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# smids_3x_deit_tiny_sgd_0001_fold2

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: 0.6082
- Accuracy: 0.7554

## 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 |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|
| 1.1785        | 1.0   | 225   | 1.1903          | 0.3511   |
| 1.1205        | 2.0   | 450   | 1.1198          | 0.3760   |
| 1.092         | 3.0   | 675   | 1.0810          | 0.4309   |
| 1.0637        | 4.0   | 900   | 1.0502          | 0.4626   |
| 1.0626        | 5.0   | 1125  | 1.0222          | 0.4842   |
| 0.9785        | 6.0   | 1350  | 0.9974          | 0.5008   |
| 0.9914        | 7.0   | 1575  | 0.9736          | 0.5175   |
| 0.9757        | 8.0   | 1800  | 0.9516          | 0.5408   |
| 0.9275        | 9.0   | 2025  | 0.9302          | 0.5541   |
| 0.9237        | 10.0  | 2250  | 0.9088          | 0.5624   |
| 0.9248        | 11.0  | 2475  | 0.8885          | 0.5691   |
| 0.8852        | 12.0  | 2700  | 0.8691          | 0.5957   |
| 0.8693        | 13.0  | 2925  | 0.8509          | 0.6073   |
| 0.8044        | 14.0  | 3150  | 0.8334          | 0.6156   |
| 0.8135        | 15.0  | 3375  | 0.8176          | 0.6389   |
| 0.8455        | 16.0  | 3600  | 0.8028          | 0.6506   |
| 0.8081        | 17.0  | 3825  | 0.7887          | 0.6539   |
| 0.75          | 18.0  | 4050  | 0.7752          | 0.6622   |
| 0.762         | 19.0  | 4275  | 0.7624          | 0.6722   |
| 0.779         | 20.0  | 4500  | 0.7498          | 0.6772   |
| 0.7954        | 21.0  | 4725  | 0.7380          | 0.6822   |
| 0.7639        | 22.0  | 4950  | 0.7272          | 0.6938   |
| 0.7811        | 23.0  | 5175  | 0.7165          | 0.7022   |
| 0.7218        | 24.0  | 5400  | 0.7064          | 0.7055   |
| 0.725         | 25.0  | 5625  | 0.6973          | 0.7105   |
| 0.6921        | 26.0  | 5850  | 0.6889          | 0.7105   |
| 0.7457        | 27.0  | 6075  | 0.6809          | 0.7221   |
| 0.6572        | 28.0  | 6300  | 0.6736          | 0.7271   |
| 0.6717        | 29.0  | 6525  | 0.6667          | 0.7288   |
| 0.6421        | 30.0  | 6750  | 0.6605          | 0.7354   |
| 0.6376        | 31.0  | 6975  | 0.6545          | 0.7421   |
| 0.6251        | 32.0  | 7200  | 0.6490          | 0.7438   |
| 0.6178        | 33.0  | 7425  | 0.6441          | 0.7488   |
| 0.6335        | 34.0  | 7650  | 0.6394          | 0.7504   |
| 0.6522        | 35.0  | 7875  | 0.6351          | 0.7521   |
| 0.6157        | 36.0  | 8100  | 0.6314          | 0.7521   |
| 0.5791        | 37.0  | 8325  | 0.6279          | 0.7521   |
| 0.596         | 38.0  | 8550  | 0.6248          | 0.7521   |
| 0.5791        | 39.0  | 8775  | 0.6218          | 0.7554   |
| 0.6017        | 40.0  | 9000  | 0.6193          | 0.7554   |
| 0.5375        | 41.0  | 9225  | 0.6170          | 0.7571   |
| 0.6415        | 42.0  | 9450  | 0.6151          | 0.7571   |
| 0.5804        | 43.0  | 9675  | 0.6133          | 0.7571   |
| 0.5964        | 44.0  | 9900  | 0.6118          | 0.7571   |
| 0.5945        | 45.0  | 10125 | 0.6106          | 0.7554   |
| 0.6415        | 46.0  | 10350 | 0.6096          | 0.7554   |
| 0.5566        | 47.0  | 10575 | 0.6089          | 0.7554   |
| 0.5708        | 48.0  | 10800 | 0.6085          | 0.7554   |
| 0.6262        | 49.0  | 11025 | 0.6082          | 0.7554   |
| 0.5519        | 50.0  | 11250 | 0.6082          | 0.7554   |


### 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