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# smids_3x_deit_small_rms_0001_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: 1.1839
- Accuracy: 0.885

## 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 |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|
| 0.3285        | 1.0   | 225   | 0.3079          | 0.8733   |
| 0.249         | 2.0   | 450   | 0.2876          | 0.8933   |
| 0.1277        | 3.0   | 675   | 0.3615          | 0.8983   |
| 0.1138        | 4.0   | 900   | 0.3786          | 0.9      |
| 0.0577        | 5.0   | 1125  | 0.5033          | 0.8833   |
| 0.0713        | 6.0   | 1350  | 0.6038          | 0.88     |
| 0.0526        | 7.0   | 1575  | 0.5533          | 0.8783   |
| 0.1027        | 8.0   | 1800  | 0.6305          | 0.885    |
| 0.1119        | 9.0   | 2025  | 0.5230          | 0.8883   |
| 0.0596        | 10.0  | 2250  | 0.4789          | 0.895    |
| 0.0427        | 11.0  | 2475  | 0.6163          | 0.8883   |
| 0.0824        | 12.0  | 2700  | 0.6835          | 0.8817   |
| 0.029         | 13.0  | 2925  | 0.6829          | 0.8933   |
| 0.0703        | 14.0  | 3150  | 0.7193          | 0.8833   |
| 0.0396        | 15.0  | 3375  | 0.6819          | 0.9      |
| 0.0094        | 16.0  | 3600  | 0.6229          | 0.9083   |
| 0.032         | 17.0  | 3825  | 0.7440          | 0.885    |
| 0.0019        | 18.0  | 4050  | 0.6469          | 0.8933   |
| 0.0426        | 19.0  | 4275  | 0.6592          | 0.8817   |
| 0.0085        | 20.0  | 4500  | 0.7146          | 0.8967   |
| 0.0003        | 21.0  | 4725  | 0.7501          | 0.8933   |
| 0.0003        | 22.0  | 4950  | 0.7432          | 0.885    |
| 0.0137        | 23.0  | 5175  | 0.7637          | 0.8717   |
| 0.0017        | 24.0  | 5400  | 0.8802          | 0.8817   |
| 0.0465        | 25.0  | 5625  | 0.7437          | 0.8883   |
| 0.0022        | 26.0  | 5850  | 0.7726          | 0.8933   |
| 0.0063        | 27.0  | 6075  | 0.8349          | 0.8883   |
| 0.0003        | 28.0  | 6300  | 0.7609          | 0.8933   |
| 0.0094        | 29.0  | 6525  | 0.8877          | 0.8917   |
| 0.0308        | 30.0  | 6750  | 0.8912          | 0.8967   |
| 0.0037        | 31.0  | 6975  | 0.9195          | 0.8817   |
| 0.0           | 32.0  | 7200  | 0.8416          | 0.8967   |
| 0.0           | 33.0  | 7425  | 0.8645          | 0.895    |
| 0.0046        | 34.0  | 7650  | 0.9341          | 0.9      |
| 0.0           | 35.0  | 7875  | 0.9327          | 0.8967   |
| 0.0014        | 36.0  | 8100  | 1.0523          | 0.8733   |
| 0.0027        | 37.0  | 8325  | 0.9916          | 0.8883   |
| 0.0           | 38.0  | 8550  | 0.9734          | 0.8983   |
| 0.0           | 39.0  | 8775  | 1.0426          | 0.89     |
| 0.0           | 40.0  | 9000  | 1.0377          | 0.8917   |
| 0.0           | 41.0  | 9225  | 1.0718          | 0.8867   |
| 0.0           | 42.0  | 9450  | 1.1007          | 0.89     |
| 0.0           | 43.0  | 9675  | 1.1332          | 0.8883   |
| 0.0           | 44.0  | 9900  | 1.1398          | 0.89     |
| 0.0           | 45.0  | 10125 | 1.1492          | 0.89     |
| 0.0           | 46.0  | 10350 | 1.1564          | 0.8883   |
| 0.0026        | 47.0  | 10575 | 1.1721          | 0.885    |
| 0.0           | 48.0  | 10800 | 1.1759          | 0.885    |
| 0.0           | 49.0  | 11025 | 1.1812          | 0.885    |
| 0.0           | 50.0  | 11250 | 1.1839          | 0.885    |


### 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