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# smids_3x_beit_base_sgd_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: 0.3939
- Accuracy: 0.8483

## 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 |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|
| 0.8245        | 1.0   | 225   | 0.8340          | 0.6383   |
| 0.6387        | 2.0   | 450   | 0.6132          | 0.7483   |
| 0.532         | 3.0   | 675   | 0.5292          | 0.7867   |
| 0.4946        | 4.0   | 900   | 0.4935          | 0.8017   |
| 0.5105        | 5.0   | 1125  | 0.4602          | 0.8217   |
| 0.3964        | 6.0   | 1350  | 0.4420          | 0.8217   |
| 0.4068        | 7.0   | 1575  | 0.4284          | 0.83     |
| 0.4501        | 8.0   | 1800  | 0.4257          | 0.8217   |
| 0.3713        | 9.0   | 2025  | 0.4132          | 0.835    |
| 0.3427        | 10.0  | 2250  | 0.4081          | 0.8383   |
| 0.4054        | 11.0  | 2475  | 0.4089          | 0.8367   |
| 0.3818        | 12.0  | 2700  | 0.4017          | 0.84     |
| 0.3036        | 13.0  | 2925  | 0.4061          | 0.8317   |
| 0.2784        | 14.0  | 3150  | 0.3991          | 0.84     |
| 0.2822        | 15.0  | 3375  | 0.3953          | 0.8383   |
| 0.3106        | 16.0  | 3600  | 0.3913          | 0.8383   |
| 0.2716        | 17.0  | 3825  | 0.3985          | 0.8367   |
| 0.3166        | 18.0  | 4050  | 0.3943          | 0.8417   |
| 0.334         | 19.0  | 4275  | 0.3982          | 0.8333   |
| 0.2592        | 20.0  | 4500  | 0.3982          | 0.8383   |
| 0.2836        | 21.0  | 4725  | 0.3926          | 0.8367   |
| 0.2688        | 22.0  | 4950  | 0.3918          | 0.8417   |
| 0.2602        | 23.0  | 5175  | 0.3951          | 0.8417   |
| 0.2941        | 24.0  | 5400  | 0.3932          | 0.8417   |
| 0.254         | 25.0  | 5625  | 0.3963          | 0.8433   |
| 0.2248        | 26.0  | 5850  | 0.3967          | 0.8417   |
| 0.2349        | 27.0  | 6075  | 0.3902          | 0.8417   |
| 0.2318        | 28.0  | 6300  | 0.3960          | 0.8417   |
| 0.2339        | 29.0  | 6525  | 0.3900          | 0.8467   |
| 0.2256        | 30.0  | 6750  | 0.3940          | 0.8483   |
| 0.2306        | 31.0  | 6975  | 0.3948          | 0.84     |
| 0.1769        | 32.0  | 7200  | 0.3920          | 0.8433   |
| 0.2714        | 33.0  | 7425  | 0.3958          | 0.8483   |
| 0.2441        | 34.0  | 7650  | 0.3973          | 0.845    |
| 0.2336        | 35.0  | 7875  | 0.3946          | 0.8483   |
| 0.2411        | 36.0  | 8100  | 0.3957          | 0.8517   |
| 0.2513        | 37.0  | 8325  | 0.3968          | 0.845    |
| 0.2269        | 38.0  | 8550  | 0.3976          | 0.8467   |
| 0.2515        | 39.0  | 8775  | 0.3973          | 0.8517   |
| 0.2727        | 40.0  | 9000  | 0.3940          | 0.8467   |
| 0.2023        | 41.0  | 9225  | 0.3933          | 0.845    |
| 0.2359        | 42.0  | 9450  | 0.3953          | 0.85     |
| 0.2348        | 43.0  | 9675  | 0.3957          | 0.8483   |
| 0.2703        | 44.0  | 9900  | 0.3944          | 0.8517   |
| 0.2898        | 45.0  | 10125 | 0.3951          | 0.8483   |
| 0.2247        | 46.0  | 10350 | 0.3937          | 0.85     |
| 0.2326        | 47.0  | 10575 | 0.3934          | 0.85     |
| 0.2372        | 48.0  | 10800 | 0.3941          | 0.8483   |
| 0.2457        | 49.0  | 11025 | 0.3940          | 0.8483   |
| 0.2302        | 50.0  | 11250 | 0.3939          | 0.8483   |


### 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