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# smids_3x_deit_base_rms_0001_fold3

This model is a fine-tuned version of [facebook/deit-base-patch16-224](https://huggingface.co/facebook/deit-base-patch16-224) on the imagefolder dataset.
It achieves the following results on the evaluation set:
- Loss: 1.0651
- Accuracy: 0.8983

## 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 |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|
| 0.337         | 1.0   | 225   | 0.2720          | 0.8967   |
| 0.2132        | 2.0   | 450   | 0.3434          | 0.885    |
| 0.1499        | 3.0   | 675   | 0.3688          | 0.895    |
| 0.0712        | 4.0   | 900   | 0.4644          | 0.8983   |
| 0.0713        | 5.0   | 1125  | 0.3775          | 0.8967   |
| 0.0583        | 6.0   | 1350  | 0.4485          | 0.8967   |
| 0.0178        | 7.0   | 1575  | 0.5692          | 0.8983   |
| 0.0543        | 8.0   | 1800  | 0.5124          | 0.9017   |
| 0.0524        | 9.0   | 2025  | 0.5816          | 0.8967   |
| 0.091         | 10.0  | 2250  | 0.7863          | 0.8483   |
| 0.0396        | 11.0  | 2475  | 0.6045          | 0.8833   |
| 0.0486        | 12.0  | 2700  | 0.6519          | 0.8867   |
| 0.018         | 13.0  | 2925  | 0.6027          | 0.9      |
| 0.011         | 14.0  | 3150  | 0.6223          | 0.9017   |
| 0.0027        | 15.0  | 3375  | 0.6635          | 0.8883   |
| 0.0168        | 16.0  | 3600  | 0.7279          | 0.8967   |
| 0.0421        | 17.0  | 3825  | 0.5369          | 0.9083   |
| 0.0321        | 18.0  | 4050  | 0.7204          | 0.8833   |
| 0.0392        | 19.0  | 4275  | 0.6016          | 0.89     |
| 0.043         | 20.0  | 4500  | 0.5463          | 0.9033   |
| 0.0004        | 21.0  | 4725  | 0.8261          | 0.8933   |
| 0.0001        | 22.0  | 4950  | 0.7660          | 0.8933   |
| 0.0229        | 23.0  | 5175  | 0.6989          | 0.8967   |
| 0.0011        | 24.0  | 5400  | 0.8082          | 0.8867   |
| 0.0036        | 25.0  | 5625  | 0.7432          | 0.905    |
| 0.0           | 26.0  | 5850  | 0.7507          | 0.9033   |
| 0.0005        | 27.0  | 6075  | 0.7412          | 0.8983   |
| 0.0156        | 28.0  | 6300  | 0.7887          | 0.9      |
| 0.0479        | 29.0  | 6525  | 0.6286          | 0.9117   |
| 0.0031        | 30.0  | 6750  | 0.7938          | 0.8883   |
| 0.0034        | 31.0  | 6975  | 0.8118          | 0.8917   |
| 0.0001        | 32.0  | 7200  | 0.7433          | 0.8917   |
| 0.0           | 33.0  | 7425  | 0.7678          | 0.905    |
| 0.0001        | 34.0  | 7650  | 0.8245          | 0.9      |
| 0.0           | 35.0  | 7875  | 0.9668          | 0.8917   |
| 0.0176        | 36.0  | 8100  | 0.7443          | 0.9017   |
| 0.0174        | 37.0  | 8325  | 0.8368          | 0.8883   |
| 0.0           | 38.0  | 8550  | 0.8506          | 0.8983   |
| 0.0           | 39.0  | 8775  | 0.8935          | 0.9017   |
| 0.0           | 40.0  | 9000  | 0.8981          | 0.9      |
| 0.0           | 41.0  | 9225  | 0.9362          | 0.9      |
| 0.0           | 42.0  | 9450  | 0.9833          | 0.8967   |
| 0.0           | 43.0  | 9675  | 0.9431          | 0.905    |
| 0.0           | 44.0  | 9900  | 1.0334          | 0.8967   |
| 0.0           | 45.0  | 10125 | 1.0384          | 0.8967   |
| 0.0           | 46.0  | 10350 | 1.0481          | 0.8967   |
| 0.0024        | 47.0  | 10575 | 1.0461          | 0.9017   |
| 0.0           | 48.0  | 10800 | 1.0546          | 0.9      |
| 0.0           | 49.0  | 11025 | 1.0617          | 0.8983   |
| 0.0           | 50.0  | 11250 | 1.0651          | 0.8983   |


### 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