
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->

# smids_10x_deit_base_sgd_00001_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: 1.0147
- Accuracy: 0.53

## 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 |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|
| 1.1118        | 1.0   | 750   | 1.1081          | 0.3483   |
| 1.0989        | 2.0   | 1500  | 1.1045          | 0.3467   |
| 1.098         | 3.0   | 2250  | 1.1011          | 0.3483   |
| 1.0956        | 4.0   | 3000  | 1.0977          | 0.35     |
| 1.0908        | 5.0   | 3750  | 1.0945          | 0.3533   |
| 1.0728        | 6.0   | 4500  | 1.0914          | 0.36     |
| 1.0863        | 7.0   | 5250  | 1.0883          | 0.3783   |
| 1.0893        | 8.0   | 6000  | 1.0853          | 0.3833   |
| 1.0669        | 9.0   | 6750  | 1.0824          | 0.3883   |
| 1.0847        | 10.0  | 7500  | 1.0795          | 0.3933   |
| 1.062         | 11.0  | 8250  | 1.0767          | 0.4033   |
| 1.0612        | 12.0  | 9000  | 1.0739          | 0.4183   |
| 1.0546        | 13.0  | 9750  | 1.0712          | 0.4267   |
| 1.0486        | 14.0  | 10500 | 1.0685          | 0.43     |
| 1.0488        | 15.0  | 11250 | 1.0659          | 0.4367   |
| 1.0504        | 16.0  | 12000 | 1.0633          | 0.4367   |
| 1.0424        | 17.0  | 12750 | 1.0607          | 0.4417   |
| 1.0426        | 18.0  | 13500 | 1.0582          | 0.4533   |
| 1.0273        | 19.0  | 14250 | 1.0557          | 0.4533   |
| 1.0365        | 20.0  | 15000 | 1.0533          | 0.4633   |
| 1.0042        | 21.0  | 15750 | 1.0509          | 0.4683   |
| 1.0296        | 22.0  | 16500 | 1.0486          | 0.4717   |
| 1.0417        | 23.0  | 17250 | 1.0462          | 0.475    |
| 1.0189        | 24.0  | 18000 | 1.0440          | 0.4817   |
| 1.0029        | 25.0  | 18750 | 1.0418          | 0.4833   |
| 1.0163        | 26.0  | 19500 | 1.0396          | 0.4883   |
| 1.0097        | 27.0  | 20250 | 1.0376          | 0.4867   |
| 1.0284        | 28.0  | 21000 | 1.0356          | 0.49     |
| 1.0024        | 29.0  | 21750 | 1.0337          | 0.4967   |
| 1.0018        | 30.0  | 22500 | 1.0318          | 0.505    |
| 1.0059        | 31.0  | 23250 | 1.0301          | 0.5083   |
| 1.0064        | 32.0  | 24000 | 1.0284          | 0.5133   |
| 1.0048        | 33.0  | 24750 | 1.0269          | 0.5133   |
| 0.9972        | 34.0  | 25500 | 1.0254          | 0.5217   |
| 0.9969        | 35.0  | 26250 | 1.0240          | 0.5217   |
| 1.0105        | 36.0  | 27000 | 1.0228          | 0.5217   |
| 0.9858        | 37.0  | 27750 | 1.0216          | 0.525    |
| 1.0046        | 38.0  | 28500 | 1.0205          | 0.525    |
| 0.9725        | 39.0  | 29250 | 1.0195          | 0.5267   |
| 0.9836        | 40.0  | 30000 | 1.0186          | 0.5267   |
| 0.9756        | 41.0  | 30750 | 1.0178          | 0.5283   |
| 0.9954        | 42.0  | 31500 | 1.0171          | 0.5283   |
| 1.0089        | 43.0  | 32250 | 1.0165          | 0.5283   |
| 0.9937        | 44.0  | 33000 | 1.0160          | 0.5267   |
| 0.9919        | 45.0  | 33750 | 1.0156          | 0.53     |
| 0.9943        | 46.0  | 34500 | 1.0153          | 0.53     |
| 0.9683        | 47.0  | 35250 | 1.0150          | 0.53     |
| 0.9833        | 48.0  | 36000 | 1.0148          | 0.53     |
| 0.9815        | 49.0  | 36750 | 1.0147          | 0.53     |
| 0.9749        | 50.0  | 37500 | 1.0147          | 0.53     |


### 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