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# smids_3x_deit_small_sgd_00001_fold4

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: 1.0105
- Accuracy: 0.505

## 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 |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|
| 1.0492        | 1.0   | 225   | 1.0666          | 0.43     |
| 1.0754        | 2.0   | 450   | 1.0640          | 0.43     |
| 1.0504        | 3.0   | 675   | 1.0616          | 0.4283   |
| 1.071         | 4.0   | 900   | 1.0591          | 0.43     |
| 1.052         | 5.0   | 1125  | 1.0568          | 0.4333   |
| 1.0616        | 6.0   | 1350  | 1.0545          | 0.44     |
| 1.0716        | 7.0   | 1575  | 1.0524          | 0.4433   |
| 1.0533        | 8.0   | 1800  | 1.0502          | 0.4417   |
| 1.0683        | 9.0   | 2025  | 1.0482          | 0.44     |
| 1.0375        | 10.0  | 2250  | 1.0461          | 0.4417   |
| 1.0594        | 11.0  | 2475  | 1.0442          | 0.445    |
| 1.0638        | 12.0  | 2700  | 1.0423          | 0.4467   |
| 1.0743        | 13.0  | 2925  | 1.0405          | 0.45     |
| 1.0117        | 14.0  | 3150  | 1.0387          | 0.4517   |
| 1.0604        | 15.0  | 3375  | 1.0370          | 0.4517   |
| 1.0498        | 16.0  | 3600  | 1.0354          | 0.4567   |
| 1.0315        | 17.0  | 3825  | 1.0338          | 0.46     |
| 1.0306        | 18.0  | 4050  | 1.0323          | 0.465    |
| 1.0262        | 19.0  | 4275  | 1.0309          | 0.4667   |
| 1.0262        | 20.0  | 4500  | 1.0294          | 0.4667   |
| 1.0341        | 21.0  | 4725  | 1.0281          | 0.4683   |
| 1.0464        | 22.0  | 4950  | 1.0268          | 0.4717   |
| 1.0098        | 23.0  | 5175  | 1.0255          | 0.4733   |
| 1.029         | 24.0  | 5400  | 1.0243          | 0.475    |
| 1.0091        | 25.0  | 5625  | 1.0231          | 0.4817   |
| 1.017         | 26.0  | 5850  | 1.0221          | 0.4833   |
| 1.0365        | 27.0  | 6075  | 1.0210          | 0.4883   |
| 1.019         | 28.0  | 6300  | 1.0200          | 0.4883   |
| 1.0442        | 29.0  | 6525  | 1.0191          | 0.4883   |
| 1.0415        | 30.0  | 6750  | 1.0182          | 0.4867   |
| 1.0316        | 31.0  | 6975  | 1.0174          | 0.4883   |
| 1.045         | 32.0  | 7200  | 1.0166          | 0.4883   |
| 1.0078        | 33.0  | 7425  | 1.0159          | 0.49     |
| 1.023         | 34.0  | 7650  | 1.0152          | 0.49     |
| 1.0174        | 35.0  | 7875  | 1.0146          | 0.495    |
| 1.0095        | 36.0  | 8100  | 1.0140          | 0.5      |
| 1.0162        | 37.0  | 8325  | 1.0135          | 0.5      |
| 1.0427        | 38.0  | 8550  | 1.0130          | 0.5      |
| 1.0155        | 39.0  | 8775  | 1.0125          | 0.5033   |
| 1.0159        | 40.0  | 9000  | 1.0122          | 0.505    |
| 1.0255        | 41.0  | 9225  | 1.0118          | 0.505    |
| 1.023         | 42.0  | 9450  | 1.0115          | 0.5067   |
| 1.0068        | 43.0  | 9675  | 1.0113          | 0.505    |
| 1.0321        | 44.0  | 9900  | 1.0110          | 0.505    |
| 1.0329        | 45.0  | 10125 | 1.0109          | 0.505    |
| 1.0275        | 46.0  | 10350 | 1.0107          | 0.505    |
| 1.0181        | 47.0  | 10575 | 1.0106          | 0.505    |
| 1.0137        | 48.0  | 10800 | 1.0106          | 0.505    |
| 1.0177        | 49.0  | 11025 | 1.0105          | 0.505    |
| 1.0148        | 50.0  | 11250 | 1.0105          | 0.505    |


### 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