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# vit-base-patch16-224-ve-U13-b-120

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: 0.6378
- Accuracy: 0.8696

## 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: 120

### Training results

| Training Loss | Epoch  | Step | Validation Loss | Accuracy |
|:-------------:|:------:|:----:|:---------------:|:--------:|
| No log        | 0.92   | 6    | 1.3853          | 0.3261   |
| 1.3854        | 2.0    | 13   | 1.3764          | 0.6087   |
| 1.3854        | 2.92   | 19   | 1.3484          | 0.5870   |
| 1.3679        | 4.0    | 26   | 1.2873          | 0.5      |
| 1.2945        | 4.92   | 32   | 1.2122          | 0.4130   |
| 1.2945        | 6.0    | 39   | 1.1105          | 0.4130   |
| 1.1527        | 6.92   | 45   | 1.0386          | 0.5652   |
| 0.9999        | 8.0    | 52   | 0.9454          | 0.7174   |
| 0.9999        | 8.92   | 58   | 0.8886          | 0.7174   |
| 0.8606        | 10.0   | 65   | 0.7935          | 0.8261   |
| 0.7153        | 10.92  | 71   | 0.7424          | 0.7826   |
| 0.7153        | 12.0   | 78   | 0.6803          | 0.8043   |
| 0.5691        | 12.92  | 84   | 0.6104          | 0.8261   |
| 0.4187        | 14.0   | 91   | 0.5848          | 0.8043   |
| 0.4187        | 14.92  | 97   | 0.5254          | 0.8478   |
| 0.3203        | 16.0   | 104  | 0.5790          | 0.8261   |
| 0.2248        | 16.92  | 110  | 0.6315          | 0.7826   |
| 0.2248        | 18.0   | 117  | 0.7864          | 0.7391   |
| 0.2384        | 18.92  | 123  | 0.6028          | 0.8043   |
| 0.2437        | 20.0   | 130  | 0.6135          | 0.8043   |
| 0.2437        | 20.92  | 136  | 0.6210          | 0.7826   |
| 0.2309        | 22.0   | 143  | 0.6329          | 0.8043   |
| 0.2309        | 22.92  | 149  | 0.6236          | 0.8261   |
| 0.1367        | 24.0   | 156  | 0.6919          | 0.7826   |
| 0.1318        | 24.92  | 162  | 0.7770          | 0.7391   |
| 0.1318        | 26.0   | 169  | 0.7394          | 0.7609   |
| 0.1228        | 26.92  | 175  | 0.5662          | 0.8261   |
| 0.1173        | 28.0   | 182  | 0.8995          | 0.7391   |
| 0.1173        | 28.92  | 188  | 0.6780          | 0.7826   |
| 0.129         | 30.0   | 195  | 0.7868          | 0.7826   |
| 0.1043        | 30.92  | 201  | 0.7302          | 0.8261   |
| 0.1043        | 32.0   | 208  | 0.7549          | 0.7826   |
| 0.0917        | 32.92  | 214  | 0.6124          | 0.7826   |
| 0.0843        | 34.0   | 221  | 0.6607          | 0.8261   |
| 0.0843        | 34.92  | 227  | 0.6816          | 0.8261   |
| 0.1054        | 36.0   | 234  | 0.6349          | 0.7826   |
| 0.0923        | 36.92  | 240  | 0.7346          | 0.8261   |
| 0.0923        | 38.0   | 247  | 0.7571          | 0.8043   |
| 0.0879        | 38.92  | 253  | 0.7625          | 0.7826   |
| 0.0632        | 40.0   | 260  | 0.7908          | 0.7826   |
| 0.0632        | 40.92  | 266  | 0.8490          | 0.7826   |
| 0.0533        | 42.0   | 273  | 0.8177          | 0.8043   |
| 0.0533        | 42.92  | 279  | 0.8878          | 0.7826   |
| 0.0633        | 44.0   | 286  | 0.6725          | 0.8043   |
| 0.0526        | 44.92  | 292  | 0.7090          | 0.8261   |
| 0.0526        | 46.0   | 299  | 0.7725          | 0.8043   |
| 0.0716        | 46.92  | 305  | 0.7965          | 0.8043   |
| 0.0783        | 48.0   | 312  | 0.9016          | 0.8043   |
| 0.0783        | 48.92  | 318  | 0.9555          | 0.7826   |
| 0.0789        | 50.0   | 325  | 0.9379          | 0.7609   |
| 0.0418        | 50.92  | 331  | 0.7863          | 0.8043   |
| 0.0418        | 52.0   | 338  | 0.7688          | 0.8261   |
| 0.0483        | 52.92  | 344  | 0.7040          | 0.8261   |
| 0.0493        | 54.0   | 351  | 0.7560          | 0.8043   |
| 0.0493        | 54.92  | 357  | 0.9141          | 0.7609   |
| 0.0554        | 56.0   | 364  | 0.7642          | 0.8043   |
| 0.0612        | 56.92  | 370  | 0.7923          | 0.8478   |
| 0.0612        | 58.0   | 377  | 0.8156          | 0.8478   |
| 0.0468        | 58.92  | 383  | 0.6847          | 0.8043   |
| 0.0419        | 60.0   | 390  | 0.6378          | 0.8696   |
| 0.0419        | 60.92  | 396  | 0.8031          | 0.8261   |
| 0.0436        | 62.0   | 403  | 0.7883          | 0.8478   |
| 0.0436        | 62.92  | 409  | 0.8270          | 0.8478   |
| 0.0429        | 64.0   | 416  | 0.8654          | 0.8261   |
| 0.0438        | 64.92  | 422  | 0.7054          | 0.8478   |
| 0.0438        | 66.0   | 429  | 0.6511          | 0.8696   |
| 0.0378        | 66.92  | 435  | 0.7341          | 0.8478   |
| 0.0294        | 68.0   | 442  | 0.8695          | 0.8478   |
| 0.0294        | 68.92  | 448  | 0.8984          | 0.8043   |
| 0.0362        | 70.0   | 455  | 0.9207          | 0.8261   |
| 0.0367        | 70.92  | 461  | 0.9426          | 0.7826   |
| 0.0367        | 72.0   | 468  | 0.9156          | 0.8261   |
| 0.0332        | 72.92  | 474  | 0.9034          | 0.8043   |
| 0.0294        | 74.0   | 481  | 0.9086          | 0.7826   |
| 0.0294        | 74.92  | 487  | 0.8890          | 0.8043   |
| 0.0285        | 76.0   | 494  | 0.8999          | 0.8261   |
| 0.0232        | 76.92  | 500  | 0.9546          | 0.7826   |
| 0.0232        | 78.0   | 507  | 0.9126          | 0.8043   |
| 0.0349        | 78.92  | 513  | 0.9537          | 0.8043   |
| 0.0393        | 80.0   | 520  | 0.9870          | 0.8043   |
| 0.0393        | 80.92  | 526  | 0.9763          | 0.8043   |
| 0.0225        | 82.0   | 533  | 0.9384          | 0.8043   |
| 0.0225        | 82.92  | 539  | 0.8600          | 0.8478   |
| 0.0304        | 84.0   | 546  | 0.8530          | 0.8478   |
| 0.0263        | 84.92  | 552  | 0.8588          | 0.8043   |
| 0.0263        | 86.0   | 559  | 0.8635          | 0.8043   |
| 0.0186        | 86.92  | 565  | 0.8602          | 0.8261   |
| 0.0258        | 88.0   | 572  | 0.8514          | 0.8261   |
| 0.0258        | 88.92  | 578  | 0.8431          | 0.8261   |
| 0.0161        | 90.0   | 585  | 0.8046          | 0.8261   |
| 0.0208        | 90.92  | 591  | 0.8082          | 0.8261   |
| 0.0208        | 92.0   | 598  | 0.8276          | 0.8043   |
| 0.0331        | 92.92  | 604  | 0.7698          | 0.8261   |
| 0.0322        | 94.0   | 611  | 0.8191          | 0.8261   |
| 0.0322        | 94.92  | 617  | 0.9046          | 0.8043   |
| 0.0284        | 96.0   | 624  | 0.9535          | 0.8043   |
| 0.0187        | 96.92  | 630  | 0.9304          | 0.8043   |
| 0.0187        | 98.0   | 637  | 0.8834          | 0.8043   |
| 0.0209        | 98.92  | 643  | 0.8519          | 0.8043   |
| 0.027         | 100.0  | 650  | 0.8522          | 0.8261   |
| 0.027         | 100.92 | 656  | 0.8978          | 0.8261   |
| 0.0218        | 102.0  | 663  | 0.9194          | 0.8261   |
| 0.0218        | 102.92 | 669  | 0.9140          | 0.8261   |
| 0.021         | 104.0  | 676  | 0.9173          | 0.8261   |
| 0.0179        | 104.92 | 682  | 0.9279          | 0.8261   |
| 0.0179        | 106.0  | 689  | 0.9263          | 0.8261   |
| 0.0167        | 106.92 | 695  | 0.9158          | 0.8261   |
| 0.0229        | 108.0  | 702  | 0.9109          | 0.8261   |
| 0.0229        | 108.92 | 708  | 0.9065          | 0.8261   |
| 0.0219        | 110.0  | 715  | 0.9011          | 0.8261   |
| 0.0271        | 110.77 | 720  | 0.9002          | 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