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# vit-base-patch16-224-ve-U13b-80RX1

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: 0.7770
- Accuracy: 0.8478

## 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: 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 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 1.3157        | 0.99  | 51   | 1.2968          | 0.3478   |
| 1.0334        | 2.0   | 103  | 1.0060          | 0.5217   |
| 0.691         | 2.99  | 154  | 0.7506          | 0.7609   |
| 0.5005        | 4.0   | 206  | 0.6433          | 0.7826   |
| 0.3478        | 4.99  | 257  | 0.5674          | 0.7609   |
| 0.3339        | 6.0   | 309  | 0.6623          | 0.7609   |
| 0.2533        | 6.99  | 360  | 0.6905          | 0.7391   |
| 0.138         | 8.0   | 412  | 0.7251          | 0.7826   |
| 0.1289        | 8.99  | 463  | 0.7467          | 0.7391   |
| 0.152         | 10.0  | 515  | 0.9011          | 0.7174   |
| 0.2609        | 10.99 | 566  | 1.0150          | 0.7174   |
| 0.2202        | 12.0  | 618  | 0.9713          | 0.7826   |
| 0.1083        | 12.99 | 669  | 1.1106          | 0.6739   |
| 0.07          | 14.0  | 721  | 1.1211          | 0.7174   |
| 0.0791        | 14.99 | 772  | 1.1830          | 0.7609   |
| 0.0427        | 16.0  | 824  | 0.7770          | 0.8478   |
| 0.1219        | 16.99 | 875  | 1.0962          | 0.7391   |
| 0.0739        | 18.0  | 927  | 0.9447          | 0.7609   |
| 0.1989        | 18.99 | 978  | 1.1543          | 0.7391   |
| 0.1097        | 20.0  | 1030 | 1.1795          | 0.7609   |
| 0.1204        | 20.99 | 1081 | 1.2679          | 0.6739   |
| 0.0514        | 22.0  | 1133 | 1.0646          | 0.7174   |
| 0.0612        | 22.99 | 1184 | 1.1413          | 0.6957   |
| 0.0207        | 24.0  | 1236 | 0.8928          | 0.7826   |
| 0.1063        | 24.99 | 1287 | 1.1186          | 0.7609   |
| 0.1076        | 26.0  | 1339 | 1.1741          | 0.7609   |
| 0.0714        | 26.99 | 1390 | 1.0977          | 0.8043   |
| 0.062         | 28.0  | 1442 | 1.3965          | 0.7174   |
| 0.0617        | 28.99 | 1493 | 1.1849          | 0.7609   |
| 0.0536        | 30.0  | 1545 | 1.0865          | 0.7826   |
| 0.0707        | 30.99 | 1596 | 1.2081          | 0.7609   |
| 0.0967        | 32.0  | 1648 | 1.3300          | 0.7391   |
| 0.0564        | 32.99 | 1699 | 1.2240          | 0.7826   |
| 0.0435        | 34.0  | 1751 | 1.2391          | 0.7609   |
| 0.043         | 34.99 | 1802 | 1.1813          | 0.7609   |
| 0.0218        | 36.0  | 1854 | 1.2496          | 0.7826   |
| 0.0043        | 36.99 | 1905 | 1.2797          | 0.7174   |
| 0.0051        | 38.0  | 1957 | 1.2493          | 0.7391   |
| 0.0123        | 38.99 | 2008 | 1.2538          | 0.7391   |
| 0.0546        | 39.61 | 2040 | 1.2530          | 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