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# smids_3x_deit_small_sgd_0001_fold5

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: 0.5879
- Accuracy: 0.775

## 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 |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|
| 1.0612        | 1.0   | 225   | 1.0496          | 0.4583   |
| 1.0331        | 2.0   | 450   | 1.0241          | 0.4917   |
| 0.9976        | 3.0   | 675   | 0.9994          | 0.5317   |
| 0.9646        | 4.0   | 900   | 0.9754          | 0.5567   |
| 0.9242        | 5.0   | 1125  | 0.9523          | 0.5833   |
| 0.9274        | 6.0   | 1350  | 0.9296          | 0.6183   |
| 0.9164        | 7.0   | 1575  | 0.9074          | 0.6367   |
| 0.9203        | 8.0   | 1800  | 0.8860          | 0.6517   |
| 0.8456        | 9.0   | 2025  | 0.8654          | 0.6783   |
| 0.8517        | 10.0  | 2250  | 0.8458          | 0.6767   |
| 0.8446        | 11.0  | 2475  | 0.8273          | 0.685    |
| 0.8321        | 12.0  | 2700  | 0.8097          | 0.6933   |
| 0.8204        | 13.0  | 2925  | 0.7928          | 0.695    |
| 0.8011        | 14.0  | 3150  | 0.7770          | 0.7017   |
| 0.737         | 15.0  | 3375  | 0.7621          | 0.7017   |
| 0.7399        | 16.0  | 3600  | 0.7486          | 0.7067   |
| 0.7567        | 17.0  | 3825  | 0.7359          | 0.715    |
| 0.7583        | 18.0  | 4050  | 0.7243          | 0.7167   |
| 0.7119        | 19.0  | 4275  | 0.7132          | 0.7233   |
| 0.6839        | 20.0  | 4500  | 0.7031          | 0.7317   |
| 0.6897        | 21.0  | 4725  | 0.6934          | 0.7317   |
| 0.6996        | 22.0  | 4950  | 0.6842          | 0.7333   |
| 0.6814        | 23.0  | 5175  | 0.6758          | 0.7417   |
| 0.6885        | 24.0  | 5400  | 0.6680          | 0.7433   |
| 0.6315        | 25.0  | 5625  | 0.6607          | 0.7417   |
| 0.6519        | 26.0  | 5850  | 0.6539          | 0.7417   |
| 0.6951        | 27.0  | 6075  | 0.6475          | 0.7467   |
| 0.6243        | 28.0  | 6300  | 0.6416          | 0.75     |
| 0.6218        | 29.0  | 6525  | 0.6361          | 0.7533   |
| 0.5941        | 30.0  | 6750  | 0.6309          | 0.7533   |
| 0.5704        | 31.0  | 6975  | 0.6263          | 0.755    |
| 0.5836        | 32.0  | 7200  | 0.6219          | 0.7583   |
| 0.6485        | 33.0  | 7425  | 0.6178          | 0.76     |
| 0.5854        | 34.0  | 7650  | 0.6142          | 0.76     |
| 0.5905        | 35.0  | 7875  | 0.6108          | 0.7617   |
| 0.5617        | 36.0  | 8100  | 0.6076          | 0.7633   |
| 0.5964        | 37.0  | 8325  | 0.6047          | 0.7683   |
| 0.5721        | 38.0  | 8550  | 0.6021          | 0.7683   |
| 0.5681        | 39.0  | 8775  | 0.5996          | 0.7683   |
| 0.5364        | 40.0  | 9000  | 0.5974          | 0.7683   |
| 0.5643        | 41.0  | 9225  | 0.5955          | 0.7683   |
| 0.6152        | 42.0  | 9450  | 0.5938          | 0.77     |
| 0.5824        | 43.0  | 9675  | 0.5924          | 0.7717   |
| 0.627         | 44.0  | 9900  | 0.5911          | 0.7733   |
| 0.5753        | 45.0  | 10125 | 0.5900          | 0.7733   |
| 0.5992        | 46.0  | 10350 | 0.5892          | 0.7733   |
| 0.6048        | 47.0  | 10575 | 0.5886          | 0.775    |
| 0.5934        | 48.0  | 10800 | 0.5882          | 0.775    |
| 0.5665        | 49.0  | 11025 | 0.5880          | 0.775    |
| 0.5873        | 50.0  | 11250 | 0.5879          | 0.775    |


### 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