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# smids_10x_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: 0.9599
- Accuracy: 0.5233

## 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 |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|
| 1.3812        | 1.0   | 750   | 1.2940          | 0.36     |
| 1.3175        | 2.0   | 1500  | 1.2389          | 0.3683   |
| 1.2691        | 3.0   | 2250  | 1.1984          | 0.3733   |
| 1.221         | 4.0   | 3000  | 1.1690          | 0.385    |
| 1.1712        | 5.0   | 3750  | 1.1479          | 0.3967   |
| 1.1485        | 6.0   | 4500  | 1.1321          | 0.4067   |
| 1.0967        | 7.0   | 5250  | 1.1194          | 0.3917   |
| 1.0947        | 8.0   | 6000  | 1.1088          | 0.4017   |
| 1.1331        | 9.0   | 6750  | 1.0995          | 0.405    |
| 1.0758        | 10.0  | 7500  | 1.0911          | 0.4167   |
| 1.0859        | 11.0  | 8250  | 1.0832          | 0.4117   |
| 1.074         | 12.0  | 9000  | 1.0758          | 0.4217   |
| 1.0354        | 13.0  | 9750  | 1.0688          | 0.4233   |
| 1.0611        | 14.0  | 10500 | 1.0620          | 0.4217   |
| 1.0504        | 15.0  | 11250 | 1.0556          | 0.425    |
| 1.0195        | 16.0  | 12000 | 1.0495          | 0.4367   |
| 1.0374        | 17.0  | 12750 | 1.0437          | 0.4383   |
| 1.0062        | 18.0  | 13500 | 1.0380          | 0.4433   |
| 1.0602        | 19.0  | 14250 | 1.0326          | 0.45     |
| 1.024         | 20.0  | 15000 | 1.0275          | 0.4533   |
| 0.9853        | 21.0  | 15750 | 1.0225          | 0.4567   |
| 1.024         | 22.0  | 16500 | 1.0178          | 0.46     |
| 1.0062        | 23.0  | 17250 | 1.0132          | 0.4617   |
| 0.9775        | 24.0  | 18000 | 1.0089          | 0.4683   |
| 0.9615        | 25.0  | 18750 | 1.0048          | 0.4733   |
| 0.9865        | 26.0  | 19500 | 1.0008          | 0.4783   |
| 0.9677        | 27.0  | 20250 | 0.9971          | 0.4867   |
| 0.9698        | 28.0  | 21000 | 0.9935          | 0.4867   |
| 0.9829        | 29.0  | 21750 | 0.9901          | 0.49     |
| 0.9556        | 30.0  | 22500 | 0.9870          | 0.49     |
| 0.963         | 31.0  | 23250 | 0.9840          | 0.4917   |
| 0.9489        | 32.0  | 24000 | 0.9813          | 0.495    |
| 0.9694        | 33.0  | 24750 | 0.9787          | 0.4967   |
| 0.9392        | 34.0  | 25500 | 0.9762          | 0.4967   |
| 0.9586        | 35.0  | 26250 | 0.9740          | 0.5      |
| 0.9291        | 36.0  | 27000 | 0.9720          | 0.5083   |
| 0.9064        | 37.0  | 27750 | 0.9701          | 0.5117   |
| 0.9352        | 38.0  | 28500 | 0.9684          | 0.5117   |
| 0.9164        | 39.0  | 29250 | 0.9668          | 0.5133   |
| 0.9501        | 40.0  | 30000 | 0.9654          | 0.515    |
| 0.8967        | 41.0  | 30750 | 0.9642          | 0.5167   |
| 0.9489        | 42.0  | 31500 | 0.9632          | 0.5167   |
| 0.9594        | 43.0  | 32250 | 0.9623          | 0.52     |
| 0.9042        | 44.0  | 33000 | 0.9616          | 0.5217   |
| 0.9218        | 45.0  | 33750 | 0.9610          | 0.5217   |
| 0.9234        | 46.0  | 34500 | 0.9605          | 0.5217   |
| 0.9392        | 47.0  | 35250 | 0.9602          | 0.5217   |
| 0.9497        | 48.0  | 36000 | 0.9600          | 0.525    |
| 0.9139        | 49.0  | 36750 | 0.9599          | 0.5233   |
| 0.8915        | 50.0  | 37500 | 0.9599          | 0.5233   |


### 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