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# FFPP-Raw_1FPS_faces-expand-0-aligned_augmentation-normalize-image-mean-std

This model is a fine-tuned version of [microsoft/swin-tiny-patch4-window7-224](https://huggingface.co/microsoft/swin-tiny-patch4-window7-224) on the imagefolder dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0034
- Accuracy: 0.9983
- Recall: 0.9929
- Precision: 0.9994
- F1: 0.9961
- Roc Auc: 1.0000

## 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: 16
- eval_batch_size: 16
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 64
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 20

### Training results

| Training Loss | Epoch | Step  | Validation Loss | Accuracy | Recall | Precision | F1     | Roc Auc |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|:------:|:---------:|:------:|:-------:|
| 0.1054        | 1.0   | 1377  | 0.0750          | 0.9716   | 0.9180 | 0.9495    | 0.9335 | 0.9957  |
| 0.0785        | 2.0   | 2755  | 0.0406          | 0.9853   | 0.9596 | 0.9723    | 0.9660 | 0.9986  |
| 0.0713        | 3.0   | 4132  | 0.0348          | 0.9878   | 0.9534 | 0.9899    | 0.9713 | 0.9994  |
| 0.0447        | 4.0   | 5510  | 0.0172          | 0.9933   | 0.9842 | 0.9851    | 0.9846 | 0.9997  |
| 0.0388        | 5.0   | 6887  | 0.0186          | 0.9936   | 0.9741 | 0.9964    | 0.9851 | 0.9998  |
| 0.0236        | 6.0   | 8265  | 0.0119          | 0.9957   | 0.9830 | 0.9971    | 0.9900 | 0.9999  |
| 0.031         | 7.0   | 9642  | 0.0137          | 0.9957   | 0.9928 | 0.9873    | 0.9900 | 0.9999  |
| 0.015         | 8.0   | 11020 | 0.0072          | 0.9972   | 0.9903 | 0.9969    | 0.9936 | 1.0000  |
| 0.0429        | 9.0   | 12397 | 0.0087          | 0.9967   | 0.9863 | 0.9987    | 0.9925 | 0.9999  |
| 0.0186        | 10.0  | 13775 | 0.0052          | 0.9979   | 0.9919 | 0.9985    | 0.9952 | 1.0000  |
| 0.0282        | 11.0  | 15152 | 0.0069          | 0.9974   | 0.9892 | 0.9988    | 0.9940 | 1.0000  |
| 0.0034        | 12.0  | 16530 | 0.0045          | 0.9979   | 0.9947 | 0.9956    | 0.9951 | 1.0000  |
| 0.0187        | 13.0  | 17907 | 0.0070          | 0.9972   | 0.9886 | 0.9986    | 0.9935 | 1.0000  |
| 0.0136        | 14.0  | 19285 | 0.0038          | 0.9982   | 0.9931 | 0.9988    | 0.9959 | 1.0000  |
| 0.006         | 15.0  | 20662 | 0.0039          | 0.9982   | 0.9928 | 0.9988    | 0.9958 | 1.0000  |
| 0.0067        | 16.0  | 22040 | 0.0037          | 0.9983   | 0.9926 | 0.9995    | 0.9960 | 1.0000  |
| 0.0121        | 17.0  | 23417 | 0.0036          | 0.9983   | 0.9929 | 0.9992    | 0.9960 | 1.0000  |
| 0.0026        | 18.0  | 24795 | 0.0037          | 0.9982   | 0.9925 | 0.9993    | 0.9959 | 1.0000  |
| 0.0024        | 19.0  | 26172 | 0.0034          | 0.9983   | 0.9932 | 0.9991    | 0.9961 | 1.0000  |
| 0.002         | 19.99 | 27540 | 0.0034          | 0.9983   | 0.9929 | 0.9994    | 0.9961 | 1.0000  |


### Framework versions

- Transformers 4.39.2
- Pytorch 2.2.2
- Datasets 2.18.0
- Tokenizers 0.15.2

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