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# vit-base-patch16-224-RU2-40

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: 1.2003
- Accuracy: 0.8333

## 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 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 1.3226        | 0.99  | 38   | 1.2293          | 0.6      |
| 0.9048        | 2.0   | 77   | 0.7969          | 0.7      |
| 0.4039        | 2.99  | 115  | 0.6800          | 0.7167   |
| 0.281         | 4.0   | 154  | 0.8892          | 0.7667   |
| 0.1755        | 4.99  | 192  | 0.9072          | 0.7333   |
| 0.1035        | 6.0   | 231  | 0.8036          | 0.8167   |
| 0.1275        | 6.99  | 269  | 0.8627          | 0.8      |
| 0.107         | 8.0   | 308  | 0.8713          | 0.8      |
| 0.0984        | 8.99  | 346  | 0.9660          | 0.8      |
| 0.0823        | 10.0  | 385  | 1.0704          | 0.7833   |
| 0.0771        | 10.99 | 423  | 0.9409          | 0.7667   |
| 0.0527        | 12.0  | 462  | 1.0052          | 0.7833   |
| 0.0708        | 12.99 | 500  | 0.9578          | 0.8      |
| 0.0562        | 14.0  | 539  | 1.0712          | 0.8167   |
| 0.0467        | 14.99 | 577  | 1.0586          | 0.8167   |
| 0.0445        | 16.0  | 616  | 1.2066          | 0.7667   |
| 0.0474        | 16.99 | 654  | 1.1863          | 0.75     |
| 0.0263        | 18.0  | 693  | 1.1207          | 0.8167   |
| 0.0307        | 18.99 | 731  | 1.1813          | 0.8167   |
| 0.0393        | 20.0  | 770  | 1.3761          | 0.75     |
| 0.0475        | 20.99 | 808  | 1.3008          | 0.7667   |
| 0.0215        | 22.0  | 847  | 1.2625          | 0.7333   |
| 0.0311        | 22.99 | 885  | 1.1508          | 0.8      |
| 0.027         | 24.0  | 924  | 1.3035          | 0.7667   |
| 0.0251        | 24.99 | 962  | 1.2270          | 0.7667   |
| 0.0161        | 26.0  | 1001 | 1.1470          | 0.8167   |
| 0.0258        | 26.99 | 1039 | 1.1473          | 0.8167   |
| 0.0142        | 28.0  | 1078 | 1.2326          | 0.7667   |
| 0.0151        | 28.99 | 1116 | 1.3978          | 0.7667   |
| 0.021         | 30.0  | 1155 | 1.2003          | 0.8333   |
| 0.0158        | 30.99 | 1193 | 1.2488          | 0.7667   |
| 0.0163        | 32.0  | 1232 | 1.3232          | 0.75     |
| 0.0143        | 32.99 | 1270 | 1.2467          | 0.8      |
| 0.02          | 34.0  | 1309 | 1.3176          | 0.7833   |
| 0.0128        | 34.99 | 1347 | 1.3083          | 0.7667   |
| 0.0144        | 36.0  | 1386 | 1.3080          | 0.7667   |
| 0.0109        | 36.99 | 1424 | 1.2999          | 0.8      |
| 0.0082        | 38.0  | 1463 | 1.2718          | 0.8      |
| 0.0064        | 38.99 | 1501 | 1.2588          | 0.7667   |
| 0.0097        | 39.48 | 1520 | 1.2597          | 0.7667   |


### Framework versions

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

avanzada leve moderada no dmae