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# smids_3x_beit_base_rms_001_fold5

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: 1.1444
- Accuracy: 0.8217

## 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 |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|
| 0.9503        | 1.0   | 225   | 0.9116          | 0.5117   |
| 0.9706        | 2.0   | 450   | 0.9826          | 0.46     |
| 0.8           | 3.0   | 675   | 0.8216          | 0.55     |
| 0.7869        | 4.0   | 900   | 0.7274          | 0.6417   |
| 0.7386        | 5.0   | 1125  | 0.7210          | 0.65     |
| 0.6956        | 6.0   | 1350  | 0.8161          | 0.6183   |
| 0.8586        | 7.0   | 1575  | 0.7427          | 0.6283   |
| 0.6974        | 8.0   | 1800  | 0.7391          | 0.6467   |
| 0.6497        | 9.0   | 2025  | 0.6781          | 0.665    |
| 0.665         | 10.0  | 2250  | 0.6784          | 0.69     |
| 0.6749        | 11.0  | 2475  | 0.6355          | 0.7083   |
| 0.6727        | 12.0  | 2700  | 0.6116          | 0.7083   |
| 0.6759        | 13.0  | 2925  | 0.6229          | 0.715    |
| 0.6034        | 14.0  | 3150  | 0.6562          | 0.685    |
| 0.5372        | 15.0  | 3375  | 0.5788          | 0.755    |
| 0.539         | 16.0  | 3600  | 0.5524          | 0.7583   |
| 0.5144        | 17.0  | 3825  | 0.5824          | 0.7483   |
| 0.4796        | 18.0  | 4050  | 0.5455          | 0.7617   |
| 0.5096        | 19.0  | 4275  | 0.5692          | 0.765    |
| 0.4664        | 20.0  | 4500  | 0.5893          | 0.7533   |
| 0.3623        | 21.0  | 4725  | 0.5578          | 0.745    |
| 0.3075        | 22.0  | 4950  | 0.5688          | 0.7867   |
| 0.3806        | 23.0  | 5175  | 0.5983          | 0.7633   |
| 0.4403        | 24.0  | 5400  | 0.4856          | 0.8017   |
| 0.3263        | 25.0  | 5625  | 0.4951          | 0.8083   |
| 0.4298        | 26.0  | 5850  | 0.5186          | 0.8067   |
| 0.3696        | 27.0  | 6075  | 0.5017          | 0.8017   |
| 0.3505        | 28.0  | 6300  | 0.5055          | 0.805    |
| 0.2809        | 29.0  | 6525  | 0.5401          | 0.81     |
| 0.2639        | 30.0  | 6750  | 0.5378          | 0.8083   |
| 0.1827        | 31.0  | 6975  | 0.5714          | 0.815    |
| 0.2309        | 32.0  | 7200  | 0.5483          | 0.8167   |
| 0.2167        | 33.0  | 7425  | 0.5706          | 0.7967   |
| 0.1201        | 34.0  | 7650  | 0.6703          | 0.8117   |
| 0.1274        | 35.0  | 7875  | 0.7662          | 0.7917   |
| 0.1115        | 36.0  | 8100  | 0.6767          | 0.8183   |
| 0.1604        | 37.0  | 8325  | 0.8509          | 0.8083   |
| 0.0668        | 38.0  | 8550  | 0.7497          | 0.8233   |
| 0.1178        | 39.0  | 8775  | 0.8497          | 0.8067   |
| 0.0788        | 40.0  | 9000  | 0.9494          | 0.8033   |
| 0.0775        | 41.0  | 9225  | 0.9252          | 0.81     |
| 0.1033        | 42.0  | 9450  | 0.9696          | 0.8217   |
| 0.0903        | 43.0  | 9675  | 0.9856          | 0.8133   |
| 0.037         | 44.0  | 9900  | 1.0200          | 0.81     |
| 0.019         | 45.0  | 10125 | 1.1824          | 0.8067   |
| 0.0484        | 46.0  | 10350 | 1.0838          | 0.8183   |
| 0.0259        | 47.0  | 10575 | 1.1218          | 0.8083   |
| 0.0077        | 48.0  | 10800 | 1.1617          | 0.8133   |
| 0.0106        | 49.0  | 11025 | 1.1590          | 0.8117   |
| 0.0158        | 50.0  | 11250 | 1.1444          | 0.8217   |


### 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