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# smids_3x_deit_small_rms_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: 0.9862
- Accuracy: 0.8967

## 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 |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|
| 0.2966        | 1.0   | 225   | 0.2690          | 0.9033   |
| 0.1769        | 2.0   | 450   | 0.2748          | 0.8983   |
| 0.056         | 3.0   | 675   | 0.2735          | 0.9083   |
| 0.044         | 4.0   | 900   | 0.3836          | 0.8983   |
| 0.0464        | 5.0   | 1125  | 0.4418          | 0.9033   |
| 0.0264        | 6.0   | 1350  | 0.5404          | 0.9      |
| 0.0095        | 7.0   | 1575  | 0.6147          | 0.9033   |
| 0.0323        | 8.0   | 1800  | 0.7069          | 0.895    |
| 0.0188        | 9.0   | 2025  | 0.6561          | 0.8917   |
| 0.0451        | 10.0  | 2250  | 0.7550          | 0.8883   |
| 0.0417        | 11.0  | 2475  | 0.7782          | 0.885    |
| 0.0052        | 12.0  | 2700  | 0.7305          | 0.8983   |
| 0.0           | 13.0  | 2925  | 0.7512          | 0.895    |
| 0.0288        | 14.0  | 3150  | 0.7604          | 0.905    |
| 0.0005        | 15.0  | 3375  | 0.9139          | 0.8833   |
| 0.0005        | 16.0  | 3600  | 0.8987          | 0.895    |
| 0.0052        | 17.0  | 3825  | 0.8858          | 0.8867   |
| 0.0001        | 18.0  | 4050  | 0.8522          | 0.9067   |
| 0.0           | 19.0  | 4275  | 0.8054          | 0.8983   |
| 0.0188        | 20.0  | 4500  | 0.9106          | 0.8833   |
| 0.0011        | 21.0  | 4725  | 0.9098          | 0.9      |
| 0.0058        | 22.0  | 4950  | 0.8872          | 0.895    |
| 0.0           | 23.0  | 5175  | 0.8360          | 0.9017   |
| 0.0049        | 24.0  | 5400  | 0.8800          | 0.9017   |
| 0.0051        | 25.0  | 5625  | 0.8690          | 0.8967   |
| 0.0025        | 26.0  | 5850  | 0.8437          | 0.9067   |
| 0.0           | 27.0  | 6075  | 0.8956          | 0.8983   |
| 0.0186        | 28.0  | 6300  | 0.8603          | 0.895    |
| 0.0039        | 29.0  | 6525  | 0.8775          | 0.9      |
| 0.0038        | 30.0  | 6750  | 0.8713          | 0.91     |
| 0.0039        | 31.0  | 6975  | 0.8607          | 0.9033   |
| 0.0           | 32.0  | 7200  | 0.9394          | 0.895    |
| 0.0           | 33.0  | 7425  | 0.9694          | 0.8917   |
| 0.0           | 34.0  | 7650  | 0.9863          | 0.8917   |
| 0.0           | 35.0  | 7875  | 0.9386          | 0.8983   |
| 0.0           | 36.0  | 8100  | 0.9806          | 0.8867   |
| 0.0           | 37.0  | 8325  | 0.9851          | 0.89     |
| 0.0           | 38.0  | 8550  | 0.9929          | 0.89     |
| 0.0           | 39.0  | 8775  | 0.9484          | 0.9067   |
| 0.0           | 40.0  | 9000  | 0.9905          | 0.8933   |
| 0.0           | 41.0  | 9225  | 0.9966          | 0.8917   |
| 0.0           | 42.0  | 9450  | 0.9879          | 0.8933   |
| 0.0           | 43.0  | 9675  | 0.9809          | 0.8967   |
| 0.0           | 44.0  | 9900  | 0.9763          | 0.9017   |
| 0.0           | 45.0  | 10125 | 0.9905          | 0.8967   |
| 0.0           | 46.0  | 10350 | 0.9941          | 0.8967   |
| 0.0024        | 47.0  | 10575 | 0.9905          | 0.8967   |
| 0.0           | 48.0  | 10800 | 0.9890          | 0.8967   |
| 0.0           | 49.0  | 11025 | 0.9879          | 0.8967   |
| 0.0           | 50.0  | 11250 | 0.9862          | 0.8967   |


### 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