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# smids_3x_deit_tiny_sgd_00001_fold4

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: 1.1127
- 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 |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|
| 1.3668        | 1.0   | 225   | 1.3432          | 0.3467   |
| 1.2646        | 2.0   | 450   | 1.3216          | 0.3467   |
| 1.3414        | 3.0   | 675   | 1.3018          | 0.3417   |
| 1.249         | 4.0   | 900   | 1.2839          | 0.3417   |
| 1.3057        | 5.0   | 1125  | 1.2676          | 0.345    |
| 1.2751        | 6.0   | 1350  | 1.2530          | 0.35     |
| 1.2676        | 7.0   | 1575  | 1.2399          | 0.3483   |
| 1.1849        | 8.0   | 1800  | 1.2280          | 0.355    |
| 1.2324        | 9.0   | 2025  | 1.2173          | 0.365    |
| 1.2387        | 10.0  | 2250  | 1.2077          | 0.365    |
| 1.1505        | 11.0  | 2475  | 1.1990          | 0.3767   |
| 1.2698        | 12.0  | 2700  | 1.1911          | 0.37     |
| 1.1755        | 13.0  | 2925  | 1.1841          | 0.3717   |
| 1.1872        | 14.0  | 3150  | 1.1777          | 0.3717   |
| 1.2051        | 15.0  | 3375  | 1.1720          | 0.3683   |
| 1.1902        | 16.0  | 3600  | 1.1669          | 0.3733   |
| 1.144         | 17.0  | 3825  | 1.1622          | 0.38     |
| 1.0991        | 18.0  | 4050  | 1.1579          | 0.375    |
| 1.1454        | 19.0  | 4275  | 1.1539          | 0.38     |
| 1.1739        | 20.0  | 4500  | 1.1503          | 0.3717   |
| 1.193         | 21.0  | 4725  | 1.1470          | 0.375    |
| 1.154         | 22.0  | 4950  | 1.1439          | 0.3783   |
| 1.1465        | 23.0  | 5175  | 1.1410          | 0.3733   |
| 1.1397        | 24.0  | 5400  | 1.1384          | 0.3733   |
| 1.1433        | 25.0  | 5625  | 1.1360          | 0.375    |
| 1.1492        | 26.0  | 5850  | 1.1337          | 0.3717   |
| 1.1616        | 27.0  | 6075  | 1.1316          | 0.37     |
| 1.137         | 28.0  | 6300  | 1.1297          | 0.3767   |
| 1.1705        | 29.0  | 6525  | 1.1279          | 0.3817   |
| 1.1514        | 30.0  | 6750  | 1.1262          | 0.3817   |
| 1.1348        | 31.0  | 6975  | 1.1247          | 0.385    |
| 1.152         | 32.0  | 7200  | 1.1232          | 0.3817   |
| 1.1108        | 33.0  | 7425  | 1.1219          | 0.385    |
| 1.1032        | 34.0  | 7650  | 1.1207          | 0.3867   |
| 1.0973        | 35.0  | 7875  | 1.1195          | 0.3833   |
| 1.1492        | 36.0  | 8100  | 1.1185          | 0.3867   |
| 1.1046        | 37.0  | 8325  | 1.1176          | 0.3883   |
| 1.1281        | 38.0  | 8550  | 1.1167          | 0.3883   |
| 1.0952        | 39.0  | 8775  | 1.1160          | 0.3917   |
| 1.0675        | 40.0  | 9000  | 1.1153          | 0.3883   |
| 1.1354        | 41.0  | 9225  | 1.1147          | 0.39     |
| 1.1352        | 42.0  | 9450  | 1.1142          | 0.3883   |
| 1.1158        | 43.0  | 9675  | 1.1138          | 0.39     |
| 1.1146        | 44.0  | 9900  | 1.1135          | 0.39     |
| 1.0831        | 45.0  | 10125 | 1.1132          | 0.39     |
| 1.1243        | 46.0  | 10350 | 1.1130          | 0.39     |
| 1.0703        | 47.0  | 10575 | 1.1128          | 0.3917   |
| 1.1356        | 48.0  | 10800 | 1.1127          | 0.3917   |
| 1.1249        | 49.0  | 11025 | 1.1127          | 0.3917   |
| 1.0942        | 50.0  | 11250 | 1.1127          | 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