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# vit-base-patch16-224-ve-U13b-R

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: 0.3534
- Accuracy: 0.9348

## Model description

More information needed

## Intended uses & limitations

More information needed

## Training and evaluation data

More information needed

## Training procedure

### Training hyperparameters



### Training results

| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 1.3157        | 0.99  | 51   | 1.2967          | 0.3478   |
| 0.9801        | 2.0   | 103  | 0.9966          | 0.5870   |
| 0.7385        | 2.99  | 154  | 0.7600          | 0.7174   |
| 0.572         | 4.0   | 206  | 0.6425          | 0.7826   |
| 0.3646        | 4.99  | 257  | 0.7687          | 0.6957   |
| 0.3033        | 6.0   | 309  | 0.6336          | 0.7391   |
| 0.3073        | 6.99  | 360  | 0.3534          | 0.9348   |
| 0.1623        | 8.0   | 412  | 0.8559          | 0.6739   |
| 0.1079        | 8.99  | 463  | 0.9730          | 0.7391   |
| 0.2703        | 10.0  | 515  | 0.7768          | 0.8043   |
| 0.178         | 10.99 | 566  | 0.8520          | 0.7826   |
| 0.2191        | 12.0  | 618  | 1.0049          | 0.7391   |
| 0.0597        | 12.99 | 669  | 0.8334          | 0.7609   |
| 0.0881        | 14.0  | 721  | 0.9985          | 0.7609   |
| 0.1265        | 14.99 | 772  | 0.9443          | 0.8043   |
| 0.0696        | 16.0  | 824  | 0.9878          | 0.8261   |
| 0.1198        | 16.99 | 875  | 0.8784          | 0.8043   |
| 0.1484        | 18.0  | 927  | 0.9595          | 0.7609   |
| 0.2887        | 18.99 | 978  | 1.0563          | 0.8043   |
| 0.1423        | 20.0  | 1030 | 0.8550          | 0.8043   |
| 0.083         | 20.99 | 1081 | 0.9093          | 0.7826   |
| 0.0695        | 22.0  | 1133 | 1.2758          | 0.6739   |
| 0.0285        | 22.99 | 1184 | 1.0852          | 0.7609   |
| 0.0132        | 24.0  | 1236 | 1.3341          | 0.6957   |
| 0.0957        | 24.99 | 1287 | 1.1965          | 0.7391   |
| 0.0633        | 26.0  | 1339 | 1.1199          | 0.7609   |
| 0.0705        | 26.99 | 1390 | 1.0551          | 0.8043   |
| 0.0564        | 28.0  | 1442 | 1.4332          | 0.7391   |
| 0.0798        | 28.99 | 1493 | 1.3855          | 0.7391   |
| 0.0326        | 30.0  | 1545 | 1.0534          | 0.8043   |
| 0.092         | 30.99 | 1596 | 1.1745          | 0.7609   |
| 0.1243        | 32.0  | 1648 | 1.1341          | 0.8043   |
| 0.062         | 32.99 | 1699 | 1.2648          | 0.7826   |
| 0.0941        | 34.0  | 1751 | 1.1236          | 0.7826   |
| 0.0119        | 34.99 | 1802 | 1.1303          | 0.8043   |
| 0.044         | 36.0  | 1854 | 1.1848          | 0.7826   |
| 0.0073        | 36.99 | 1905 | 1.1796          | 0.7609   |
| 0.0149        | 38.0  | 1957 | 1.2491          | 0.7826   |
| 0.0194        | 38.99 | 2008 | 1.1812          | 0.7826   |
| 0.0577        | 39.61 | 2040 | 1.1777          | 0.7609   |


### Framework versions

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

avanzada leve moderada no dmae