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# 6-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: 0.6407
- F1: 0.7990

## 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     |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| 2.0829        | 1.0   | 18   | 2.0240          | 0.1072 |
| 1.9599        | 2.0   | 36   | 1.8375          | 0.3757 |
| 1.725         | 3.0   | 54   | 1.5851          | 0.4462 |
| 1.5014        | 4.0   | 72   | 1.3785          | 0.4928 |
| 1.3135        | 5.0   | 90   | 1.2678          | 0.5368 |
| 1.2446        | 6.0   | 108  | 1.1646          | 0.6053 |
| 1.1576        | 7.0   | 126  | 1.1553          | 0.5554 |
| 1.0868        | 8.0   | 144  | 1.0353          | 0.6231 |
| 1.0121        | 9.0   | 162  | 1.0081          | 0.6435 |
| 0.988         | 10.0  | 180  | 0.9306          | 0.6951 |
| 0.9663        | 11.0  | 198  | 0.9062          | 0.7062 |
| 0.8709        | 12.0  | 216  | 0.8939          | 0.6950 |
| 0.8891        | 13.0  | 234  | 0.8283          | 0.7371 |
| 0.843         | 14.0  | 252  | 0.7945          | 0.7482 |
| 0.8339        | 15.0  | 270  | 0.8384          | 0.7236 |
| 0.8029        | 16.0  | 288  | 0.8167          | 0.7426 |
| 0.777         | 17.0  | 306  | 0.7842          | 0.7659 |
| 0.7592        | 18.0  | 324  | 0.8064          | 0.7427 |
| 0.7052        | 19.0  | 342  | 0.7804          | 0.7553 |
| 0.7556        | 20.0  | 360  | 0.7332          | 0.7851 |
| 0.688         | 21.0  | 378  | 0.7643          | 0.7676 |
| 0.7216        | 22.0  | 396  | 0.7391          | 0.7623 |
| 0.6434        | 23.0  | 414  | 0.6996          | 0.7869 |
| 0.6673        | 24.0  | 432  | 0.7297          | 0.7775 |
| 0.6474        | 25.0  | 450  | 0.7006          | 0.7807 |
| 0.6352        | 26.0  | 468  | 0.7134          | 0.7778 |
| 0.6068        | 27.0  | 486  | 0.7377          | 0.7776 |
| 0.5942        | 28.0  | 504  | 0.6723          | 0.8089 |
| 0.5945        | 29.0  | 522  | 0.6686          | 0.7941 |
| 0.603         | 30.0  | 540  | 0.6667          | 0.7809 |
| 0.5974        | 31.0  | 558  | 0.6698          | 0.7946 |
| 0.5743        | 32.0  | 576  | 0.6531          | 0.8090 |
| 0.5663        | 33.0  | 594  | 0.6756          | 0.8013 |
| 0.5583        | 34.0  | 612  | 0.6535          | 0.8025 |
| 0.5199        | 35.0  | 630  | 0.6542          | 0.7936 |
| 0.5851        | 36.0  | 648  | 0.6595          | 0.7956 |
| 0.5105        | 37.0  | 666  | 0.6784          | 0.7886 |
| 0.4947        | 38.0  | 684  | 0.6625          | 0.8002 |
| 0.5197        | 39.0  | 702  | 0.6637          | 0.7975 |
| 0.514         | 40.0  | 720  | 0.6527          | 0.7925 |
| 0.4949        | 41.0  | 738  | 0.6482          | 0.7992 |
| 0.5047        | 42.0  | 756  | 0.6427          | 0.8036 |
| 0.5058        | 43.0  | 774  | 0.6437          | 0.8052 |
| 0.4645        | 44.0  | 792  | 0.6324          | 0.8062 |
| 0.4411        | 45.0  | 810  | 0.6481          | 0.8052 |
| 0.4602        | 46.0  | 828  | 0.6460          | 0.8037 |
| 0.4265        | 47.0  | 846  | 0.6505          | 0.8036 |
| 0.4945        | 48.0  | 864  | 0.6467          | 0.7991 |
| 0.4794        | 49.0  | 882  | 0.6388          | 0.8084 |
| 0.442         | 50.0  | 900  | 0.6407          | 0.7990 |


### Framework versions

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

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