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# smids_10x_deit_small_sgd_0001_fold3

This model is a fine-tuned version of [facebook/deit-small-patch16-224](https://huggingface.co/facebook/deit-small-patch16-224) on the imagefolder dataset.
It achieves the following results on the evaluation set:
- Loss: 0.3901
- Accuracy: 0.855

## 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 |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|
| 0.9824        | 1.0   | 750   | 1.0331          | 0.435    |
| 0.9063        | 2.0   | 1500  | 0.9735          | 0.5233   |
| 0.8503        | 3.0   | 2250  | 0.9109          | 0.59     |
| 0.7679        | 4.0   | 3000  | 0.8466          | 0.645    |
| 0.7248        | 5.0   | 3750  | 0.7860          | 0.69     |
| 0.6585        | 6.0   | 4500  | 0.7311          | 0.7167   |
| 0.6129        | 7.0   | 5250  | 0.6856          | 0.7283   |
| 0.6082        | 8.0   | 6000  | 0.6417          | 0.7617   |
| 0.581         | 9.0   | 6750  | 0.6068          | 0.7683   |
| 0.5231        | 10.0  | 7500  | 0.5777          | 0.7767   |
| 0.5113        | 11.0  | 8250  | 0.5554          | 0.7833   |
| 0.4834        | 12.0  | 9000  | 0.5347          | 0.8      |
| 0.5002        | 13.0  | 9750  | 0.5194          | 0.8067   |
| 0.5244        | 14.0  | 10500 | 0.5049          | 0.8117   |
| 0.478         | 15.0  | 11250 | 0.4926          | 0.8183   |
| 0.4573        | 16.0  | 12000 | 0.4823          | 0.8183   |
| 0.4332        | 17.0  | 12750 | 0.4737          | 0.8233   |
| 0.4552        | 18.0  | 13500 | 0.4642          | 0.8283   |
| 0.4717        | 19.0  | 14250 | 0.4573          | 0.8283   |
| 0.4284        | 20.0  | 15000 | 0.4511          | 0.8283   |
| 0.418         | 21.0  | 15750 | 0.4442          | 0.835    |
| 0.4355        | 22.0  | 16500 | 0.4394          | 0.8417   |
| 0.442         | 23.0  | 17250 | 0.4349          | 0.84     |
| 0.4592        | 24.0  | 18000 | 0.4307          | 0.845    |
| 0.4174        | 25.0  | 18750 | 0.4266          | 0.8483   |
| 0.4133        | 26.0  | 19500 | 0.4227          | 0.8483   |
| 0.3538        | 27.0  | 20250 | 0.4190          | 0.8517   |
| 0.4061        | 28.0  | 21000 | 0.4159          | 0.8533   |
| 0.4077        | 29.0  | 21750 | 0.4132          | 0.8517   |
| 0.4051        | 30.0  | 22500 | 0.4109          | 0.8533   |
| 0.3404        | 31.0  | 23250 | 0.4086          | 0.8517   |
| 0.353         | 32.0  | 24000 | 0.4061          | 0.855    |
| 0.3864        | 33.0  | 24750 | 0.4039          | 0.8567   |
| 0.3572        | 34.0  | 25500 | 0.4020          | 0.8567   |
| 0.3431        | 35.0  | 26250 | 0.4002          | 0.8567   |
| 0.3693        | 36.0  | 27000 | 0.3992          | 0.8567   |
| 0.3706        | 37.0  | 27750 | 0.3978          | 0.8567   |
| 0.423         | 38.0  | 28500 | 0.3964          | 0.855    |
| 0.3909        | 39.0  | 29250 | 0.3953          | 0.855    |
| 0.41          | 40.0  | 30000 | 0.3943          | 0.855    |
| 0.3387        | 41.0  | 30750 | 0.3933          | 0.855    |
| 0.3698        | 42.0  | 31500 | 0.3927          | 0.855    |
| 0.3644        | 43.0  | 32250 | 0.3919          | 0.855    |
| 0.3722        | 44.0  | 33000 | 0.3914          | 0.8567   |
| 0.3269        | 45.0  | 33750 | 0.3910          | 0.8567   |
| 0.3532        | 46.0  | 34500 | 0.3906          | 0.8567   |
| 0.3899        | 47.0  | 35250 | 0.3904          | 0.8567   |
| 0.3783        | 48.0  | 36000 | 0.3902          | 0.855    |
| 0.3767        | 49.0  | 36750 | 0.3901          | 0.855    |
| 0.3232        | 50.0  | 37500 | 0.3901          | 0.855    |


### 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