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# smids_10x_deit_base_sgd_001_fold4

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: 0.3178
- Accuracy: 0.8733

## 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: 0.001
- 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 |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|
| 0.599         | 1.0   | 750   | 0.5727          | 0.805    |
| 0.4144        | 2.0   | 1500  | 0.4280          | 0.8367   |
| 0.3778        | 3.0   | 2250  | 0.3848          | 0.85     |
| 0.3651        | 4.0   | 3000  | 0.3623          | 0.8483   |
| 0.2724        | 5.0   | 3750  | 0.3497          | 0.8633   |
| 0.2557        | 6.0   | 4500  | 0.3423          | 0.865    |
| 0.2087        | 7.0   | 5250  | 0.3348          | 0.8617   |
| 0.2338        | 8.0   | 6000  | 0.3321          | 0.8667   |
| 0.2309        | 9.0   | 6750  | 0.3286          | 0.8683   |
| 0.222         | 10.0  | 7500  | 0.3223          | 0.8683   |
| 0.2535        | 11.0  | 8250  | 0.3209          | 0.8683   |
| 0.2943        | 12.0  | 9000  | 0.3182          | 0.865    |
| 0.2348        | 13.0  | 9750  | 0.3166          | 0.87     |
| 0.2428        | 14.0  | 10500 | 0.3176          | 0.865    |
| 0.1672        | 15.0  | 11250 | 0.3141          | 0.875    |
| 0.2041        | 16.0  | 12000 | 0.3122          | 0.8783   |
| 0.143         | 17.0  | 12750 | 0.3133          | 0.8717   |
| 0.1867        | 18.0  | 13500 | 0.3116          | 0.8767   |
| 0.2171        | 19.0  | 14250 | 0.3182          | 0.87     |
| 0.1733        | 20.0  | 15000 | 0.3144          | 0.8733   |
| 0.1721        | 21.0  | 15750 | 0.3128          | 0.875    |
| 0.1968        | 22.0  | 16500 | 0.3133          | 0.8717   |
| 0.1782        | 23.0  | 17250 | 0.3143          | 0.8717   |
| 0.2288        | 24.0  | 18000 | 0.3135          | 0.8717   |
| 0.1955        | 25.0  | 18750 | 0.3134          | 0.8733   |
| 0.1366        | 26.0  | 19500 | 0.3154          | 0.87     |
| 0.1576        | 27.0  | 20250 | 0.3131          | 0.8783   |
| 0.1394        | 28.0  | 21000 | 0.3139          | 0.8733   |
| 0.1334        | 29.0  | 21750 | 0.3147          | 0.8733   |
| 0.1696        | 30.0  | 22500 | 0.3135          | 0.8767   |
| 0.1131        | 31.0  | 23250 | 0.3147          | 0.8767   |
| 0.1384        | 32.0  | 24000 | 0.3141          | 0.8733   |
| 0.1516        | 33.0  | 24750 | 0.3150          | 0.875    |
| 0.19          | 34.0  | 25500 | 0.3153          | 0.8733   |
| 0.1122        | 35.0  | 26250 | 0.3150          | 0.8733   |
| 0.164         | 36.0  | 27000 | 0.3158          | 0.8717   |
| 0.1263        | 37.0  | 27750 | 0.3166          | 0.875    |
| 0.1818        | 38.0  | 28500 | 0.3156          | 0.8733   |
| 0.1588        | 39.0  | 29250 | 0.3163          | 0.8717   |
| 0.1761        | 40.0  | 30000 | 0.3172          | 0.8733   |
| 0.1577        | 41.0  | 30750 | 0.3157          | 0.8733   |
| 0.1328        | 42.0  | 31500 | 0.3172          | 0.875    |
| 0.1699        | 43.0  | 32250 | 0.3163          | 0.8733   |
| 0.1505        | 44.0  | 33000 | 0.3182          | 0.875    |
| 0.1595        | 45.0  | 33750 | 0.3176          | 0.8733   |
| 0.1562        | 46.0  | 34500 | 0.3182          | 0.8733   |
| 0.1053        | 47.0  | 35250 | 0.3176          | 0.875    |
| 0.1264        | 48.0  | 36000 | 0.3177          | 0.8733   |
| 0.1357        | 49.0  | 36750 | 0.3177          | 0.8733   |
| 0.0881        | 50.0  | 37500 | 0.3178          | 0.8733   |


### 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