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# smids_3x_deit_small_sgd_0001_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: 0.6009
- Accuracy: 0.7717

## 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 |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|
| 1.0412        | 1.0   | 225   | 1.0610          | 0.4117   |
| 1.0073        | 2.0   | 450   | 1.0359          | 0.4517   |
| 0.9921        | 3.0   | 675   | 1.0120          | 0.4917   |
| 0.9585        | 4.0   | 900   | 0.9890          | 0.515    |
| 0.951         | 5.0   | 1125  | 0.9674          | 0.5517   |
| 0.9167        | 6.0   | 1350  | 0.9465          | 0.5667   |
| 0.9008        | 7.0   | 1575  | 0.9258          | 0.58     |
| 0.9034        | 8.0   | 1800  | 0.9058          | 0.605    |
| 0.8748        | 9.0   | 2025  | 0.8869          | 0.6283   |
| 0.8544        | 10.0  | 2250  | 0.8687          | 0.64     |
| 0.8432        | 11.0  | 2475  | 0.8520          | 0.65     |
| 0.8244        | 12.0  | 2700  | 0.8358          | 0.66     |
| 0.8203        | 13.0  | 2925  | 0.8201          | 0.665    |
| 0.7835        | 14.0  | 3150  | 0.8053          | 0.6733   |
| 0.7537        | 15.0  | 3375  | 0.7910          | 0.68     |
| 0.7828        | 16.0  | 3600  | 0.7779          | 0.6983   |
| 0.7529        | 17.0  | 3825  | 0.7656          | 0.7067   |
| 0.7496        | 18.0  | 4050  | 0.7534          | 0.7167   |
| 0.6935        | 19.0  | 4275  | 0.7418          | 0.7283   |
| 0.7161        | 20.0  | 4500  | 0.7310          | 0.7283   |
| 0.6651        | 21.0  | 4725  | 0.7207          | 0.7283   |
| 0.7137        | 22.0  | 4950  | 0.7112          | 0.7333   |
| 0.6691        | 23.0  | 5175  | 0.7018          | 0.7367   |
| 0.6667        | 24.0  | 5400  | 0.6932          | 0.7383   |
| 0.6381        | 25.0  | 5625  | 0.6850          | 0.735    |
| 0.636         | 26.0  | 5850  | 0.6772          | 0.74     |
| 0.6231        | 27.0  | 6075  | 0.6700          | 0.7467   |
| 0.6306        | 28.0  | 6300  | 0.6632          | 0.7517   |
| 0.6547        | 29.0  | 6525  | 0.6569          | 0.7583   |
| 0.5884        | 30.0  | 6750  | 0.6510          | 0.7583   |
| 0.5952        | 31.0  | 6975  | 0.6455          | 0.7583   |
| 0.6201        | 32.0  | 7200  | 0.6404          | 0.7633   |
| 0.6515        | 33.0  | 7425  | 0.6357          | 0.765    |
| 0.6177        | 34.0  | 7650  | 0.6313          | 0.765    |
| 0.5987        | 35.0  | 7875  | 0.6273          | 0.765    |
| 0.6037        | 36.0  | 8100  | 0.6236          | 0.7633   |
| 0.571         | 37.0  | 8325  | 0.6202          | 0.765    |
| 0.5948        | 38.0  | 8550  | 0.6172          | 0.7667   |
| 0.6088        | 39.0  | 8775  | 0.6144          | 0.765    |
| 0.5862        | 40.0  | 9000  | 0.6119          | 0.77     |
| 0.5659        | 41.0  | 9225  | 0.6097          | 0.7733   |
| 0.6112        | 42.0  | 9450  | 0.6077          | 0.7733   |
| 0.5562        | 43.0  | 9675  | 0.6060          | 0.775    |
| 0.6002        | 44.0  | 9900  | 0.6046          | 0.7733   |
| 0.6408        | 45.0  | 10125 | 0.6034          | 0.7717   |
| 0.6068        | 46.0  | 10350 | 0.6024          | 0.7717   |
| 0.5514        | 47.0  | 10575 | 0.6017          | 0.7717   |
| 0.6016        | 48.0  | 10800 | 0.6013          | 0.7717   |
| 0.6117        | 49.0  | 11025 | 0.6010          | 0.7717   |
| 0.5649        | 50.0  | 11250 | 0.6009          | 0.7717   |


### 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