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# smids_5x_deit_base_sgd_00001_fold3

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: 1.0622
- Accuracy: 0.4483

## 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 |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|
| 1.1145        | 1.0   | 375   | 1.1057          | 0.3517   |
| 1.1046        | 2.0   | 750   | 1.1036          | 0.365    |
| 1.1155        | 3.0   | 1125  | 1.1017          | 0.3617   |
| 1.0962        | 4.0   | 1500  | 1.0998          | 0.3733   |
| 1.1039        | 5.0   | 1875  | 1.0980          | 0.375    |
| 1.1203        | 6.0   | 2250  | 1.0963          | 0.3833   |
| 1.072         | 7.0   | 2625  | 1.0946          | 0.3833   |
| 1.0855        | 8.0   | 3000  | 1.0930          | 0.3883   |
| 1.0787        | 9.0   | 3375  | 1.0914          | 0.395    |
| 1.1082        | 10.0  | 3750  | 1.0899          | 0.3933   |
| 1.0738        | 11.0  | 4125  | 1.0885          | 0.4033   |
| 1.0788        | 12.0  | 4500  | 1.0870          | 0.4067   |
| 1.0872        | 13.0  | 4875  | 1.0857          | 0.415    |
| 1.0927        | 14.0  | 5250  | 1.0844          | 0.4133   |
| 1.0769        | 15.0  | 5625  | 1.0831          | 0.4167   |
| 1.085         | 16.0  | 6000  | 1.0819          | 0.4217   |
| 1.0634        | 17.0  | 6375  | 1.0806          | 0.42     |
| 1.0703        | 18.0  | 6750  | 1.0795          | 0.42     |
| 1.0536        | 19.0  | 7125  | 1.0783          | 0.4233   |
| 1.0852        | 20.0  | 7500  | 1.0773          | 0.4267   |
| 1.076         | 21.0  | 7875  | 1.0762          | 0.43     |
| 1.0765        | 22.0  | 8250  | 1.0752          | 0.4333   |
| 1.0647        | 23.0  | 8625  | 1.0743          | 0.435    |
| 1.061         | 24.0  | 9000  | 1.0734          | 0.4367   |
| 1.0648        | 25.0  | 9375  | 1.0725          | 0.445    |
| 1.0459        | 26.0  | 9750  | 1.0716          | 0.4417   |
| 1.0405        | 27.0  | 10125 | 1.0708          | 0.4417   |
| 1.062         | 28.0  | 10500 | 1.0700          | 0.4433   |
| 1.0508        | 29.0  | 10875 | 1.0693          | 0.445    |
| 1.06          | 30.0  | 11250 | 1.0686          | 0.445    |
| 1.062         | 31.0  | 11625 | 1.0679          | 0.4433   |
| 1.0622        | 32.0  | 12000 | 1.0673          | 0.4417   |
| 1.056         | 33.0  | 12375 | 1.0667          | 0.44     |
| 1.0406        | 34.0  | 12750 | 1.0662          | 0.4417   |
| 1.0456        | 35.0  | 13125 | 1.0657          | 0.4433   |
| 1.0409        | 36.0  | 13500 | 1.0652          | 0.4483   |
| 1.0536        | 37.0  | 13875 | 1.0648          | 0.4483   |
| 1.0481        | 38.0  | 14250 | 1.0643          | 0.445    |
| 1.0612        | 39.0  | 14625 | 1.0640          | 0.445    |
| 1.0364        | 40.0  | 15000 | 1.0637          | 0.45     |
| 1.0455        | 41.0  | 15375 | 1.0634          | 0.45     |
| 1.0602        | 42.0  | 15750 | 1.0631          | 0.45     |
| 1.0611        | 43.0  | 16125 | 1.0629          | 0.45     |
| 1.0352        | 44.0  | 16500 | 1.0627          | 0.45     |
| 1.0491        | 45.0  | 16875 | 1.0625          | 0.45     |
| 1.0436        | 46.0  | 17250 | 1.0624          | 0.45     |
| 1.0657        | 47.0  | 17625 | 1.0623          | 0.45     |
| 1.0594        | 48.0  | 18000 | 1.0622          | 0.4483   |
| 1.0541        | 49.0  | 18375 | 1.0622          | 0.4483   |
| 1.0444        | 50.0  | 18750 | 1.0622          | 0.4483   |


### 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