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# smids_3x_deit_small_sgd_00001_fold3

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: 1.0287
- Accuracy: 0.4617

## 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 |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|
| 1.0656        | 1.0   | 225   | 1.0860          | 0.385    |
| 1.0566        | 2.0   | 450   | 1.0832          | 0.385    |
| 1.0608        | 3.0   | 675   | 1.0806          | 0.385    |
| 1.0487        | 4.0   | 900   | 1.0780          | 0.3883   |
| 1.0631        | 5.0   | 1125  | 1.0755          | 0.39     |
| 1.0618        | 6.0   | 1350  | 1.0731          | 0.395    |
| 1.0528        | 7.0   | 1575  | 1.0708          | 0.3967   |
| 1.0523        | 8.0   | 1800  | 1.0686          | 0.3967   |
| 1.0663        | 9.0   | 2025  | 1.0664          | 0.3983   |
| 1.0433        | 10.0  | 2250  | 1.0643          | 0.405    |
| 1.0514        | 11.0  | 2475  | 1.0623          | 0.4067   |
| 1.0454        | 12.0  | 2700  | 1.0603          | 0.4083   |
| 1.0616        | 13.0  | 2925  | 1.0585          | 0.41     |
| 1.031         | 14.0  | 3150  | 1.0567          | 0.415    |
| 1.0471        | 15.0  | 3375  | 1.0550          | 0.42     |
| 1.0587        | 16.0  | 3600  | 1.0533          | 0.42     |
| 1.0376        | 17.0  | 3825  | 1.0517          | 0.4233   |
| 1.0297        | 18.0  | 4050  | 1.0502          | 0.4267   |
| 1.0331        | 19.0  | 4275  | 1.0487          | 0.435    |
| 1.0488        | 20.0  | 4500  | 1.0473          | 0.4367   |
| 1.0355        | 21.0  | 4725  | 1.0459          | 0.4367   |
| 1.0375        | 22.0  | 4950  | 1.0446          | 0.4367   |
| 1.0233        | 23.0  | 5175  | 1.0434          | 0.4367   |
| 1.0207        | 24.0  | 5400  | 1.0422          | 0.44     |
| 1.0243        | 25.0  | 5625  | 1.0410          | 0.445    |
| 1.0105        | 26.0  | 5850  | 1.0400          | 0.4467   |
| 1.019         | 27.0  | 6075  | 1.0389          | 0.4467   |
| 1.0208        | 28.0  | 6300  | 1.0379          | 0.4467   |
| 1.0103        | 29.0  | 6525  | 1.0370          | 0.4483   |
| 1.0126        | 30.0  | 6750  | 1.0362          | 0.4517   |
| 1.0069        | 31.0  | 6975  | 1.0354          | 0.4533   |
| 1.0415        | 32.0  | 7200  | 1.0346          | 0.455    |
| 1.0107        | 33.0  | 7425  | 1.0339          | 0.4567   |
| 1.013         | 34.0  | 7650  | 1.0332          | 0.4583   |
| 0.9989        | 35.0  | 7875  | 1.0326          | 0.4583   |
| 0.9864        | 36.0  | 8100  | 1.0320          | 0.4583   |
| 1.0065        | 37.0  | 8325  | 1.0315          | 0.4583   |
| 1.0147        | 38.0  | 8550  | 1.0310          | 0.46     |
| 1.0279        | 39.0  | 8775  | 1.0306          | 0.4583   |
| 1.0198        | 40.0  | 9000  | 1.0302          | 0.4583   |
| 1.0298        | 41.0  | 9225  | 1.0299          | 0.4583   |
| 1.0219        | 42.0  | 9450  | 1.0296          | 0.4583   |
| 1.0277        | 43.0  | 9675  | 1.0293          | 0.4583   |
| 1.0097        | 44.0  | 9900  | 1.0291          | 0.4583   |
| 1.0316        | 45.0  | 10125 | 1.0290          | 0.4583   |
| 1.0133        | 46.0  | 10350 | 1.0288          | 0.46     |
| 0.9895        | 47.0  | 10575 | 1.0288          | 0.4617   |
| 1.0235        | 48.0  | 10800 | 1.0287          | 0.4617   |
| 1.0129        | 49.0  | 11025 | 1.0287          | 0.4617   |
| 0.9906        | 50.0  | 11250 | 1.0287          | 0.4617   |


### 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