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# 8-classifier-finetuned-padchest

This model is a fine-tuned version of [google/vit-base-patch16-224-in21k](https://huggingface.co/google/vit-base-patch16-224-in21k) on the imagefolder dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2276
- F1: 0.9325

## 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: 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: 0.1
- num_epochs: 50

### Training results

| Training Loss | Epoch | Step | Validation Loss | F1     |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| 0.6321        | 1.0   | 18   | 0.5224          | 0.7896 |
| 0.4633        | 2.0   | 36   | 0.3809          | 0.7896 |
| 0.3552        | 3.0   | 54   | 0.3305          | 0.7896 |
| 0.2718        | 4.0   | 72   | 0.2696          | 0.8197 |
| 0.2345        | 5.0   | 90   | 0.2178          | 0.9149 |
| 0.211         | 6.0   | 108  | 0.2405          | 0.8861 |
| 0.2208        | 7.0   | 126  | 0.2713          | 0.8605 |
| 0.1698        | 8.0   | 144  | 0.1747          | 0.9422 |
| 0.1547        | 9.0   | 162  | 0.1783          | 0.9322 |
| 0.1697        | 10.0  | 180  | 0.1629          | 0.9350 |
| 0.1684        | 11.0  | 198  | 0.1740          | 0.9319 |
| 0.1722        | 12.0  | 216  | 0.1885          | 0.9173 |
| 0.158         | 13.0  | 234  | 0.1637          | 0.9331 |
| 0.1469        | 14.0  | 252  | 0.1716          | 0.9325 |
| 0.1271        | 15.0  | 270  | 0.1700          | 0.9384 |
| 0.131         | 16.0  | 288  | 0.1785          | 0.9409 |
| 0.1245        | 17.0  | 306  | 0.2124          | 0.9206 |
| 0.1182        | 18.0  | 324  | 0.1715          | 0.9322 |
| 0.1082        | 19.0  | 342  | 0.1946          | 0.9322 |
| 0.1274        | 20.0  | 360  | 0.1757          | 0.9379 |
| 0.1115        | 21.0  | 378  | 0.1908          | 0.9307 |
| 0.0995        | 22.0  | 396  | 0.2001          | 0.9289 |
| 0.0996        | 23.0  | 414  | 0.1820          | 0.9293 |
| 0.0993        | 24.0  | 432  | 0.2095          | 0.9355 |
| 0.1006        | 25.0  | 450  | 0.1973          | 0.9314 |
| 0.0703        | 26.0  | 468  | 0.1934          | 0.9389 |
| 0.0901        | 27.0  | 486  | 0.2276          | 0.9238 |
| 0.0827        | 28.0  | 504  | 0.1949          | 0.936  |
| 0.0701        | 29.0  | 522  | 0.2076          | 0.9317 |
| 0.0813        | 30.0  | 540  | 0.2001          | 0.9374 |
| 0.0776        | 31.0  | 558  | 0.2440          | 0.9357 |
| 0.0842        | 32.0  | 576  | 0.2163          | 0.9271 |
| 0.0872        | 33.0  | 594  | 0.2248          | 0.9332 |
| 0.0743        | 34.0  | 612  | 0.2007          | 0.9344 |
| 0.0692        | 35.0  | 630  | 0.1971          | 0.9283 |
| 0.0763        | 36.0  | 648  | 0.2094          | 0.9393 |
| 0.0714        | 37.0  | 666  | 0.2139          | 0.9271 |
| 0.0683        | 38.0  | 684  | 0.2065          | 0.9331 |
| 0.0698        | 39.0  | 702  | 0.2177          | 0.9295 |
| 0.0507        | 40.0  | 720  | 0.2171          | 0.9344 |
| 0.0523        | 41.0  | 738  | 0.2240          | 0.9344 |
| 0.0546        | 42.0  | 756  | 0.2083          | 0.9394 |
| 0.0695        | 43.0  | 774  | 0.2171          | 0.936  |
| 0.0634        | 44.0  | 792  | 0.2193          | 0.9301 |
| 0.0462        | 45.0  | 810  | 0.2017          | 0.9409 |
| 0.0581        | 46.0  | 828  | 0.2209          | 0.9350 |
| 0.0468        | 47.0  | 846  | 0.2335          | 0.9301 |
| 0.0424        | 48.0  | 864  | 0.2294          | 0.9301 |
| 0.0472        | 49.0  | 882  | 0.2310          | 0.9350 |
| 0.044         | 50.0  | 900  | 0.2276          | 0.9325 |


### Framework versions

- Transformers 4.28.0.dev0
- Pytorch 2.0.0+cu117
- Datasets 2.18.0
- Tokenizers 0.13.3

artifacts no artifacts