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# vit-base-patch16-224-ve-U13b-80RX3

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: 0.4344
- Accuracy: 0.9130

## 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: 4.74e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 16
- 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 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 1.33          | 0.99  | 51   | 1.3133          | 0.3478   |
| 1.0288        | 2.0   | 103  | 1.0045          | 0.5652   |
| 0.7322        | 2.99  | 154  | 0.7309          | 0.8043   |
| 0.5476        | 4.0   | 206  | 0.6316          | 0.7826   |
| 0.2863        | 4.99  | 257  | 0.5598          | 0.8043   |
| 0.3149        | 6.0   | 309  | 0.5428          | 0.8478   |
| 0.1489        | 6.99  | 360  | 0.5150          | 0.8696   |
| 0.1134        | 8.0   | 412  | 0.4585          | 0.8043   |
| 0.1613        | 8.99  | 463  | 0.6284          | 0.8478   |
| 0.1855        | 10.0  | 515  | 0.5985          | 0.8478   |
| 0.1908        | 10.99 | 566  | 1.0336          | 0.7391   |
| 0.2293        | 12.0  | 618  | 0.7746          | 0.8043   |
| 0.1414        | 12.99 | 669  | 0.6517          | 0.8261   |
| 0.0877        | 14.0  | 721  | 0.5639          | 0.8261   |
| 0.1302        | 14.99 | 772  | 0.7687          | 0.8261   |
| 0.047         | 16.0  | 824  | 0.6773          | 0.8696   |
| 0.1045        | 16.99 | 875  | 0.4344          | 0.9130   |
| 0.0751        | 18.0  | 927  | 1.0160          | 0.7391   |
| 0.1141        | 18.99 | 978  | 0.6643          | 0.8696   |
| 0.1756        | 20.0  | 1030 | 0.5582          | 0.8913   |
| 0.1212        | 20.99 | 1081 | 0.5641          | 0.8913   |
| 0.0903        | 22.0  | 1133 | 0.6990          | 0.8261   |
| 0.0693        | 22.99 | 1184 | 0.5548          | 0.8913   |
| 0.0048        | 24.0  | 1236 | 0.6958          | 0.8478   |
| 0.0785        | 24.99 | 1287 | 0.7886          | 0.8043   |
| 0.0373        | 26.0  | 1339 | 0.6345          | 0.8478   |
| 0.0763        | 26.99 | 1390 | 0.6830          | 0.8696   |
| 0.0621        | 28.0  | 1442 | 0.7294          | 0.8478   |
| 0.0367        | 28.99 | 1493 | 0.6636          | 0.8696   |
| 0.0124        | 30.0  | 1545 | 0.8031          | 0.8478   |
| 0.0759        | 30.99 | 1596 | 0.7076          | 0.8696   |
| 0.0786        | 32.0  | 1648 | 0.8024          | 0.8261   |
| 0.0487        | 32.99 | 1699 | 0.7927          | 0.8696   |
| 0.0664        | 34.0  | 1751 | 0.9607          | 0.8261   |
| 0.0054        | 34.99 | 1802 | 0.9702          | 0.8261   |
| 0.0277        | 36.0  | 1854 | 0.8351          | 0.8261   |
| 0.0025        | 36.99 | 1905 | 0.9318          | 0.8261   |
| 0.0188        | 38.0  | 1957 | 0.8995          | 0.8478   |
| 0.0385        | 38.99 | 2008 | 0.8928          | 0.8478   |
| 0.0474        | 39.61 | 2040 | 0.8863          | 0.8478   |


### Framework versions

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

avanzada leve moderada no dmae