
<!-- 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_5x_deit_tiny_sgd_00001_fold5

This model is a fine-tuned version of [facebook/deit-tiny-patch16-224](https://huggingface.co/facebook/deit-tiny-patch16-224) on the imagefolder dataset.
It achieves the following results on the evaluation set:
- Loss: 1.0692
- Accuracy: 0.45

## 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 |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|
| 1.4013        | 1.0   | 375   | 1.3267          | 0.3533   |
| 1.3461        | 2.0   | 750   | 1.2947          | 0.36     |
| 1.2972        | 3.0   | 1125  | 1.2670          | 0.3617   |
| 1.31          | 4.0   | 1500  | 1.2433          | 0.3683   |
| 1.2221        | 5.0   | 1875  | 1.2236          | 0.3717   |
| 1.2656        | 6.0   | 2250  | 1.2067          | 0.375    |
| 1.2312        | 7.0   | 2625  | 1.1923          | 0.3817   |
| 1.1861        | 8.0   | 3000  | 1.1803          | 0.3817   |
| 1.1289        | 9.0   | 3375  | 1.1699          | 0.385    |
| 1.218         | 10.0  | 3750  | 1.1612          | 0.3867   |
| 1.1921        | 11.0  | 4125  | 1.1535          | 0.3983   |
| 1.1315        | 12.0  | 4500  | 1.1468          | 0.405    |
| 1.1732        | 13.0  | 4875  | 1.1407          | 0.4133   |
| 1.1412        | 14.0  | 5250  | 1.1354          | 0.41     |
| 1.1502        | 15.0  | 5625  | 1.1305          | 0.4133   |
| 1.126         | 16.0  | 6000  | 1.1259          | 0.4133   |
| 1.1098        | 17.0  | 6375  | 1.1217          | 0.4117   |
| 1.1197        | 18.0  | 6750  | 1.1177          | 0.4083   |
| 1.1329        | 19.0  | 7125  | 1.1140          | 0.4083   |
| 1.0741        | 20.0  | 7500  | 1.1105          | 0.4117   |
| 1.0617        | 21.0  | 7875  | 1.1072          | 0.4117   |
| 1.0917        | 22.0  | 8250  | 1.1041          | 0.41     |
| 1.0822        | 23.0  | 8625  | 1.1011          | 0.41     |
| 1.1336        | 24.0  | 9000  | 1.0984          | 0.4167   |
| 1.0665        | 25.0  | 9375  | 1.0958          | 0.415    |
| 1.1097        | 26.0  | 9750  | 1.0934          | 0.415    |
| 1.0499        | 27.0  | 10125 | 1.0911          | 0.4183   |
| 1.1202        | 28.0  | 10500 | 1.0889          | 0.4167   |
| 1.1038        | 29.0  | 10875 | 1.0869          | 0.4283   |
| 1.0838        | 30.0  | 11250 | 1.0850          | 0.4333   |
| 1.0717        | 31.0  | 11625 | 1.0832          | 0.4367   |
| 1.0773        | 32.0  | 12000 | 1.0816          | 0.4383   |
| 1.0858        | 33.0  | 12375 | 1.0800          | 0.44     |
| 1.0072        | 34.0  | 12750 | 1.0786          | 0.44     |
| 1.0435        | 35.0  | 13125 | 1.0773          | 0.4417   |
| 1.047         | 36.0  | 13500 | 1.0761          | 0.4433   |
| 1.0361        | 37.0  | 13875 | 1.0750          | 0.4483   |
| 1.0477        | 38.0  | 14250 | 1.0740          | 0.45     |
| 1.0658        | 39.0  | 14625 | 1.0731          | 0.4483   |
| 1.0711        | 40.0  | 15000 | 1.0723          | 0.445    |
| 1.0473        | 41.0  | 15375 | 1.0716          | 0.445    |
| 1.0521        | 42.0  | 15750 | 1.0711          | 0.445    |
| 1.0368        | 43.0  | 16125 | 1.0705          | 0.4467   |
| 1.0636        | 44.0  | 16500 | 1.0701          | 0.4483   |
| 1.0424        | 45.0  | 16875 | 1.0698          | 0.4483   |
| 1.0442        | 46.0  | 17250 | 1.0695          | 0.45     |
| 1.0667        | 47.0  | 17625 | 1.0694          | 0.45     |
| 1.0523        | 48.0  | 18000 | 1.0693          | 0.45     |
| 1.0135        | 49.0  | 18375 | 1.0692          | 0.45     |
| 1.0393        | 50.0  | 18750 | 1.0692          | 0.45     |


### Framework versions

- Transformers 4.32.1
- Pytorch 2.1.1+cu121
- Datasets 2.12.0
- Tokenizers 0.13.2

abnormal_sperm non-sperm normal_sperm