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# smids_3x_beit_base_rms_001_fold2

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: 0.8871
- Accuracy: 0.7737

## 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 |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|
| 1.1063        | 1.0   | 225   | 1.1925          | 0.3627   |
| 0.8848        | 2.0   | 450   | 0.8623          | 0.5557   |
| 0.9929        | 3.0   | 675   | 0.7924          | 0.5774   |
| 0.7922        | 4.0   | 900   | 0.7743          | 0.5973   |
| 0.7804        | 5.0   | 1125  | 0.7554          | 0.5940   |
| 0.7536        | 6.0   | 1350  | 0.7911          | 0.5740   |
| 0.7389        | 7.0   | 1575  | 0.8973          | 0.5524   |
| 0.8004        | 8.0   | 1800  | 0.7349          | 0.6140   |
| 0.7283        | 9.0   | 2025  | 0.7228          | 0.6356   |
| 0.7381        | 10.0  | 2250  | 0.7154          | 0.6389   |
| 0.8566        | 11.0  | 2475  | 0.7154          | 0.6373   |
| 0.725         | 12.0  | 2700  | 0.6853          | 0.6539   |
| 0.7139        | 13.0  | 2925  | 0.6833          | 0.6722   |
| 0.708         | 14.0  | 3150  | 0.7156          | 0.6489   |
| 0.6892        | 15.0  | 3375  | 0.6841          | 0.6955   |
| 0.7392        | 16.0  | 3600  | 0.6648          | 0.6905   |
| 0.7123        | 17.0  | 3825  | 0.6864          | 0.6689   |
| 0.6752        | 18.0  | 4050  | 0.6534          | 0.7088   |
| 0.7193        | 19.0  | 4275  | 0.7054          | 0.6755   |
| 0.6734        | 20.0  | 4500  | 0.6500          | 0.6855   |
| 0.649         | 21.0  | 4725  | 0.6222          | 0.6872   |
| 0.7173        | 22.0  | 4950  | 0.6280          | 0.7321   |
| 0.6723        | 23.0  | 5175  | 0.6016          | 0.7587   |
| 0.6406        | 24.0  | 5400  | 0.6206          | 0.7221   |
| 0.6216        | 25.0  | 5625  | 0.6173          | 0.7338   |
| 0.6154        | 26.0  | 5850  | 0.5917          | 0.7488   |
| 0.6137        | 27.0  | 6075  | 0.6327          | 0.7304   |
| 0.597         | 28.0  | 6300  | 0.6319          | 0.7155   |
| 0.6292        | 29.0  | 6525  | 0.6003          | 0.7321   |
| 0.615         | 30.0  | 6750  | 0.5967          | 0.7554   |
| 0.5842        | 31.0  | 6975  | 0.5866          | 0.7587   |
| 0.5976        | 32.0  | 7200  | 0.5968          | 0.7388   |
| 0.5096        | 33.0  | 7425  | 0.5717          | 0.7671   |
| 0.4883        | 34.0  | 7650  | 0.5888          | 0.7804   |
| 0.5258        | 35.0  | 7875  | 0.6027          | 0.7820   |
| 0.49          | 36.0  | 8100  | 0.6052          | 0.7820   |
| 0.5271        | 37.0  | 8325  | 0.5944          | 0.7654   |
| 0.4464        | 38.0  | 8550  | 0.6867          | 0.7504   |
| 0.3796        | 39.0  | 8775  | 0.6032          | 0.7820   |
| 0.4175        | 40.0  | 9000  | 0.6446          | 0.7704   |
| 0.3633        | 41.0  | 9225  | 0.6564          | 0.7804   |
| 0.4496        | 42.0  | 9450  | 0.6467          | 0.7770   |
| 0.2811        | 43.0  | 9675  | 0.6703          | 0.7754   |
| 0.3066        | 44.0  | 9900  | 0.7311          | 0.7754   |
| 0.3558        | 45.0  | 10125 | 0.7685          | 0.7787   |
| 0.2645        | 46.0  | 10350 | 0.7874          | 0.7754   |
| 0.2214        | 47.0  | 10575 | 0.8226          | 0.7737   |
| 0.2321        | 48.0  | 10800 | 0.8600          | 0.7704   |
| 0.314         | 49.0  | 11025 | 0.8728          | 0.7770   |
| 0.1915        | 50.0  | 11250 | 0.8871          | 0.7737   |


### 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