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# vit-base-patch16-224-ve-U12-b-80

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: 0.8139
- 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: 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: 80

### Training results

| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| No log        | 0.92  | 6    | 1.3850          | 0.3478   |
| 1.3848        | 2.0   | 13   | 1.3701          | 0.4783   |
| 1.3848        | 2.92  | 19   | 1.3196          | 0.5      |
| 1.3508        | 4.0   | 26   | 1.2287          | 0.4130   |
| 1.2282        | 4.92  | 32   | 1.1280          | 0.3913   |
| 1.2282        | 6.0   | 39   | 1.0625          | 0.3913   |
| 1.0677        | 6.92  | 45   | 0.9840          | 0.5      |
| 0.9278        | 8.0   | 52   | 0.8970          | 0.6957   |
| 0.9278        | 8.92  | 58   | 0.8530          | 0.7391   |
| 0.8003        | 10.0  | 65   | 0.7872          | 0.8043   |
| 0.6486        | 10.92 | 71   | 0.6974          | 0.8043   |
| 0.6486        | 12.0  | 78   | 0.6409          | 0.8043   |
| 0.514         | 12.92 | 84   | 0.6050          | 0.8261   |
| 0.3945        | 14.0  | 91   | 0.6589          | 0.7609   |
| 0.3945        | 14.92 | 97   | 0.6343          | 0.7609   |
| 0.337         | 16.0  | 104  | 0.7340          | 0.7174   |
| 0.2779        | 16.92 | 110  | 0.5629          | 0.8261   |
| 0.2779        | 18.0  | 117  | 0.5934          | 0.8261   |
| 0.2374        | 18.92 | 123  | 0.7080          | 0.7609   |
| 0.2201        | 20.0  | 130  | 0.7100          | 0.7391   |
| 0.2201        | 20.92 | 136  | 0.7673          | 0.7609   |
| 0.1889        | 22.0  | 143  | 0.7889          | 0.7391   |
| 0.1889        | 22.92 | 149  | 0.7971          | 0.7391   |
| 0.1463        | 24.0  | 156  | 0.6888          | 0.7826   |
| 0.1261        | 24.92 | 162  | 0.8399          | 0.7609   |
| 0.1261        | 26.0  | 169  | 0.7244          | 0.7826   |
| 0.1489        | 26.92 | 175  | 0.8311          | 0.7391   |
| 0.1132        | 28.0  | 182  | 0.7987          | 0.7609   |
| 0.1132        | 28.92 | 188  | 0.7380          | 0.8043   |
| 0.1279        | 30.0  | 195  | 0.8103          | 0.8043   |
| 0.0925        | 30.92 | 201  | 0.8462          | 0.7609   |
| 0.0925        | 32.0  | 208  | 0.8233          | 0.8043   |
| 0.0893        | 32.92 | 214  | 0.8241          | 0.7826   |
| 0.083         | 34.0  | 221  | 0.8443          | 0.7826   |
| 0.083         | 34.92 | 227  | 0.8429          | 0.7826   |
| 0.1044        | 36.0  | 234  | 0.9362          | 0.7609   |
| 0.0739        | 36.92 | 240  | 1.1173          | 0.7391   |
| 0.0739        | 38.0  | 247  | 0.7812          | 0.8261   |
| 0.0962        | 38.92 | 253  | 0.7595          | 0.8043   |
| 0.0869        | 40.0  | 260  | 0.8031          | 0.8261   |
| 0.0869        | 40.92 | 266  | 0.8359          | 0.8261   |
| 0.0837        | 42.0  | 273  | 0.8151          | 0.8261   |
| 0.0837        | 42.92 | 279  | 0.8295          | 0.8261   |
| 0.0535        | 44.0  | 286  | 0.8096          | 0.8261   |
| 0.0694        | 44.92 | 292  | 0.8352          | 0.8261   |
| 0.0694        | 46.0  | 299  | 0.8216          | 0.8261   |
| 0.0736        | 46.92 | 305  | 0.8683          | 0.8043   |
| 0.0705        | 48.0  | 312  | 0.8554          | 0.8261   |
| 0.0705        | 48.92 | 318  | 0.8139          | 0.8478   |
| 0.0559        | 50.0  | 325  | 0.9030          | 0.7826   |
| 0.0474        | 50.92 | 331  | 0.9053          | 0.7609   |
| 0.0474        | 52.0  | 338  | 0.8810          | 0.8261   |
| 0.0477        | 52.92 | 344  | 0.8912          | 0.8043   |
| 0.0529        | 54.0  | 351  | 0.9078          | 0.8043   |
| 0.0529        | 54.92 | 357  | 0.8804          | 0.8043   |
| 0.038         | 56.0  | 364  | 0.9498          | 0.7826   |
| 0.0407        | 56.92 | 370  | 0.9134          | 0.8043   |
| 0.0407        | 58.0  | 377  | 0.8452          | 0.8478   |
| 0.0353        | 58.92 | 383  | 0.8735          | 0.8261   |
| 0.0349        | 60.0  | 390  | 0.9153          | 0.8043   |
| 0.0349        | 60.92 | 396  | 0.9209          | 0.8043   |
| 0.0322        | 62.0  | 403  | 0.9091          | 0.8261   |
| 0.0322        | 62.92 | 409  | 0.9137          | 0.8261   |
| 0.0392        | 64.0  | 416  | 0.8896          | 0.8261   |
| 0.0419        | 64.92 | 422  | 0.8613          | 0.8478   |
| 0.0419        | 66.0  | 429  | 0.8844          | 0.8261   |
| 0.0518        | 66.92 | 435  | 0.9093          | 0.8043   |
| 0.0349        | 68.0  | 442  | 0.9082          | 0.8043   |
| 0.0349        | 68.92 | 448  | 0.8879          | 0.8261   |
| 0.0359        | 70.0  | 455  | 0.8809          | 0.8261   |
| 0.0377        | 70.92 | 461  | 0.8777          | 0.8261   |
| 0.0377        | 72.0  | 468  | 0.8845          | 0.8261   |
| 0.0324        | 72.92 | 474  | 0.8845          | 0.8261   |
| 0.0365        | 73.85 | 480  | 0.8850          | 0.8261   |


### Framework versions

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

avanzada leve moderada no dmae