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# smids_5x_deit_tiny_rms_0001_fold1

This model is a fine-tuned version of [facebook/deit-tiny-patch16-224](https://huggingface.co/facebook/deit-tiny-patch16-224) on the imagefolder dataset.
It achieves the following results on the evaluation set:
- Loss: 0.9972
- Accuracy: 0.9048

## 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: 0.0001
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- 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 | Accuracy |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|
| 0.2949        | 1.0   | 376   | 0.4792          | 0.7896   |
| 0.1877        | 2.0   | 752   | 0.3869          | 0.8631   |
| 0.1943        | 3.0   | 1128  | 0.4273          | 0.8514   |
| 0.1151        | 4.0   | 1504  | 0.4170          | 0.8932   |
| 0.1309        | 5.0   | 1880  | 0.4159          | 0.8748   |
| 0.0937        | 6.0   | 2256  | 0.5222          | 0.8831   |
| 0.0299        | 7.0   | 2632  | 0.5974          | 0.8932   |
| 0.0659        | 8.0   | 3008  | 0.6171          | 0.8715   |
| 0.0586        | 9.0   | 3384  | 0.7200          | 0.8781   |
| 0.0715        | 10.0  | 3760  | 0.9149          | 0.8664   |
| 0.0752        | 11.0  | 4136  | 0.7964          | 0.8765   |
| 0.0401        | 12.0  | 4512  | 0.6968          | 0.8831   |
| 0.0094        | 13.0  | 4888  | 0.6898          | 0.8865   |
| 0.0111        | 14.0  | 5264  | 0.7411          | 0.8932   |
| 0.0334        | 15.0  | 5640  | 0.8411          | 0.8798   |
| 0.0369        | 16.0  | 6016  | 0.7849          | 0.8798   |
| 0.0017        | 17.0  | 6392  | 0.7191          | 0.8898   |
| 0.0026        | 18.0  | 6768  | 0.8047          | 0.8815   |
| 0.0265        | 19.0  | 7144  | 0.6550          | 0.8982   |
| 0.0527        | 20.0  | 7520  | 0.7590          | 0.8798   |
| 0.0052        | 21.0  | 7896  | 0.7860          | 0.8881   |
| 0.001         | 22.0  | 8272  | 0.8487          | 0.8965   |
| 0.0432        | 23.0  | 8648  | 0.8524          | 0.8865   |
| 0.0032        | 24.0  | 9024  | 0.8174          | 0.9015   |
| 0.0001        | 25.0  | 9400  | 0.8214          | 0.8815   |
| 0.0146        | 26.0  | 9776  | 0.9080          | 0.8765   |
| 0.0           | 27.0  | 10152 | 0.8028          | 0.9032   |
| 0.0001        | 28.0  | 10528 | 0.9579          | 0.8915   |
| 0.0043        | 29.0  | 10904 | 0.8349          | 0.8982   |
| 0.0053        | 30.0  | 11280 | 0.9140          | 0.8831   |
| 0.0204        | 31.0  | 11656 | 0.9273          | 0.8898   |
| 0.0001        | 32.0  | 12032 | 0.9480          | 0.8848   |
| 0.0006        | 33.0  | 12408 | 1.0366          | 0.8865   |
| 0.0042        | 34.0  | 12784 | 1.0682          | 0.8798   |
| 0.0025        | 35.0  | 13160 | 0.9542          | 0.8932   |
| 0.0006        | 36.0  | 13536 | 0.8930          | 0.9048   |
| 0.0001        | 37.0  | 13912 | 0.9451          | 0.8932   |
| 0.0112        | 38.0  | 14288 | 1.0303          | 0.8848   |
| 0.0           | 39.0  | 14664 | 1.0298          | 0.8932   |
| 0.0           | 40.0  | 15040 | 0.9996          | 0.8932   |
| 0.0           | 41.0  | 15416 | 0.9909          | 0.8998   |
| 0.0           | 42.0  | 15792 | 0.9652          | 0.9015   |
| 0.0           | 43.0  | 16168 | 0.9547          | 0.9032   |
| 0.0           | 44.0  | 16544 | 0.9994          | 0.8982   |
| 0.0           | 45.0  | 16920 | 0.9802          | 0.9015   |
| 0.003         | 46.0  | 17296 | 0.9911          | 0.9032   |
| 0.0           | 47.0  | 17672 | 0.9936          | 0.9048   |
| 0.0           | 48.0  | 18048 | 0.9937          | 0.9048   |
| 0.0           | 49.0  | 18424 | 0.9932          | 0.9048   |
| 0.0025        | 50.0  | 18800 | 0.9972          | 0.9048   |


### Framework versions

- Transformers 4.32.1
- Pytorch 2.1.1+cu121
- Datasets 2.12.0
- Tokenizers 0.13.2

abnormal_sperm non-sperm normal_sperm