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# smids_3x_deit_small_adamax_00001_fold4

This model is a fine-tuned version of [facebook/deit-small-patch16-224](https://huggingface.co/facebook/deit-small-patch16-224) on the imagefolder dataset.
It achieves the following results on the evaluation set:
- Loss: 1.0772
- Accuracy: 0.87

## 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: 1e-05
- 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 |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|
| 0.3797        | 1.0   | 225   | 0.3792          | 0.8467   |
| 0.1834        | 2.0   | 450   | 0.3415          | 0.8583   |
| 0.1521        | 3.0   | 675   | 0.3364          | 0.8767   |
| 0.1673        | 4.0   | 900   | 0.3475          | 0.8733   |
| 0.1145        | 5.0   | 1125  | 0.3681          | 0.8733   |
| 0.0638        | 6.0   | 1350  | 0.4237          | 0.8683   |
| 0.0638        | 7.0   | 1575  | 0.4385          | 0.875    |
| 0.1043        | 8.0   | 1800  | 0.4695          | 0.88     |
| 0.0499        | 9.0   | 2025  | 0.5316          | 0.8783   |
| 0.0307        | 10.0  | 2250  | 0.6026          | 0.87     |
| 0.0636        | 11.0  | 2475  | 0.6654          | 0.87     |
| 0.0182        | 12.0  | 2700  | 0.6645          | 0.8767   |
| 0.0011        | 13.0  | 2925  | 0.7026          | 0.8717   |
| 0.0033        | 14.0  | 3150  | 0.7540          | 0.8717   |
| 0.0005        | 15.0  | 3375  | 0.7821          | 0.87     |
| 0.0003        | 16.0  | 3600  | 0.7783          | 0.8767   |
| 0.0002        | 17.0  | 3825  | 0.8172          | 0.8717   |
| 0.0057        | 18.0  | 4050  | 0.8281          | 0.8717   |
| 0.0001        | 19.0  | 4275  | 0.8404          | 0.8717   |
| 0.0001        | 20.0  | 4500  | 0.8657          | 0.8733   |
| 0.0001        | 21.0  | 4725  | 0.8953          | 0.875    |
| 0.0001        | 22.0  | 4950  | 0.9021          | 0.87     |
| 0.0001        | 23.0  | 5175  | 0.9104          | 0.8733   |
| 0.0001        | 24.0  | 5400  | 0.9154          | 0.8733   |
| 0.0001        | 25.0  | 5625  | 0.9415          | 0.87     |
| 0.0           | 26.0  | 5850  | 0.9539          | 0.8733   |
| 0.0           | 27.0  | 6075  | 0.9672          | 0.87     |
| 0.0001        | 28.0  | 6300  | 0.9843          | 0.8683   |
| 0.0           | 29.0  | 6525  | 0.9786          | 0.87     |
| 0.0           | 30.0  | 6750  | 0.9994          | 0.8683   |
| 0.0           | 31.0  | 6975  | 0.9988          | 0.8733   |
| 0.0           | 32.0  | 7200  | 1.0032          | 0.8717   |
| 0.0           | 33.0  | 7425  | 1.0174          | 0.87     |
| 0.0           | 34.0  | 7650  | 1.0208          | 0.87     |
| 0.0           | 35.0  | 7875  | 1.0273          | 0.8717   |
| 0.0           | 36.0  | 8100  | 1.0325          | 0.8717   |
| 0.0           | 37.0  | 8325  | 1.0347          | 0.87     |
| 0.0           | 38.0  | 8550  | 1.0403          | 0.87     |
| 0.0           | 39.0  | 8775  | 1.0555          | 0.87     |
| 0.0           | 40.0  | 9000  | 1.0534          | 0.87     |
| 0.0           | 41.0  | 9225  | 1.0588          | 0.87     |
| 0.0084        | 42.0  | 9450  | 1.0654          | 0.87     |
| 0.0           | 43.0  | 9675  | 1.0680          | 0.87     |
| 0.0079        | 44.0  | 9900  | 1.0688          | 0.8683   |
| 0.0           | 45.0  | 10125 | 1.0727          | 0.87     |
| 0.0           | 46.0  | 10350 | 1.0741          | 0.87     |
| 0.0           | 47.0  | 10575 | 1.0751          | 0.87     |
| 0.0           | 48.0  | 10800 | 1.0765          | 0.87     |
| 0.0           | 49.0  | 11025 | 1.0763          | 0.8667   |
| 0.0           | 50.0  | 11250 | 1.0772          | 0.87     |


### Framework versions

- Transformers 4.32.1
- Pytorch 2.1.0+cu121
- Datasets 2.12.0
- Tokenizers 0.13.2

abnormal_sperm non-sperm normal_sperm