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# smids_10x_deit_base_sgd_0001_fold5

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: 0.3787
- Accuracy: 0.835

## 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 |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|
| 1.0636        | 1.0   | 750   | 1.0738          | 0.4167   |
| 0.9993        | 2.0   | 1500  | 1.0277          | 0.5117   |
| 0.9435        | 3.0   | 2250  | 0.9677          | 0.5817   |
| 0.8744        | 4.0   | 3000  | 0.8995          | 0.6383   |
| 0.8256        | 5.0   | 3750  | 0.8300          | 0.705    |
| 0.7543        | 6.0   | 4500  | 0.7676          | 0.73     |
| 0.673         | 7.0   | 5250  | 0.7129          | 0.76     |
| 0.6641        | 8.0   | 6000  | 0.6669          | 0.7717   |
| 0.6662        | 9.0   | 6750  | 0.6286          | 0.7817   |
| 0.612         | 10.0  | 7500  | 0.5968          | 0.79     |
| 0.5807        | 11.0  | 8250  | 0.5703          | 0.7933   |
| 0.5436        | 12.0  | 9000  | 0.5477          | 0.8      |
| 0.5472        | 13.0  | 9750  | 0.5286          | 0.805    |
| 0.5287        | 14.0  | 10500 | 0.5121          | 0.8083   |
| 0.4928        | 15.0  | 11250 | 0.4975          | 0.8083   |
| 0.4187        | 16.0  | 12000 | 0.4850          | 0.8133   |
| 0.4686        | 17.0  | 12750 | 0.4741          | 0.8167   |
| 0.4337        | 18.0  | 13500 | 0.4641          | 0.82     |
| 0.4835        | 19.0  | 14250 | 0.4556          | 0.8217   |
| 0.4689        | 20.0  | 15000 | 0.4478          | 0.825    |
| 0.4014        | 21.0  | 15750 | 0.4407          | 0.8233   |
| 0.4524        | 22.0  | 16500 | 0.4346          | 0.8233   |
| 0.4363        | 23.0  | 17250 | 0.4289          | 0.825    |
| 0.4366        | 24.0  | 18000 | 0.4239          | 0.825    |
| 0.419         | 25.0  | 18750 | 0.4192          | 0.8267   |
| 0.4041        | 26.0  | 19500 | 0.4151          | 0.8283   |
| 0.334         | 27.0  | 20250 | 0.4112          | 0.8317   |
| 0.3991        | 28.0  | 21000 | 0.4077          | 0.8317   |
| 0.4162        | 29.0  | 21750 | 0.4044          | 0.83     |
| 0.3862        | 30.0  | 22500 | 0.4016          | 0.8317   |
| 0.3788        | 31.0  | 23250 | 0.3990          | 0.8333   |
| 0.3692        | 32.0  | 24000 | 0.3965          | 0.8333   |
| 0.3919        | 33.0  | 24750 | 0.3944          | 0.8333   |
| 0.3436        | 34.0  | 25500 | 0.3923          | 0.8317   |
| 0.385         | 35.0  | 26250 | 0.3904          | 0.8317   |
| 0.4009        | 36.0  | 27000 | 0.3887          | 0.835    |
| 0.3069        | 37.0  | 27750 | 0.3872          | 0.835    |
| 0.3924        | 38.0  | 28500 | 0.3858          | 0.835    |
| 0.3366        | 39.0  | 29250 | 0.3846          | 0.835    |
| 0.3431        | 40.0  | 30000 | 0.3835          | 0.8333   |
| 0.3539        | 41.0  | 30750 | 0.3825          | 0.8333   |
| 0.3975        | 42.0  | 31500 | 0.3816          | 0.8333   |
| 0.3795        | 43.0  | 32250 | 0.3809          | 0.835    |
| 0.36          | 44.0  | 33000 | 0.3803          | 0.835    |
| 0.3923        | 45.0  | 33750 | 0.3798          | 0.835    |
| 0.3388        | 46.0  | 34500 | 0.3793          | 0.835    |
| 0.3751        | 47.0  | 35250 | 0.3790          | 0.835    |
| 0.3764        | 48.0  | 36000 | 0.3788          | 0.835    |
| 0.3502        | 49.0  | 36750 | 0.3787          | 0.835    |
| 0.28          | 50.0  | 37500 | 0.3787          | 0.835    |


### 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