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

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: 0.5609
- Accuracy: 0.8235

## 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: 0.05
- num_epochs: 40

### Training results

| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 1.3495        | 1.0   | 20   | 1.3142          | 0.4706   |
| 1.1689        | 2.0   | 40   | 1.1153          | 0.5686   |
| 0.8673        | 3.0   | 60   | 0.8498          | 0.6667   |
| 0.5847        | 4.0   | 80   | 0.7220          | 0.7843   |
| 0.4029        | 5.0   | 100  | 0.8654          | 0.6275   |
| 0.2562        | 6.0   | 120  | 0.5609          | 0.8235   |
| 0.2352        | 7.0   | 140  | 0.7272          | 0.7843   |
| 0.2131        | 8.0   | 160  | 0.7581          | 0.7255   |
| 0.1616        | 9.0   | 180  | 0.5437          | 0.8235   |
| 0.1266        | 10.0  | 200  | 0.6345          | 0.8039   |
| 0.1557        | 11.0  | 220  | 0.8280          | 0.7647   |
| 0.0871        | 12.0  | 240  | 0.9016          | 0.7059   |
| 0.0879        | 13.0  | 260  | 0.8099          | 0.7647   |
| 0.0844        | 14.0  | 280  | 0.8791          | 0.7255   |
| 0.0865        | 15.0  | 300  | 0.9713          | 0.7843   |
| 0.1005        | 16.0  | 320  | 0.9966          | 0.7843   |
| 0.0718        | 17.0  | 340  | 1.0468          | 0.7647   |
| 0.0591        | 18.0  | 360  | 0.9471          | 0.7843   |
| 0.0641        | 19.0  | 380  | 0.9905          | 0.7451   |
| 0.0542        | 20.0  | 400  | 1.0300          | 0.7451   |
| 0.0813        | 21.0  | 420  | 1.0330          | 0.7647   |
| 0.059         | 22.0  | 440  | 0.9995          | 0.7647   |
| 0.0679        | 23.0  | 460  | 0.9327          | 0.7451   |
| 0.0611        | 24.0  | 480  | 1.0073          | 0.7647   |
| 0.0694        | 25.0  | 500  | 0.9348          | 0.7647   |
| 0.0454        | 26.0  | 520  | 0.8551          | 0.7843   |
| 0.0536        | 27.0  | 540  | 0.9782          | 0.7647   |
| 0.0429        | 28.0  | 560  | 0.9203          | 0.7843   |
| 0.0386        | 29.0  | 580  | 0.8732          | 0.8039   |
| 0.0433        | 30.0  | 600  | 0.9376          | 0.7647   |
| 0.0353        | 31.0  | 620  | 0.8532          | 0.7843   |
| 0.0332        | 32.0  | 640  | 0.9123          | 0.8039   |
| 0.0405        | 33.0  | 660  | 0.9603          | 0.8039   |
| 0.0423        | 34.0  | 680  | 0.9424          | 0.8039   |
| 0.0383        | 35.0  | 700  | 0.9687          | 0.8235   |
| 0.0245        | 36.0  | 720  | 0.9509          | 0.8235   |
| 0.0309        | 37.0  | 740  | 0.8950          | 0.8235   |
| 0.026         | 38.0  | 760  | 0.9082          | 0.8039   |
| 0.0192        | 39.0  | 780  | 0.8859          | 0.8235   |
| 0.0322        | 40.0  | 800  | 0.8968          | 0.8235   |


### Framework versions

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

avanzada leve moderada no dmae