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# smids_3x_beit_base_rms_001_fold4

This model is a fine-tuned version of [microsoft/beit-base-patch16-224](https://huggingface.co/microsoft/beit-base-patch16-224) on the imagefolder dataset.
It achieves the following results on the evaluation set:
- Loss: 0.6701
- Accuracy: 0.7583

## 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 |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|
| 1.1325        | 1.0   | 225   | 1.0820          | 0.33     |
| 0.9647        | 2.0   | 450   | 0.8610          | 0.5233   |
| 0.9155        | 3.0   | 675   | 0.8470          | 0.5233   |
| 0.8045        | 4.0   | 900   | 0.7955          | 0.5633   |
| 0.9422        | 5.0   | 1125  | 0.7622          | 0.5833   |
| 0.7846        | 6.0   | 1350  | 0.7519          | 0.6167   |
| 0.7593        | 7.0   | 1575  | 0.7344          | 0.6267   |
| 0.7843        | 8.0   | 1800  | 0.7233          | 0.625    |
| 0.758         | 9.0   | 2025  | 0.6963          | 0.675    |
| 0.7521        | 10.0  | 2250  | 0.7172          | 0.6367   |
| 0.7273        | 11.0  | 2475  | 0.7162          | 0.6867   |
| 0.7253        | 12.0  | 2700  | 0.7548          | 0.6367   |
| 0.7429        | 13.0  | 2925  | 0.7073          | 0.6933   |
| 0.6572        | 14.0  | 3150  | 0.7052          | 0.6733   |
| 0.668         | 15.0  | 3375  | 0.6850          | 0.6967   |
| 0.7304        | 16.0  | 3600  | 0.6940          | 0.6633   |
| 0.6361        | 17.0  | 3825  | 0.7269          | 0.68     |
| 0.7538        | 18.0  | 4050  | 0.6743          | 0.7      |
| 0.7884        | 19.0  | 4275  | 0.6564          | 0.7067   |
| 0.6141        | 20.0  | 4500  | 0.7026          | 0.68     |
| 0.6658        | 21.0  | 4725  | 0.6553          | 0.6983   |
| 0.7013        | 22.0  | 4950  | 0.6518          | 0.7133   |
| 0.6988        | 23.0  | 5175  | 0.7048          | 0.6433   |
| 0.6506        | 24.0  | 5400  | 0.6539          | 0.725    |
| 0.6644        | 25.0  | 5625  | 0.6442          | 0.7083   |
| 0.6782        | 26.0  | 5850  | 0.6333          | 0.735    |
| 0.6752        | 27.0  | 6075  | 0.6258          | 0.72     |
| 0.7055        | 28.0  | 6300  | 0.6242          | 0.7267   |
| 0.6118        | 29.0  | 6525  | 0.6321          | 0.7333   |
| 0.6455        | 30.0  | 6750  | 0.6581          | 0.7067   |
| 0.5483        | 31.0  | 6975  | 0.6054          | 0.745    |
| 0.6021        | 32.0  | 7200  | 0.6170          | 0.7333   |
| 0.5857        | 33.0  | 7425  | 0.6206          | 0.7367   |
| 0.657         | 34.0  | 7650  | 0.6354          | 0.72     |
| 0.6083        | 35.0  | 7875  | 0.6084          | 0.7517   |
| 0.6036        | 36.0  | 8100  | 0.6122          | 0.7267   |
| 0.5986        | 37.0  | 8325  | 0.6097          | 0.7383   |
| 0.5126        | 38.0  | 8550  | 0.6043          | 0.7467   |
| 0.5361        | 39.0  | 8775  | 0.6148          | 0.7483   |
| 0.5689        | 40.0  | 9000  | 0.6233          | 0.7567   |
| 0.5001        | 41.0  | 9225  | 0.6245          | 0.7567   |
| 0.5505        | 42.0  | 9450  | 0.6430          | 0.745    |
| 0.5115        | 43.0  | 9675  | 0.6524          | 0.7333   |
| 0.5425        | 44.0  | 9900  | 0.6414          | 0.7467   |
| 0.5416        | 45.0  | 10125 | 0.6407          | 0.75     |
| 0.4698        | 46.0  | 10350 | 0.6413          | 0.7367   |
| 0.5037        | 47.0  | 10575 | 0.6665          | 0.7533   |
| 0.5074        | 48.0  | 10800 | 0.6614          | 0.7583   |
| 0.4187        | 49.0  | 11025 | 0.6632          | 0.755    |
| 0.4669        | 50.0  | 11250 | 0.6701          | 0.7583   |


### 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