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# smids_3x_deit_base_sgd_00001_fold3

This model is a fine-tuned version of [facebook/deit-base-patch16-224](https://huggingface.co/facebook/deit-base-patch16-224) on the imagefolder dataset.
It achieves the following results on the evaluation set:
- Loss: 1.0784
- Accuracy: 0.4267

## 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 |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|
| 1.1104        | 1.0   | 225   | 1.1064          | 0.345    |
| 1.1269        | 2.0   | 450   | 1.1051          | 0.355    |
| 1.1082        | 3.0   | 675   | 1.1039          | 0.3667   |
| 1.0954        | 4.0   | 900   | 1.1026          | 0.365    |
| 1.1069        | 5.0   | 1125  | 1.1015          | 0.36     |
| 1.1012        | 6.0   | 1350  | 1.1003          | 0.3667   |
| 1.1071        | 7.0   | 1575  | 1.0992          | 0.3733   |
| 1.1242        | 8.0   | 1800  | 1.0982          | 0.3767   |
| 1.086         | 9.0   | 2025  | 1.0971          | 0.3783   |
| 1.0866        | 10.0  | 2250  | 1.0961          | 0.3867   |
| 1.0948        | 11.0  | 2475  | 1.0952          | 0.3833   |
| 1.0863        | 12.0  | 2700  | 1.0942          | 0.385    |
| 1.0844        | 13.0  | 2925  | 1.0933          | 0.3833   |
| 1.0933        | 14.0  | 3150  | 1.0925          | 0.3867   |
| 1.0947        | 15.0  | 3375  | 1.0916          | 0.3917   |
| 1.084         | 16.0  | 3600  | 1.0908          | 0.39     |
| 1.0986        | 17.0  | 3825  | 1.0900          | 0.395    |
| 1.0824        | 18.0  | 4050  | 1.0893          | 0.3967   |
| 1.0832        | 19.0  | 4275  | 1.0886          | 0.395    |
| 1.0894        | 20.0  | 4500  | 1.0879          | 0.3967   |
| 1.0841        | 21.0  | 4725  | 1.0872          | 0.4      |
| 1.0872        | 22.0  | 4950  | 1.0865          | 0.405    |
| 1.0916        | 23.0  | 5175  | 1.0859          | 0.4117   |
| 1.0847        | 24.0  | 5400  | 1.0853          | 0.4117   |
| 1.0901        | 25.0  | 5625  | 1.0848          | 0.41     |
| 1.0732        | 26.0  | 5850  | 1.0842          | 0.41     |
| 1.0848        | 27.0  | 6075  | 1.0837          | 0.4133   |
| 1.0818        | 28.0  | 6300  | 1.0832          | 0.415    |
| 1.0774        | 29.0  | 6525  | 1.0828          | 0.415    |
| 1.0812        | 30.0  | 6750  | 1.0823          | 0.4183   |
| 1.0886        | 31.0  | 6975  | 1.0819          | 0.4183   |
| 1.0712        | 32.0  | 7200  | 1.0815          | 0.42     |
| 1.0744        | 33.0  | 7425  | 1.0812          | 0.42     |
| 1.0756        | 34.0  | 7650  | 1.0808          | 0.425    |
| 1.0664        | 35.0  | 7875  | 1.0805          | 0.4267   |
| 1.0977        | 36.0  | 8100  | 1.0802          | 0.4283   |
| 1.0683        | 37.0  | 8325  | 1.0799          | 0.4283   |
| 1.0735        | 38.0  | 8550  | 1.0797          | 0.4267   |
| 1.0832        | 39.0  | 8775  | 1.0795          | 0.4267   |
| 1.0815        | 40.0  | 9000  | 1.0793          | 0.4267   |
| 1.0823        | 41.0  | 9225  | 1.0791          | 0.425    |
| 1.0956        | 42.0  | 9450  | 1.0789          | 0.425    |
| 1.0851        | 43.0  | 9675  | 1.0788          | 0.425    |
| 1.0774        | 44.0  | 9900  | 1.0787          | 0.4267   |
| 1.0466        | 45.0  | 10125 | 1.0786          | 0.4267   |
| 1.0871        | 46.0  | 10350 | 1.0785          | 0.4267   |
| 1.0722        | 47.0  | 10575 | 1.0784          | 0.4267   |
| 1.069         | 48.0  | 10800 | 1.0784          | 0.4267   |
| 1.0654        | 49.0  | 11025 | 1.0784          | 0.4267   |
| 1.0659        | 50.0  | 11250 | 1.0784          | 0.4267   |


### 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