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# smids_5x_deit_small_sgd_00001_fold1

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: 0.9977
- Accuracy: 0.5159

## 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 |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|
| 1.0592        | 1.0   | 376   | 1.0737          | 0.4391   |
| 1.0782        | 2.0   | 752   | 1.0706          | 0.4374   |
| 1.0408        | 3.0   | 1128  | 1.0676          | 0.4391   |
| 1.0826        | 4.0   | 1504  | 1.0646          | 0.4441   |
| 1.0586        | 5.0   | 1880  | 1.0616          | 0.4407   |
| 1.0333        | 6.0   | 2256  | 1.0588          | 0.4424   |
| 1.0668        | 7.0   | 2632  | 1.0559          | 0.4424   |
| 1.0617        | 8.0   | 3008  | 1.0531          | 0.4441   |
| 1.0464        | 9.0   | 3384  | 1.0504          | 0.4457   |
| 1.0296        | 10.0  | 3760  | 1.0477          | 0.4474   |
| 1.0219        | 11.0  | 4136  | 1.0452          | 0.4524   |
| 1.036         | 12.0  | 4512  | 1.0426          | 0.4558   |
| 1.0086        | 13.0  | 4888  | 1.0402          | 0.4591   |
| 1.0374        | 14.0  | 5264  | 1.0378          | 0.4591   |
| 1.0308        | 15.0  | 5640  | 1.0354          | 0.4624   |
| 1.0138        | 16.0  | 6016  | 1.0332          | 0.4641   |
| 1.039         | 17.0  | 6392  | 1.0310          | 0.4674   |
| 1.0251        | 18.0  | 6768  | 1.0289          | 0.4691   |
| 1.0132        | 19.0  | 7144  | 1.0268          | 0.4674   |
| 1.0078        | 20.0  | 7520  | 1.0248          | 0.4674   |
| 1.0073        | 21.0  | 7896  | 1.0229          | 0.4741   |
| 0.9973        | 22.0  | 8272  | 1.0210          | 0.4775   |
| 0.9979        | 23.0  | 8648  | 1.0192          | 0.4791   |
| 0.9943        | 24.0  | 9024  | 1.0175          | 0.4791   |
| 0.9653        | 25.0  | 9400  | 1.0159          | 0.4841   |
| 0.9982        | 26.0  | 9776  | 1.0143          | 0.4841   |
| 1.0041        | 27.0  | 10152 | 1.0128          | 0.4875   |
| 1.0054        | 28.0  | 10528 | 1.0114          | 0.4908   |
| 0.9643        | 29.0  | 10904 | 1.0101          | 0.4925   |
| 0.9735        | 30.0  | 11280 | 1.0088          | 0.4958   |
| 1.0           | 31.0  | 11656 | 1.0076          | 0.4958   |
| 0.998         | 32.0  | 12032 | 1.0064          | 0.4975   |
| 0.9763        | 33.0  | 12408 | 1.0054          | 0.4975   |
| 0.9704        | 34.0  | 12784 | 1.0044          | 0.4992   |
| 0.9948        | 35.0  | 13160 | 1.0035          | 0.5008   |
| 0.9708        | 36.0  | 13536 | 1.0026          | 0.5008   |
| 0.9711        | 37.0  | 13912 | 1.0019          | 0.5025   |
| 0.999         | 38.0  | 14288 | 1.0012          | 0.5042   |
| 0.9534        | 39.0  | 14664 | 1.0005          | 0.5042   |
| 0.9776        | 40.0  | 15040 | 1.0000          | 0.5058   |
| 1.0022        | 41.0  | 15416 | 0.9995          | 0.5058   |
| 0.9618        | 42.0  | 15792 | 0.9991          | 0.5058   |
| 0.9978        | 43.0  | 16168 | 0.9987          | 0.5109   |
| 0.9845        | 44.0  | 16544 | 0.9984          | 0.5142   |
| 0.9783        | 45.0  | 16920 | 0.9982          | 0.5159   |
| 0.99          | 46.0  | 17296 | 0.9980          | 0.5159   |
| 0.9708        | 47.0  | 17672 | 0.9979          | 0.5159   |
| 1.0004        | 48.0  | 18048 | 0.9978          | 0.5159   |
| 0.9871        | 49.0  | 18424 | 0.9977          | 0.5159   |
| 0.9947        | 50.0  | 18800 | 0.9977          | 0.5159   |


### 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