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# smids_10x_deit_tiny_adamax_001_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: 1.5843
- Accuracy: 0.8717

## 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 |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|
| 0.3385        | 1.0   | 750   | 0.3848          | 0.84     |
| 0.2692        | 2.0   | 1500  | 0.3830          | 0.8633   |
| 0.2345        | 3.0   | 2250  | 0.4255          | 0.8617   |
| 0.1851        | 4.0   | 3000  | 0.4988          | 0.8517   |
| 0.1806        | 5.0   | 3750  | 0.5032          | 0.8433   |
| 0.1568        | 6.0   | 4500  | 0.5429          | 0.8633   |
| 0.0638        | 7.0   | 5250  | 0.6033          | 0.855    |
| 0.1397        | 8.0   | 6000  | 0.6990          | 0.845    |
| 0.1208        | 9.0   | 6750  | 0.6852          | 0.8483   |
| 0.0667        | 10.0  | 7500  | 0.8743          | 0.8383   |
| 0.0482        | 11.0  | 8250  | 0.7516          | 0.8667   |
| 0.0306        | 12.0  | 9000  | 0.8187          | 0.8783   |
| 0.0125        | 13.0  | 9750  | 0.8525          | 0.86     |
| 0.0512        | 14.0  | 10500 | 1.0441          | 0.8483   |
| 0.0023        | 15.0  | 11250 | 1.0562          | 0.85     |
| 0.0353        | 16.0  | 12000 | 1.1914          | 0.8583   |
| 0.0637        | 17.0  | 12750 | 1.1115          | 0.8667   |
| 0.025         | 18.0  | 13500 | 1.1677          | 0.865    |
| 0.0126        | 19.0  | 14250 | 1.0523          | 0.8833   |
| 0.0           | 20.0  | 15000 | 1.0935          | 0.8633   |
| 0.0359        | 21.0  | 15750 | 1.1791          | 0.8733   |
| 0.0003        | 22.0  | 16500 | 1.0630          | 0.87     |
| 0.0003        | 23.0  | 17250 | 1.0996          | 0.8667   |
| 0.0006        | 24.0  | 18000 | 1.0915          | 0.8817   |
| 0.0001        | 25.0  | 18750 | 1.1484          | 0.8617   |
| 0.0           | 26.0  | 19500 | 1.1656          | 0.875    |
| 0.0179        | 27.0  | 20250 | 1.2101          | 0.8717   |
| 0.0           | 28.0  | 21000 | 1.3179          | 0.86     |
| 0.0           | 29.0  | 21750 | 1.2425          | 0.8733   |
| 0.0           | 30.0  | 22500 | 1.3660          | 0.87     |
| 0.0           | 31.0  | 23250 | 1.3781          | 0.87     |
| 0.0           | 32.0  | 24000 | 1.4541          | 0.86     |
| 0.0003        | 33.0  | 24750 | 1.3447          | 0.8717   |
| 0.0           | 34.0  | 25500 | 1.3846          | 0.8633   |
| 0.0           | 35.0  | 26250 | 1.3907          | 0.8733   |
| 0.0           | 36.0  | 27000 | 1.4240          | 0.87     |
| 0.0           | 37.0  | 27750 | 1.3878          | 0.8717   |
| 0.0           | 38.0  | 28500 | 1.4082          | 0.87     |
| 0.0           | 39.0  | 29250 | 1.4530          | 0.8717   |
| 0.0           | 40.0  | 30000 | 1.4653          | 0.8717   |
| 0.0           | 41.0  | 30750 | 1.4878          | 0.87     |
| 0.0           | 42.0  | 31500 | 1.5011          | 0.8717   |
| 0.0           | 43.0  | 32250 | 1.5107          | 0.8717   |
| 0.0           | 44.0  | 33000 | 1.5209          | 0.8717   |
| 0.0           | 45.0  | 33750 | 1.5429          | 0.8717   |
| 0.0           | 46.0  | 34500 | 1.5577          | 0.8717   |
| 0.0           | 47.0  | 35250 | 1.5684          | 0.8717   |
| 0.0           | 48.0  | 36000 | 1.5772          | 0.8717   |
| 0.0           | 49.0  | 36750 | 1.5824          | 0.8717   |
| 0.0           | 50.0  | 37500 | 1.5843          | 0.8717   |


### 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