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# vit-base-patch16-224-ve-b-U10-40

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: 0.5211
- 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: 40

### Training results

| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| No log        | 0.96  | 6    | 1.3845          | 0.2549   |
| 1.3817        | 1.92  | 12   | 1.3529          | 0.4706   |
| 1.3817        | 2.88  | 18   | 1.2772          | 0.5882   |
| 1.2986        | 4.0   | 25   | 1.2121          | 0.3922   |
| 1.1298        | 4.96  | 31   | 1.1164          | 0.5882   |
| 1.1298        | 5.92  | 37   | 1.0879          | 0.5882   |
| 0.9842        | 6.88  | 43   | 0.9898          | 0.6863   |
| 0.8402        | 8.0   | 50   | 0.9233          | 0.7843   |
| 0.8402        | 8.96  | 56   | 0.9650          | 0.6471   |
| 0.7084        | 9.92  | 62   | 0.8243          | 0.7451   |
| 0.7084        | 10.88 | 68   | 0.7988          | 0.7647   |
| 0.5914        | 12.0  | 75   | 0.8114          | 0.7451   |
| 0.461         | 12.96 | 81   | 0.7652          | 0.7451   |
| 0.461         | 13.92 | 87   | 0.7406          | 0.7451   |
| 0.3769        | 14.88 | 93   | 0.6916          | 0.7451   |
| 0.3376        | 16.0  | 100  | 0.6182          | 0.7843   |
| 0.3376        | 16.96 | 106  | 0.8395          | 0.6863   |
| 0.2606        | 17.92 | 112  | 0.6941          | 0.7255   |
| 0.2606        | 18.88 | 118  | 0.7345          | 0.7255   |
| 0.2314        | 20.0  | 125  | 0.7374          | 0.7059   |
| 0.1907        | 20.96 | 131  | 0.7490          | 0.7647   |
| 0.1907        | 21.92 | 137  | 0.7292          | 0.7255   |
| 0.1804        | 22.88 | 143  | 0.7301          | 0.7451   |
| 0.1447        | 24.0  | 150  | 0.7224          | 0.7647   |
| 0.1447        | 24.96 | 156  | 0.7415          | 0.7255   |
| 0.1537        | 25.92 | 162  | 0.6668          | 0.7843   |
| 0.1537        | 26.88 | 168  | 0.7188          | 0.7451   |
| 0.1471        | 28.0  | 175  | 0.7291          | 0.7451   |
| 0.1241        | 28.96 | 181  | 0.5919          | 0.8039   |
| 0.1241        | 29.92 | 187  | 0.5211          | 0.8431   |
| 0.1058        | 30.88 | 193  | 0.6107          | 0.7843   |
| 0.1032        | 32.0  | 200  | 0.6863          | 0.7647   |
| 0.1032        | 32.96 | 206  | 0.6295          | 0.7647   |
| 0.1116        | 33.92 | 212  | 0.6061          | 0.7843   |
| 0.1116        | 34.88 | 218  | 0.6610          | 0.7843   |
| 0.0871        | 36.0  | 225  | 0.6109          | 0.8039   |
| 0.1037        | 36.96 | 231  | 0.6116          | 0.7843   |
| 0.1037        | 37.92 | 237  | 0.6176          | 0.8039   |
| 0.0802        | 38.4  | 240  | 0.6169          | 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