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# smids_3x_deit_small_adamax_00001_fold3

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: 0.7023
- Accuracy: 0.91

## 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 |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|
| 0.4073        | 1.0   | 225   | 0.3484          | 0.8667   |
| 0.2568        | 2.0   | 450   | 0.2863          | 0.9      |
| 0.1796        | 3.0   | 675   | 0.2552          | 0.9083   |
| 0.1374        | 4.0   | 900   | 0.2541          | 0.9067   |
| 0.1027        | 5.0   | 1125  | 0.2543          | 0.91     |
| 0.0731        | 6.0   | 1350  | 0.2688          | 0.905    |
| 0.0643        | 7.0   | 1575  | 0.2928          | 0.9117   |
| 0.0439        | 8.0   | 1800  | 0.3138          | 0.9133   |
| 0.0402        | 9.0   | 2025  | 0.3569          | 0.9083   |
| 0.0289        | 10.0  | 2250  | 0.4503          | 0.905    |
| 0.0259        | 11.0  | 2475  | 0.4729          | 0.9033   |
| 0.009         | 12.0  | 2700  | 0.4822          | 0.9117   |
| 0.0012        | 13.0  | 2925  | 0.5123          | 0.91     |
| 0.0039        | 14.0  | 3150  | 0.5071          | 0.905    |
| 0.0007        | 15.0  | 3375  | 0.5292          | 0.9083   |
| 0.0062        | 16.0  | 3600  | 0.5423          | 0.91     |
| 0.0007        | 17.0  | 3825  | 0.6134          | 0.9117   |
| 0.0002        | 18.0  | 4050  | 0.5764          | 0.9083   |
| 0.0001        | 19.0  | 4275  | 0.5936          | 0.91     |
| 0.0001        | 20.0  | 4500  | 0.5995          | 0.9117   |
| 0.0001        | 21.0  | 4725  | 0.6209          | 0.91     |
| 0.0001        | 22.0  | 4950  | 0.6160          | 0.915    |
| 0.0           | 23.0  | 5175  | 0.6182          | 0.9117   |
| 0.0           | 24.0  | 5400  | 0.6761          | 0.9067   |
| 0.0027        | 25.0  | 5625  | 0.6972          | 0.9083   |
| 0.0           | 26.0  | 5850  | 0.6567          | 0.9117   |
| 0.0           | 27.0  | 6075  | 0.6572          | 0.9133   |
| 0.0           | 28.0  | 6300  | 0.6611          | 0.9133   |
| 0.0018        | 29.0  | 6525  | 0.7006          | 0.9133   |
| 0.0016        | 30.0  | 6750  | 0.6678          | 0.9117   |
| 0.0133        | 31.0  | 6975  | 0.6927          | 0.9117   |
| 0.0           | 32.0  | 7200  | 0.6752          | 0.9117   |
| 0.0           | 33.0  | 7425  | 0.6754          | 0.915    |
| 0.0           | 34.0  | 7650  | 0.6853          | 0.9117   |
| 0.0           | 35.0  | 7875  | 0.6889          | 0.91     |
| 0.0           | 36.0  | 8100  | 0.6852          | 0.9117   |
| 0.0           | 37.0  | 8325  | 0.7018          | 0.9117   |
| 0.0           | 38.0  | 8550  | 0.6889          | 0.9133   |
| 0.0           | 39.0  | 8775  | 0.6896          | 0.9133   |
| 0.0           | 40.0  | 9000  | 0.6924          | 0.9133   |
| 0.0           | 41.0  | 9225  | 0.7070          | 0.9117   |
| 0.0           | 42.0  | 9450  | 0.6982          | 0.91     |
| 0.0           | 43.0  | 9675  | 0.6970          | 0.9133   |
| 0.0           | 44.0  | 9900  | 0.6943          | 0.9117   |
| 0.0           | 45.0  | 10125 | 0.7012          | 0.91     |
| 0.0           | 46.0  | 10350 | 0.7058          | 0.91     |
| 0.0076        | 47.0  | 10575 | 0.7019          | 0.91     |
| 0.0           | 48.0  | 10800 | 0.7041          | 0.91     |
| 0.0           | 49.0  | 11025 | 0.7038          | 0.91     |
| 0.0           | 50.0  | 11250 | 0.7023          | 0.91     |


### 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