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# smids_10x_deit_tiny_adamax_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: 0.8518
- Accuracy: 0.8933

## 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 |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|
| 0.292         | 1.0   | 750   | 0.2930          | 0.885    |
| 0.2303        | 2.0   | 1500  | 0.2459          | 0.9033   |
| 0.1782        | 3.0   | 2250  | 0.2507          | 0.9017   |
| 0.1592        | 4.0   | 3000  | 0.2451          | 0.9      |
| 0.1917        | 5.0   | 3750  | 0.2847          | 0.8967   |
| 0.0682        | 6.0   | 4500  | 0.3017          | 0.895    |
| 0.0567        | 7.0   | 5250  | 0.3497          | 0.8983   |
| 0.0573        | 8.0   | 6000  | 0.3610          | 0.9      |
| 0.1186        | 9.0   | 6750  | 0.4077          | 0.9067   |
| 0.0339        | 10.0  | 7500  | 0.4570          | 0.9017   |
| 0.0496        | 11.0  | 8250  | 0.5695          | 0.8933   |
| 0.0132        | 12.0  | 9000  | 0.6133          | 0.8983   |
| 0.0021        | 13.0  | 9750  | 0.5988          | 0.8933   |
| 0.0118        | 14.0  | 10500 | 0.6072          | 0.8933   |
| 0.0074        | 15.0  | 11250 | 0.6482          | 0.8967   |
| 0.0005        | 16.0  | 12000 | 0.7207          | 0.8933   |
| 0.0021        | 17.0  | 12750 | 0.7385          | 0.885    |
| 0.0           | 18.0  | 13500 | 0.7467          | 0.895    |
| 0.0005        | 19.0  | 14250 | 0.7555          | 0.8967   |
| 0.0           | 20.0  | 15000 | 0.7776          | 0.9      |
| 0.0           | 21.0  | 15750 | 0.8136          | 0.8933   |
| 0.0           | 22.0  | 16500 | 0.7839          | 0.8983   |
| 0.0002        | 23.0  | 17250 | 0.7795          | 0.8983   |
| 0.0           | 24.0  | 18000 | 0.7219          | 0.8967   |
| 0.0           | 25.0  | 18750 | 0.7978          | 0.9      |
| 0.0           | 26.0  | 19500 | 0.7801          | 0.8983   |
| 0.0           | 27.0  | 20250 | 0.8617          | 0.9      |
| 0.0           | 28.0  | 21000 | 0.7908          | 0.8967   |
| 0.0           | 29.0  | 21750 | 0.7838          | 0.9      |
| 0.0154        | 30.0  | 22500 | 0.8352          | 0.895    |
| 0.0           | 31.0  | 23250 | 0.8276          | 0.8967   |
| 0.0           | 32.0  | 24000 | 0.8168          | 0.8983   |
| 0.0           | 33.0  | 24750 | 0.8030          | 0.8933   |
| 0.0           | 34.0  | 25500 | 0.8162          | 0.895    |
| 0.0           | 35.0  | 26250 | 0.8097          | 0.895    |
| 0.0           | 36.0  | 27000 | 0.8226          | 0.8967   |
| 0.0047        | 37.0  | 27750 | 0.8297          | 0.895    |
| 0.0           | 38.0  | 28500 | 0.8250          | 0.8933   |
| 0.0           | 39.0  | 29250 | 0.8392          | 0.8983   |
| 0.0           | 40.0  | 30000 | 0.8326          | 0.9      |
| 0.0           | 41.0  | 30750 | 0.8399          | 0.895    |
| 0.0           | 42.0  | 31500 | 0.8505          | 0.895    |
| 0.0           | 43.0  | 32250 | 0.8292          | 0.8967   |
| 0.0           | 44.0  | 33000 | 0.8390          | 0.895    |
| 0.0           | 45.0  | 33750 | 0.8458          | 0.895    |
| 0.0           | 46.0  | 34500 | 0.8491          | 0.8933   |
| 0.0           | 47.0  | 35250 | 0.8489          | 0.8933   |
| 0.0           | 48.0  | 36000 | 0.8502          | 0.8933   |
| 0.0           | 49.0  | 36750 | 0.8534          | 0.8933   |
| 0.0           | 50.0  | 37500 | 0.8518          | 0.8933   |


### 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