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# vit-base-patch16-224-ve-Ub

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: 0.8470
- Accuracy: 0.7255

## 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: 80

### Training results

| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| No log        | 0.57  | 1    | 1.3863          | 0.0980   |
| No log        | 1.71  | 3    | 1.3813          | 0.4706   |
| No log        | 2.86  | 5    | 1.3686          | 0.4706   |
| No log        | 4.0   | 7    | 1.3480          | 0.4706   |
| No log        | 4.57  | 8    | 1.3345          | 0.4706   |
| 1.3658        | 5.71  | 10   | 1.3040          | 0.4706   |
| 1.3658        | 6.86  | 12   | 1.2754          | 0.4706   |
| 1.3658        | 8.0   | 14   | 1.2477          | 0.4902   |
| 1.3658        | 8.57  | 15   | 1.2347          | 0.5294   |
| 1.3658        | 9.71  | 17   | 1.2109          | 0.5490   |
| 1.3658        | 10.86 | 19   | 1.1889          | 0.6078   |
| 1.2512        | 12.0  | 21   | 1.1671          | 0.6275   |
| 1.2512        | 12.57 | 22   | 1.1560          | 0.6078   |
| 1.2512        | 13.71 | 24   | 1.1311          | 0.6471   |
| 1.2512        | 14.86 | 26   | 1.1128          | 0.6275   |
| 1.2512        | 16.0  | 28   | 1.0874          | 0.6667   |
| 1.2512        | 16.57 | 29   | 1.0828          | 0.6863   |
| 1.1299        | 17.71 | 31   | 1.0586          | 0.6667   |
| 1.1299        | 18.86 | 33   | 1.0362          | 0.6667   |
| 1.1299        | 20.0  | 35   | 1.0173          | 0.6863   |
| 1.1299        | 20.57 | 36   | 1.0065          | 0.6667   |
| 1.1299        | 21.71 | 38   | 1.0070          | 0.6471   |
| 1.0212        | 22.86 | 40   | 0.9792          | 0.6667   |
| 1.0212        | 24.0  | 42   | 0.9612          | 0.6667   |
| 1.0212        | 24.57 | 43   | 0.9584          | 0.6471   |
| 1.0212        | 25.71 | 45   | 0.9494          | 0.6667   |
| 1.0212        | 26.86 | 47   | 0.9294          | 0.6667   |
| 1.0212        | 28.0  | 49   | 0.9196          | 0.6667   |
| 0.9222        | 28.57 | 50   | 0.9100          | 0.7059   |
| 0.9222        | 29.71 | 52   | 0.9061          | 0.6863   |
| 0.9222        | 30.86 | 54   | 0.8904          | 0.7059   |
| 0.9222        | 32.0  | 56   | 0.8797          | 0.7059   |
| 0.9222        | 32.57 | 57   | 0.8747          | 0.6863   |
| 0.9222        | 33.71 | 59   | 0.8691          | 0.6863   |
| 0.8419        | 34.86 | 61   | 0.8550          | 0.7059   |
| 0.8419        | 36.0  | 63   | 0.8470          | 0.7255   |
| 0.8419        | 36.57 | 64   | 0.8430          | 0.7255   |
| 0.8419        | 37.71 | 66   | 0.8389          | 0.7059   |
| 0.8419        | 38.86 | 68   | 0.8298          | 0.7255   |
| 0.7865        | 40.0  | 70   | 0.8270          | 0.7255   |
| 0.7865        | 40.57 | 71   | 0.8258          | 0.7255   |
| 0.7865        | 41.71 | 73   | 0.8235          | 0.7059   |
| 0.7865        | 42.86 | 75   | 0.8211          | 0.7059   |
| 0.7865        | 44.0  | 77   | 0.8189          | 0.7059   |
| 0.7865        | 44.57 | 78   | 0.8189          | 0.7059   |
| 0.7555        | 45.71 | 80   | 0.8187          | 0.7059   |


### Framework versions

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

avanzada leve moderada no dmae