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# smids_10x_deit_small_adamax_00001_fold2

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: 1.1874
- Accuracy: 0.8719

## 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 |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|
| 0.2546        | 1.0   | 750   | 0.2964          | 0.8885   |
| 0.1392        | 2.0   | 1500  | 0.2964          | 0.8935   |
| 0.1051        | 3.0   | 2250  | 0.3173          | 0.8802   |
| 0.0797        | 4.0   | 3000  | 0.3716          | 0.8802   |
| 0.0803        | 5.0   | 3750  | 0.4496          | 0.8769   |
| 0.0599        | 6.0   | 4500  | 0.5455          | 0.8769   |
| 0.0367        | 7.0   | 5250  | 0.6753          | 0.8686   |
| 0.0203        | 8.0   | 6000  | 0.7402          | 0.8752   |
| 0.0136        | 9.0   | 6750  | 0.8455          | 0.8686   |
| 0.0001        | 10.0  | 7500  | 0.8969          | 0.8686   |
| 0.0056        | 11.0  | 8250  | 0.9305          | 0.8769   |
| 0.0002        | 12.0  | 9000  | 0.9474          | 0.8752   |
| 0.0           | 13.0  | 9750  | 0.9957          | 0.8785   |
| 0.0           | 14.0  | 10500 | 1.0123          | 0.8769   |
| 0.0001        | 15.0  | 11250 | 0.9720          | 0.8835   |
| 0.0001        | 16.0  | 12000 | 1.0684          | 0.8785   |
| 0.0003        | 17.0  | 12750 | 1.1079          | 0.8752   |
| 0.0           | 18.0  | 13500 | 1.0971          | 0.8752   |
| 0.0           | 19.0  | 14250 | 1.0987          | 0.8735   |
| 0.0           | 20.0  | 15000 | 1.1190          | 0.8769   |
| 0.0           | 21.0  | 15750 | 1.1376          | 0.8686   |
| 0.0049        | 22.0  | 16500 | 1.1379          | 0.8686   |
| 0.0014        | 23.0  | 17250 | 1.1542          | 0.8752   |
| 0.0           | 24.0  | 18000 | 1.1536          | 0.8735   |
| 0.0           | 25.0  | 18750 | 1.1721          | 0.8719   |
| 0.0           | 26.0  | 19500 | 1.1498          | 0.8719   |
| 0.01          | 27.0  | 20250 | 1.1595          | 0.8719   |
| 0.0           | 28.0  | 21000 | 1.1250          | 0.8785   |
| 0.0           | 29.0  | 21750 | 1.1514          | 0.8686   |
| 0.0           | 30.0  | 22500 | 1.1182          | 0.8735   |
| 0.0           | 31.0  | 23250 | 1.1637          | 0.8752   |
| 0.0           | 32.0  | 24000 | 1.1726          | 0.8735   |
| 0.0           | 33.0  | 24750 | 1.1697          | 0.8719   |
| 0.0           | 34.0  | 25500 | 1.1588          | 0.8752   |
| 0.0           | 35.0  | 26250 | 1.1653          | 0.8702   |
| 0.0           | 36.0  | 27000 | 1.1669          | 0.8719   |
| 0.0141        | 37.0  | 27750 | 1.1767          | 0.8719   |
| 0.0           | 38.0  | 28500 | 1.1781          | 0.8719   |
| 0.0           | 39.0  | 29250 | 1.1951          | 0.8702   |
| 0.0           | 40.0  | 30000 | 1.1887          | 0.8702   |
| 0.0           | 41.0  | 30750 | 1.1872          | 0.8702   |
| 0.0           | 42.0  | 31500 | 1.1896          | 0.8702   |
| 0.0           | 43.0  | 32250 | 1.1930          | 0.8702   |
| 0.0           | 44.0  | 33000 | 1.1942          | 0.8702   |
| 0.0056        | 45.0  | 33750 | 1.1902          | 0.8702   |
| 0.0           | 46.0  | 34500 | 1.1880          | 0.8702   |
| 0.0           | 47.0  | 35250 | 1.1877          | 0.8702   |
| 0.0           | 48.0  | 36000 | 1.1882          | 0.8702   |
| 0.0           | 49.0  | 36750 | 1.1884          | 0.8702   |
| 0.0           | 50.0  | 37500 | 1.1874          | 0.8719   |


### 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