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# smids_3x_deit_base_rms_0001_fold5

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: 1.1319
- Accuracy: 0.8933

## 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 |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|
| 0.3269        | 1.0   | 225   | 0.4237          | 0.8533   |
| 0.1863        | 2.0   | 450   | 0.4104          | 0.8633   |
| 0.1323        | 3.0   | 675   | 0.3455          | 0.8767   |
| 0.0674        | 4.0   | 900   | 0.4806          | 0.895    |
| 0.0524        | 5.0   | 1125  | 0.4637          | 0.8783   |
| 0.0407        | 6.0   | 1350  | 0.4567          | 0.89     |
| 0.0468        | 7.0   | 1575  | 0.5568          | 0.8767   |
| 0.0239        | 8.0   | 1800  | 0.6027          | 0.8783   |
| 0.0176        | 9.0   | 2025  | 0.6627          | 0.8817   |
| 0.0132        | 10.0  | 2250  | 0.7320          | 0.8683   |
| 0.0166        | 11.0  | 2475  | 0.6923          | 0.88     |
| 0.11          | 12.0  | 2700  | 0.5801          | 0.8883   |
| 0.0376        | 13.0  | 2925  | 0.4794          | 0.89     |
| 0.0285        | 14.0  | 3150  | 0.6473          | 0.8883   |
| 0.0192        | 15.0  | 3375  | 0.7068          | 0.8967   |
| 0.0041        | 16.0  | 3600  | 0.7011          | 0.895    |
| 0.012         | 17.0  | 3825  | 0.6525          | 0.9017   |
| 0.03          | 18.0  | 4050  | 0.6508          | 0.91     |
| 0.0251        | 19.0  | 4275  | 0.7493          | 0.8967   |
| 0.0108        | 20.0  | 4500  | 0.7077          | 0.895    |
| 0.0009        | 21.0  | 4725  | 0.6790          | 0.89     |
| 0.0002        | 22.0  | 4950  | 0.7411          | 0.8967   |
| 0.0264        | 23.0  | 5175  | 0.7794          | 0.8983   |
| 0.0051        | 24.0  | 5400  | 0.9553          | 0.8883   |
| 0.0221        | 25.0  | 5625  | 0.7771          | 0.905    |
| 0.0315        | 26.0  | 5850  | 0.7638          | 0.9      |
| 0.003         | 27.0  | 6075  | 0.8047          | 0.9      |
| 0.0125        | 28.0  | 6300  | 0.7560          | 0.9      |
| 0.0039        | 29.0  | 6525  | 0.7149          | 0.9067   |
| 0.0           | 30.0  | 6750  | 0.8257          | 0.9      |
| 0.0           | 31.0  | 6975  | 0.8249          | 0.9133   |
| 0.0           | 32.0  | 7200  | 0.8553          | 0.9033   |
| 0.01          | 33.0  | 7425  | 0.9333          | 0.895    |
| 0.0           | 34.0  | 7650  | 0.9286          | 0.9067   |
| 0.0024        | 35.0  | 7875  | 0.9413          | 0.8983   |
| 0.0           | 36.0  | 8100  | 0.8868          | 0.9083   |
| 0.0039        | 37.0  | 8325  | 0.9484          | 0.9033   |
| 0.0           | 38.0  | 8550  | 0.9617          | 0.9033   |
| 0.0           | 39.0  | 8775  | 0.9572          | 0.9017   |
| 0.0           | 40.0  | 9000  | 1.0465          | 0.8933   |
| 0.0           | 41.0  | 9225  | 1.0197          | 0.8983   |
| 0.0           | 42.0  | 9450  | 1.0477          | 0.895    |
| 0.0029        | 43.0  | 9675  | 1.0659          | 0.8983   |
| 0.0           | 44.0  | 9900  | 1.0846          | 0.8967   |
| 0.0           | 45.0  | 10125 | 1.1008          | 0.8983   |
| 0.0           | 46.0  | 10350 | 1.1123          | 0.8917   |
| 0.0           | 47.0  | 10575 | 1.1192          | 0.8933   |
| 0.0           | 48.0  | 10800 | 1.1251          | 0.8933   |
| 0.0           | 49.0  | 11025 | 1.1289          | 0.8933   |
| 0.0           | 50.0  | 11250 | 1.1319          | 0.8933   |


### 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