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# smids_3x_deit_small_adamax_001_fold5

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: 0.9259
- Accuracy: 0.8867

## 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: 0.001
- 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 |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|
| 0.5128        | 1.0   | 225   | 0.3960          | 0.845    |
| 0.2912        | 2.0   | 450   | 0.3545          | 0.855    |
| 0.2355        | 3.0   | 675   | 0.5138          | 0.8283   |
| 0.2322        | 4.0   | 900   | 0.3502          | 0.8567   |
| 0.1566        | 5.0   | 1125  | 0.4327          | 0.855    |
| 0.1654        | 6.0   | 1350  | 0.3396          | 0.8883   |
| 0.1711        | 7.0   | 1575  | 0.4306          | 0.8733   |
| 0.1936        | 8.0   | 1800  | 0.4649          | 0.8783   |
| 0.1252        | 9.0   | 2025  | 0.4662          | 0.875    |
| 0.0897        | 10.0  | 2250  | 0.4869          | 0.8767   |
| 0.0556        | 11.0  | 2475  | 0.4677          | 0.8883   |
| 0.117         | 12.0  | 2700  | 0.5865          | 0.8667   |
| 0.1159        | 13.0  | 2925  | 0.6366          | 0.87     |
| 0.0526        | 14.0  | 3150  | 0.5638          | 0.8917   |
| 0.0199        | 15.0  | 3375  | 0.6092          | 0.885    |
| 0.0105        | 16.0  | 3600  | 0.7275          | 0.8817   |
| 0.0173        | 17.0  | 3825  | 0.6827          | 0.8883   |
| 0.0471        | 18.0  | 4050  | 0.6845          | 0.87     |
| 0.0272        | 19.0  | 4275  | 0.6814          | 0.8933   |
| 0.0245        | 20.0  | 4500  | 0.7971          | 0.8767   |
| 0.0042        | 21.0  | 4725  | 0.7308          | 0.8867   |
| 0.0003        | 22.0  | 4950  | 0.8304          | 0.895    |
| 0.0001        | 23.0  | 5175  | 0.8296          | 0.8917   |
| 0.0235        | 24.0  | 5400  | 0.7781          | 0.88     |
| 0.0005        | 25.0  | 5625  | 0.8114          | 0.8833   |
| 0.0112        | 26.0  | 5850  | 0.8185          | 0.8833   |
| 0.0           | 27.0  | 6075  | 0.9165          | 0.8833   |
| 0.0034        | 28.0  | 6300  | 0.9111          | 0.8783   |
| 0.0037        | 29.0  | 6525  | 0.8969          | 0.8833   |
| 0.0001        | 30.0  | 6750  | 0.9635          | 0.8783   |
| 0.0           | 31.0  | 6975  | 0.8389          | 0.8933   |
| 0.0           | 32.0  | 7200  | 0.9477          | 0.8767   |
| 0.003         | 33.0  | 7425  | 0.8612          | 0.8917   |
| 0.0001        | 34.0  | 7650  | 0.8689          | 0.8983   |
| 0.0025        | 35.0  | 7875  | 0.8114          | 0.885    |
| 0.0           | 36.0  | 8100  | 0.8426          | 0.8867   |
| 0.0037        | 37.0  | 8325  | 0.8625          | 0.8833   |
| 0.0006        | 38.0  | 8550  | 0.8725          | 0.885    |
| 0.0           | 39.0  | 8775  | 0.8789          | 0.8833   |
| 0.0           | 40.0  | 9000  | 0.8405          | 0.885    |
| 0.0           | 41.0  | 9225  | 0.8737          | 0.8833   |
| 0.0           | 42.0  | 9450  | 0.9022          | 0.8867   |
| 0.003         | 43.0  | 9675  | 0.9115          | 0.8833   |
| 0.0           | 44.0  | 9900  | 0.9099          | 0.8867   |
| 0.0           | 45.0  | 10125 | 0.9104          | 0.8883   |
| 0.0           | 46.0  | 10350 | 0.9175          | 0.8867   |
| 0.0           | 47.0  | 10575 | 0.9200          | 0.8867   |
| 0.0           | 48.0  | 10800 | 0.9224          | 0.8867   |
| 0.0           | 49.0  | 11025 | 0.9241          | 0.8867   |
| 0.0           | 50.0  | 11250 | 0.9259          | 0.8867   |


### 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