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

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: 0.6495
- 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: 6e-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 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 1.3419        | 1.0   | 20   | 1.2998          | 0.4706   |
| 1.1313        | 2.0   | 40   | 1.0832          | 0.5686   |
| 0.7969        | 3.0   | 60   | 0.8094          | 0.6667   |
| 0.5063        | 4.0   | 80   | 0.6573          | 0.7843   |
| 0.3367        | 5.0   | 100  | 0.6389          | 0.7647   |
| 0.242         | 6.0   | 120  | 0.6879          | 0.7451   |
| 0.1881        | 7.0   | 140  | 0.7940          | 0.7059   |
| 0.1561        | 8.0   | 160  | 0.8030          | 0.7647   |
| 0.1557        | 9.0   | 180  | 0.7004          | 0.8235   |
| 0.1154        | 10.0  | 200  | 0.6495          | 0.8431   |
| 0.1469        | 11.0  | 220  | 1.1388          | 0.7059   |
| 0.0898        | 12.0  | 240  | 0.7967          | 0.7647   |
| 0.0719        | 13.0  | 260  | 0.8934          | 0.8039   |
| 0.0739        | 14.0  | 280  | 0.8476          | 0.7647   |
| 0.0823        | 15.0  | 300  | 0.9692          | 0.7647   |
| 0.0828        | 16.0  | 320  | 0.9385          | 0.7843   |
| 0.0761        | 17.0  | 340  | 1.1684          | 0.7255   |
| 0.0597        | 18.0  | 360  | 0.9414          | 0.7647   |
| 0.0727        | 19.0  | 380  | 1.0201          | 0.7059   |
| 0.0507        | 20.0  | 400  | 0.8563          | 0.8039   |
| 0.0587        | 21.0  | 420  | 0.8476          | 0.7843   |
| 0.0608        | 22.0  | 440  | 0.9399          | 0.8039   |
| 0.055         | 23.0  | 460  | 0.8820          | 0.7451   |
| 0.0619        | 24.0  | 480  | 1.0460          | 0.7647   |
| 0.0615        | 25.0  | 500  | 0.9392          | 0.8235   |
| 0.0455        | 26.0  | 520  | 0.9267          | 0.8235   |
| 0.0567        | 27.0  | 540  | 0.9784          | 0.7843   |
| 0.032         | 28.0  | 560  | 1.1541          | 0.7647   |
| 0.0276        | 29.0  | 580  | 0.8865          | 0.7843   |
| 0.0368        | 30.0  | 600  | 1.0848          | 0.8039   |
| 0.0342        | 31.0  | 620  | 0.9638          | 0.8039   |
| 0.037         | 32.0  | 640  | 0.9616          | 0.8039   |
| 0.0371        | 33.0  | 660  | 1.0073          | 0.8039   |
| 0.0371        | 34.0  | 680  | 1.0494          | 0.8039   |
| 0.0359        | 35.0  | 700  | 1.1287          | 0.7843   |
| 0.0255        | 36.0  | 720  | 1.1831          | 0.7647   |
| 0.0269        | 37.0  | 740  | 1.1610          | 0.7843   |
| 0.0292        | 38.0  | 760  | 1.1842          | 0.7843   |
| 0.0161        | 39.0  | 780  | 1.1092          | 0.8039   |
| 0.0333        | 40.0  | 800  | 1.1186          | 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