
| 0.11          | 16.0  | 320  | 0.7279          | 0.8      |
| 0.086         | 17.0  | 340  | 0.8491          | 0.7833   |
| 0.0671        | 18.0  | 360  | 0.7950          | 0.8      |
| 0.0775        | 19.0  | 380  | 0.6753          | 0.85     |
| 0.0636        | 20.0  | 400  | 0.7881          | 0.8333   |
| 0.0737        | 21.0  | 420  | 0.7450          | 0.8333   |
| 0.0583        | 22.0  | 440  | 0.8295          | 0.8      |
| 0.0646        | 23.0  | 460  | 0.8227          | 0.8333   |
| 0.0637        | 24.0  | 480  | 0.9030          | 0.7833   |
| 0.0647        | 25.0  | 500  | 0.8656          | 0.8      |
| 0.0477        | 26.0  | 520  | 0.8362          | 0.8      |
| 0.0481        | 27.0  | 540  | 0.8389          | 0.8      |
| 0.0355        | 28.0  | 560  | 0.9424          | 0.8      |
| 0.0352        | 29.0  | 580  | 0.8963          | 0.8      |
| 0.0335        | 30.0  | 600  | 0.8560          | 0.8333   |
| 0.0372        | 31.0  | 620  | 0.7250          | 0.8333   |
| 0.0389        | 32.0  | 640  | 0.7846          | 0.8167   |
| 0.0425        | 33.0  | 660  | 0.8532          | 0.8333   |
| 0.0404        | 34.0  | 680  | 0.8169          | 0.8333   |
| 0.0359        | 35.0  | 700  | 0.8682          | 0.8167   |
| 0.0231        | 36.0  | 720  | 0.9362          | 0.8167   |
| 0.027         | 37.0  | 740  | 0.9139          | 0.8167   |
| 0.0214        | 38.0  | 760  | 0.8782          | 0.8167   |
| 0.0191        | 39.0  | 780  | 0.8794          | 0.8167   |
| 0.0293        | 40.0  | 800  | 0.8929          | 0.8167   |


### Framework versions

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

avanzada leve moderada no dmae