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# smids_3x_deit_tiny_sgd_00001_fold5

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: 1.1096
- Accuracy: 0.3917

## 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 |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|
| 1.3197        | 1.0   | 225   | 1.3390          | 0.3517   |
| 1.27          | 2.0   | 450   | 1.3176          | 0.3517   |
| 1.3368        | 3.0   | 675   | 1.2980          | 0.36     |
| 1.2821        | 4.0   | 900   | 1.2802          | 0.3583   |
| 1.2856        | 5.0   | 1125  | 1.2641          | 0.3633   |
| 1.2974        | 6.0   | 1350  | 1.2496          | 0.3633   |
| 1.2807        | 7.0   | 1575  | 1.2366          | 0.3733   |
| 1.2428        | 8.0   | 1800  | 1.2247          | 0.3717   |
| 1.2255        | 9.0   | 2025  | 1.2140          | 0.375    |
| 1.2238        | 10.0  | 2250  | 1.2044          | 0.3717   |
| 1.1757        | 11.0  | 2475  | 1.1957          | 0.3733   |
| 1.2719        | 12.0  | 2700  | 1.1877          | 0.38     |
| 1.2029        | 13.0  | 2925  | 1.1807          | 0.38     |
| 1.2184        | 14.0  | 3150  | 1.1743          | 0.3733   |
| 1.2016        | 15.0  | 3375  | 1.1685          | 0.3717   |
| 1.1832        | 16.0  | 3600  | 1.1634          | 0.385    |
| 1.1841        | 17.0  | 3825  | 1.1587          | 0.3883   |
| 1.1122        | 18.0  | 4050  | 1.1544          | 0.395    |
| 1.1798        | 19.0  | 4275  | 1.1504          | 0.3917   |
| 1.1614        | 20.0  | 4500  | 1.1469          | 0.4      |
| 1.1973        | 21.0  | 4725  | 1.1435          | 0.4017   |
| 1.1713        | 22.0  | 4950  | 1.1404          | 0.4083   |
| 1.1334        | 23.0  | 5175  | 1.1376          | 0.4067   |
| 1.1477        | 24.0  | 5400  | 1.1349          | 0.4083   |
| 1.126         | 25.0  | 5625  | 1.1325          | 0.4067   |
| 1.1346        | 26.0  | 5850  | 1.1303          | 0.3983   |
| 1.111         | 27.0  | 6075  | 1.1282          | 0.4      |
| 1.0942        | 28.0  | 6300  | 1.1263          | 0.4067   |
| 1.1712        | 29.0  | 6525  | 1.1245          | 0.405    |
| 1.1249        | 30.0  | 6750  | 1.1229          | 0.4083   |
| 1.13          | 31.0  | 6975  | 1.1213          | 0.4067   |
| 1.1043        | 32.0  | 7200  | 1.1199          | 0.405    |
| 1.1162        | 33.0  | 7425  | 1.1186          | 0.4033   |
| 1.1041        | 34.0  | 7650  | 1.1174          | 0.4017   |
| 1.131         | 35.0  | 7875  | 1.1163          | 0.3983   |
| 1.1255        | 36.0  | 8100  | 1.1153          | 0.3983   |
| 1.0951        | 37.0  | 8325  | 1.1144          | 0.3983   |
| 1.1402        | 38.0  | 8550  | 1.1135          | 0.3967   |
| 1.0931        | 39.0  | 8775  | 1.1128          | 0.3967   |
| 1.1012        | 40.0  | 9000  | 1.1122          | 0.3967   |
| 1.1339        | 41.0  | 9225  | 1.1116          | 0.395    |
| 1.1212        | 42.0  | 9450  | 1.1111          | 0.395    |
| 1.1447        | 43.0  | 9675  | 1.1107          | 0.395    |
| 1.1467        | 44.0  | 9900  | 1.1104          | 0.395    |
| 1.1224        | 45.0  | 10125 | 1.1101          | 0.3917   |
| 1.104         | 46.0  | 10350 | 1.1099          | 0.3917   |
| 1.0801        | 47.0  | 10575 | 1.1098          | 0.39     |
| 1.1097        | 48.0  | 10800 | 1.1097          | 0.3917   |
| 1.0996        | 49.0  | 11025 | 1.1096          | 0.3917   |
| 1.1198        | 50.0  | 11250 | 1.1096          | 0.3917   |


### 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