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# smids_3x_beit_base_rms_001_fold3

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: 0.6251
- Accuracy: 0.7617

## 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 |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|
| 0.9148        | 1.0   | 225   | 0.9238          | 0.505    |
| 0.8709        | 2.0   | 450   | 0.9060          | 0.515    |
| 0.8398        | 3.0   | 675   | 0.8688          | 0.5317   |
| 0.74          | 4.0   | 900   | 0.7859          | 0.5617   |
| 0.7787        | 5.0   | 1125  | 0.7847          | 0.6017   |
| 0.7532        | 6.0   | 1350  | 0.7702          | 0.63     |
| 0.7432        | 7.0   | 1575  | 0.7450          | 0.655    |
| 0.7264        | 8.0   | 1800  | 0.7610          | 0.6317   |
| 0.7321        | 9.0   | 2025  | 0.7293          | 0.655    |
| 0.6592        | 10.0  | 2250  | 0.7888          | 0.6367   |
| 0.7528        | 11.0  | 2475  | 0.7158          | 0.6633   |
| 0.7282        | 12.0  | 2700  | 0.7365          | 0.64     |
| 0.6884        | 13.0  | 2925  | 0.6939          | 0.6733   |
| 0.6852        | 14.0  | 3150  | 0.7006          | 0.67     |
| 0.6011        | 15.0  | 3375  | 0.7591          | 0.6233   |
| 0.6904        | 16.0  | 3600  | 0.6846          | 0.6717   |
| 0.6393        | 17.0  | 3825  | 0.6741          | 0.7117   |
| 0.6772        | 18.0  | 4050  | 0.6655          | 0.6683   |
| 0.6409        | 19.0  | 4275  | 0.6658          | 0.6933   |
| 0.5941        | 20.0  | 4500  | 0.6429          | 0.7017   |
| 0.5753        | 21.0  | 4725  | 0.6753          | 0.6833   |
| 0.5975        | 22.0  | 4950  | 0.6543          | 0.6917   |
| 0.5954        | 23.0  | 5175  | 0.6358          | 0.7233   |
| 0.5729        | 24.0  | 5400  | 0.6341          | 0.7133   |
| 0.6313        | 25.0  | 5625  | 0.6336          | 0.7033   |
| 0.5938        | 26.0  | 5850  | 0.6447          | 0.7083   |
| 0.5183        | 27.0  | 6075  | 0.6247          | 0.7233   |
| 0.5713        | 28.0  | 6300  | 0.6145          | 0.73     |
| 0.5948        | 29.0  | 6525  | 0.5934          | 0.7317   |
| 0.5273        | 30.0  | 6750  | 0.5971          | 0.7367   |
| 0.5431        | 31.0  | 6975  | 0.5930          | 0.7433   |
| 0.6025        | 32.0  | 7200  | 0.6434          | 0.7183   |
| 0.5898        | 33.0  | 7425  | 0.5982          | 0.7383   |
| 0.5455        | 34.0  | 7650  | 0.5983          | 0.75     |
| 0.4857        | 35.0  | 7875  | 0.6162          | 0.735    |
| 0.5822        | 36.0  | 8100  | 0.5546          | 0.7517   |
| 0.4869        | 37.0  | 8325  | 0.5748          | 0.745    |
| 0.4722        | 38.0  | 8550  | 0.5753          | 0.7417   |
| 0.4982        | 39.0  | 8775  | 0.5694          | 0.7483   |
| 0.4478        | 40.0  | 9000  | 0.5912          | 0.74     |
| 0.4295        | 41.0  | 9225  | 0.5914          | 0.75     |
| 0.4581        | 42.0  | 9450  | 0.5846          | 0.7617   |
| 0.3797        | 43.0  | 9675  | 0.5733          | 0.7667   |
| 0.4086        | 44.0  | 9900  | 0.6072          | 0.7517   |
| 0.4164        | 45.0  | 10125 | 0.6033          | 0.7583   |
| 0.3774        | 46.0  | 10350 | 0.6024          | 0.75     |
| 0.392         | 47.0  | 10575 | 0.5976          | 0.7617   |
| 0.3586        | 48.0  | 10800 | 0.6199          | 0.76     |
| 0.3854        | 49.0  | 11025 | 0.6198          | 0.7667   |
| 0.3586        | 50.0  | 11250 | 0.6251          | 0.7617   |


### 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