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# smids_3x_deit_base_rms_001_fold4

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: 0.6585
- Accuracy: 0.7683

## 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: 0.001
- 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 |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|
| 1.111         | 1.0   | 225   | 1.0986          | 0.34     |
| 0.9281        | 2.0   | 450   | 0.8238          | 0.535    |
| 0.8429        | 3.0   | 675   | 0.7715          | 0.5733   |
| 0.7817        | 4.0   | 900   | 0.9515          | 0.5183   |
| 0.8357        | 5.0   | 1125  | 0.7549          | 0.615    |
| 0.8459        | 6.0   | 1350  | 0.7437          | 0.6367   |
| 0.8489        | 7.0   | 1575  | 0.8096          | 0.6133   |
| 0.7642        | 8.0   | 1800  | 0.7237          | 0.6317   |
| 0.7593        | 9.0   | 2025  | 0.7493          | 0.6517   |
| 0.7519        | 10.0  | 2250  | 0.7058          | 0.6667   |
| 0.7342        | 11.0  | 2475  | 0.6982          | 0.6883   |
| 0.7801        | 12.0  | 2700  | 0.7132          | 0.6683   |
| 0.8045        | 13.0  | 2925  | 0.6860          | 0.6917   |
| 0.6831        | 14.0  | 3150  | 0.6911          | 0.68     |
| 0.695         | 15.0  | 3375  | 0.6849          | 0.6917   |
| 0.7337        | 16.0  | 3600  | 0.6867          | 0.6933   |
| 0.6786        | 17.0  | 3825  | 0.7826          | 0.65     |
| 0.7106        | 18.0  | 4050  | 0.7080          | 0.685    |
| 0.7255        | 19.0  | 4275  | 0.6333          | 0.7083   |
| 0.5816        | 20.0  | 4500  | 0.6578          | 0.71     |
| 0.5877        | 21.0  | 4725  | 0.6049          | 0.74     |
| 0.64          | 22.0  | 4950  | 0.6186          | 0.715    |
| 0.6573        | 23.0  | 5175  | 0.6447          | 0.7033   |
| 0.6339        | 24.0  | 5400  | 0.5686          | 0.77     |
| 0.593         | 25.0  | 5625  | 0.5930          | 0.7583   |
| 0.6542        | 26.0  | 5850  | 0.6005          | 0.74     |
| 0.605         | 27.0  | 6075  | 0.5866          | 0.75     |
| 0.5386        | 28.0  | 6300  | 0.5839          | 0.7417   |
| 0.5132        | 29.0  | 6525  | 0.5733          | 0.76     |
| 0.512         | 30.0  | 6750  | 0.5641          | 0.7617   |
| 0.5165        | 31.0  | 6975  | 0.5435          | 0.7783   |
| 0.526         | 32.0  | 7200  | 0.5563          | 0.765    |
| 0.4963        | 33.0  | 7425  | 0.6237          | 0.7433   |
| 0.5513        | 34.0  | 7650  | 0.5398          | 0.7733   |
| 0.5129        | 35.0  | 7875  | 0.5786          | 0.76     |
| 0.5453        | 36.0  | 8100  | 0.5387          | 0.765    |
| 0.5096        | 37.0  | 8325  | 0.5322          | 0.7833   |
| 0.4852        | 38.0  | 8550  | 0.5586          | 0.775    |
| 0.4638        | 39.0  | 8775  | 0.5742          | 0.755    |
| 0.5442        | 40.0  | 9000  | 0.5696          | 0.7617   |
| 0.4516        | 41.0  | 9225  | 0.5837          | 0.78     |
| 0.4465        | 42.0  | 9450  | 0.5694          | 0.7833   |
| 0.4118        | 43.0  | 9675  | 0.5778          | 0.775    |
| 0.436         | 44.0  | 9900  | 0.5795          | 0.7683   |
| 0.3828        | 45.0  | 10125 | 0.6308          | 0.7517   |
| 0.409         | 46.0  | 10350 | 0.5949          | 0.76     |
| 0.402         | 47.0  | 10575 | 0.6432          | 0.755    |
| 0.3606        | 48.0  | 10800 | 0.6291          | 0.7683   |
| 0.2902        | 49.0  | 11025 | 0.6518          | 0.7683   |
| 0.3362        | 50.0  | 11250 | 0.6585          | 0.7683   |


### 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