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# smids_3x_deit_tiny_adamax_0001_fold4

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: 1.3554
- Accuracy: 0.875

## 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: 0.0001
- 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 |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|
| 0.3155        | 1.0   | 225   | 0.3767          | 0.855    |
| 0.1407        | 2.0   | 450   | 0.3968          | 0.87     |
| 0.0909        | 3.0   | 675   | 0.5151          | 0.8617   |
| 0.1097        | 4.0   | 900   | 0.4932          | 0.8633   |
| 0.0371        | 5.0   | 1125  | 0.6275          | 0.8683   |
| 0.0101        | 6.0   | 1350  | 0.7429          | 0.8633   |
| 0.0391        | 7.0   | 1575  | 0.8418          | 0.865    |
| 0.0077        | 8.0   | 1800  | 0.8293          | 0.8767   |
| 0.0687        | 9.0   | 2025  | 0.9529          | 0.8667   |
| 0.0191        | 10.0  | 2250  | 1.0880          | 0.8633   |
| 0.0325        | 11.0  | 2475  | 1.1276          | 0.8583   |
| 0.0003        | 12.0  | 2700  | 1.0880          | 0.8583   |
| 0.0043        | 13.0  | 2925  | 1.1462          | 0.8717   |
| 0.0003        | 14.0  | 3150  | 1.2942          | 0.8467   |
| 0.04          | 15.0  | 3375  | 1.1259          | 0.86     |
| 0.0007        | 16.0  | 3600  | 1.1878          | 0.86     |
| 0.0001        | 17.0  | 3825  | 1.2005          | 0.8683   |
| 0.0003        | 18.0  | 4050  | 1.1961          | 0.8667   |
| 0.0           | 19.0  | 4275  | 1.1734          | 0.8717   |
| 0.0002        | 20.0  | 4500  | 1.2441          | 0.865    |
| 0.0           | 21.0  | 4725  | 1.2631          | 0.87     |
| 0.0           | 22.0  | 4950  | 1.1604          | 0.8733   |
| 0.0           | 23.0  | 5175  | 1.1940          | 0.8683   |
| 0.0           | 24.0  | 5400  | 1.2754          | 0.8683   |
| 0.0           | 25.0  | 5625  | 1.2790          | 0.8617   |
| 0.0           | 26.0  | 5850  | 1.2664          | 0.8733   |
| 0.0           | 27.0  | 6075  | 1.2877          | 0.875    |
| 0.0           | 28.0  | 6300  | 1.2788          | 0.88     |
| 0.0           | 29.0  | 6525  | 1.2589          | 0.8783   |
| 0.0           | 30.0  | 6750  | 1.2671          | 0.8783   |
| 0.0           | 31.0  | 6975  | 1.2750          | 0.8783   |
| 0.0           | 32.0  | 7200  | 1.2783          | 0.875    |
| 0.0           | 33.0  | 7425  | 1.2973          | 0.8733   |
| 0.0           | 34.0  | 7650  | 1.3104          | 0.8717   |
| 0.0           | 35.0  | 7875  | 1.3108          | 0.8717   |
| 0.0           | 36.0  | 8100  | 1.3246          | 0.8717   |
| 0.0           | 37.0  | 8325  | 1.3217          | 0.87     |
| 0.0           | 38.0  | 8550  | 1.3349          | 0.8733   |
| 0.0           | 39.0  | 8775  | 1.3355          | 0.8733   |
| 0.0           | 40.0  | 9000  | 1.3337          | 0.8733   |
| 0.0           | 41.0  | 9225  | 1.3326          | 0.875    |
| 0.0036        | 42.0  | 9450  | 1.3380          | 0.8733   |
| 0.0           | 43.0  | 9675  | 1.3430          | 0.875    |
| 0.0032        | 44.0  | 9900  | 1.3445          | 0.8733   |
| 0.0           | 45.0  | 10125 | 1.3492          | 0.875    |
| 0.0           | 46.0  | 10350 | 1.3514          | 0.875    |
| 0.0           | 47.0  | 10575 | 1.3535          | 0.875    |
| 0.0           | 48.0  | 10800 | 1.3548          | 0.875    |
| 0.0           | 49.0  | 11025 | 1.3543          | 0.875    |
| 0.0           | 50.0  | 11250 | 1.3554          | 0.875    |


### 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